[2025-04-02 18:51:54 simmim_finetune] (main_finetune.py 375): INFO Full config saved to checkpoint/face/config.json
[2025-04-02 18:51:54 simmim_finetune] (main_finetune.py 378): INFO AMP_OPT_LEVEL: O0
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 1.0
  CUTMIX_MINMAX: null
  MIXUP: 0.8
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: ''
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  MASK_PATCH_SIZE: 32
  MASK_RATIO: 0.6
  NUM_WORKERS: 8
  PIN_MEMORY: true
  TRAIN_PATH: VBench-2.0_human_anomaly/dataset/face_train.jsonl
  VAL_PATH: VBench-2.0_human_anomaly/dataset/face_test.jsonl
EVAL_MODE: false
LOCAL_RANK: 0
LOSS:
  FOCAL: false
  FOCAL_ALPHA: 0.25
  FOCAL_GAMMA: 2.0
MODEL:
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NAME: simmim_finetune
  NUM_CLASSES: 2
  RESUME: ''
  SWIN:
    APE: false
    DEPTHS:
    - 2
    - 2
    - 6
    - 2
    EMBED_DIM: 96
    IN_CHANS: 3
    MLP_RATIO: 4.0
    NUM_HEADS:
    - 3
    - 6
    - 12
    - 24
    PATCH_NORM: true
    PATCH_SIZE: 4
    QKV_BIAS: true
    QK_SCALE: null
    WINDOW_SIZE: 7
  TYPE: vit
  VIT:
    DEPTH: 12
    EMBED_DIM: 768
    INIT_VALUES: 0.1
    IN_CHANS: 3
    MLP_RATIO: 4
    NUM_HEADS: 12
    PATCH_SIZE: 16
    QKV_BIAS: true
    USE_APE: false
    USE_MEAN_POOLING: true
    USE_RPB: true
    USE_SHARED_RPB: false
OUTPUT: checkpoint/face
PRETRAINED: pretrain/simmim_pretrain__vit_base__img224__800ep.pth
PRINT_FREQ: 2
SAVE_FREQ: 5
SEED: 0
TAG: simmim_finetune__vit_base__img224__800ep
TEST:
  CROP: true
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: true
  BASE_LR: 0.00125
  CLIP_GRAD: 5.0
  EPOCHS: 30
  LAYER_DECAY: 0.65
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    GAMMA: 0.1
    MULTISTEPS: []
    NAME: cosine
  MIN_LR: 2.5e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: adamw
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 3
  WARMUP_LR: 2.5e-07
  WEIGHT_DECAY: 0.05

[2025-04-02 18:51:54 simmim_finetune] (data_finetune.py 88): INFO Fine-tune data transform, is_train=True:
Compose(
    RandomResizedCropAndInterpolation(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BICUBIC)
    RandomHorizontalFlip(p=0.5)
    <timm.data.auto_augment.RandAugment object at 0x7fd7f9ee9660>
    ToTensor()
    Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))
    <timm.data.random_erasing.RandomErasing object at 0x7fd7f9ee9570>
)
[2025-04-02 18:51:54 simmim_finetune] (data_finetune.py 88): INFO Fine-tune data transform, is_train=False:
Compose(
    Resize(size=256, interpolation=bicubic, max_size=None, antialias=True)
    CenterCrop(size=(224, 224))
    ToTensor()
    Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
)
[2025-04-02 18:51:54 simmim_finetune] (data_finetune.py 26): INFO Build dataset: train images = 30020, val images = 181
[2025-04-02 18:51:54 simmim_finetune] (main_finetune.py 102): INFO Creating model:vit/simmim_finetune
[2025-04-02 18:51:55 simmim_finetune] (main_finetune.py 105): INFO VisionTransformer(
  (patch_embed): PatchEmbed(
    (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
  )
  (pos_drop): Dropout(p=0.0, inplace=False)
  (blocks): ModuleList(
    (0): Block(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=False)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): Identity()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (1-11): 11 x Block(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=False)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): Identity()
  (fc_norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
  (head): Linear(in_features=768, out_features=2, bias=True)
)
[2025-04-02 18:51:55 simmim_finetune] (optimizer.py 70): INFO >>>>>>>>>> Build Optimizer for Fine-tuning Stage
[2025-04-02 18:51:55 simmim_finetune] (optimizer.py 87): INFO No weight decay: {'cls_token', 'pos_embed'}
[2025-04-02 18:51:55 simmim_finetune] (optimizer.py 182): INFO Param groups = {
  "layer_0_no_decay": {
    "group_name": "layer_0_no_decay",
    "weight_decay": 0.0,
    "params": [
      "cls_token",
      "patch_embed.proj.bias"
    ],
    "lr": 4.621507363773394e-06,
    "lr_scale": 0.003697205891018715
  },
  "layer_0_decay": {
    "group_name": "layer_0_decay",
    "weight_decay": 0.05,
    "params": [
      "patch_embed.proj.weight"
    ],
    "lr": 4.621507363773394e-06,
    "lr_scale": 0.003697205891018715
  },
  "layer_1_no_decay": {
    "group_name": "layer_1_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.0.gamma_1",
      "blocks.0.gamma_2",
      "blocks.0.norm1.weight",
      "blocks.0.norm1.bias",
      "blocks.0.attn.q_bias",
      "blocks.0.attn.v_bias",
      "blocks.0.attn.proj.bias",
      "blocks.0.norm2.weight",
      "blocks.0.norm2.bias",
      "blocks.0.mlp.fc1.bias",
      "blocks.0.mlp.fc2.bias"
    ],
    "lr": 7.110011328882144e-06,
    "lr_scale": 0.005688009063105715
  },
  "layer_1_decay": {
    "group_name": "layer_1_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.0.attn.relative_position_bias_table",
      "blocks.0.attn.qkv.weight",
      "blocks.0.attn.proj.weight",
      "blocks.0.mlp.fc1.weight",
      "blocks.0.mlp.fc2.weight"
    ],
    "lr": 7.110011328882144e-06,
    "lr_scale": 0.005688009063105715
  },
  "layer_2_no_decay": {
    "group_name": "layer_2_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.1.gamma_1",
      "blocks.1.gamma_2",
      "blocks.1.norm1.weight",
      "blocks.1.norm1.bias",
      "blocks.1.attn.q_bias",
      "blocks.1.attn.v_bias",
      "blocks.1.attn.proj.bias",
      "blocks.1.norm2.weight",
      "blocks.1.norm2.bias",
      "blocks.1.mlp.fc1.bias",
      "blocks.1.mlp.fc2.bias"
    ],
    "lr": 1.093847896751099e-05,
    "lr_scale": 0.008750783174008792
  },
  "layer_2_decay": {
    "group_name": "layer_2_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.1.attn.relative_position_bias_table",
      "blocks.1.attn.qkv.weight",
      "blocks.1.attn.proj.weight",
      "blocks.1.mlp.fc1.weight",
      "blocks.1.mlp.fc2.weight"
    ],
    "lr": 1.093847896751099e-05,
    "lr_scale": 0.008750783174008792
  },
  "layer_3_no_decay": {
    "group_name": "layer_3_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.2.gamma_1",
      "blocks.2.gamma_2",
      "blocks.2.norm1.weight",
      "blocks.2.norm1.bias",
      "blocks.2.attn.q_bias",
      "blocks.2.attn.v_bias",
      "blocks.2.attn.proj.bias",
      "blocks.2.norm2.weight",
      "blocks.2.norm2.bias",
      "blocks.2.mlp.fc1.bias",
      "blocks.2.mlp.fc2.bias"
    ],
    "lr": 1.682842918078614e-05,
    "lr_scale": 0.013462743344628911
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  "layer_3_decay": {
    "group_name": "layer_3_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.2.attn.relative_position_bias_table",
      "blocks.2.attn.qkv.weight",
      "blocks.2.attn.proj.weight",
      "blocks.2.mlp.fc1.weight",
      "blocks.2.mlp.fc2.weight"
    ],
    "lr": 1.682842918078614e-05,
    "lr_scale": 0.013462743344628911
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  "layer_4_no_decay": {
    "group_name": "layer_4_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.3.gamma_1",
      "blocks.3.gamma_2",
      "blocks.3.norm1.weight",
      "blocks.3.norm1.bias",
      "blocks.3.attn.q_bias",
      "blocks.3.attn.v_bias",
      "blocks.3.attn.proj.bias",
      "blocks.3.norm2.weight",
      "blocks.3.norm2.bias",
      "blocks.3.mlp.fc1.bias",
      "blocks.3.mlp.fc2.bias"
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    "lr": 2.588989104736329e-05,
    "lr_scale": 0.02071191283789063
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  "layer_4_decay": {
    "group_name": "layer_4_decay",
    "weight_decay": 0.05,
    "params": [
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      "blocks.3.attn.qkv.weight",
      "blocks.3.attn.proj.weight",
      "blocks.3.mlp.fc1.weight",
      "blocks.3.mlp.fc2.weight"
    ],
    "lr": 2.588989104736329e-05,
    "lr_scale": 0.02071191283789063
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  "layer_5_no_decay": {
    "group_name": "layer_5_no_decay",
    "weight_decay": 0.0,
    "params": [
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      "blocks.4.gamma_2",
      "blocks.4.norm1.weight",
      "blocks.4.norm1.bias",
      "blocks.4.attn.q_bias",
      "blocks.4.attn.v_bias",
      "blocks.4.attn.proj.bias",
      "blocks.4.norm2.weight",
      "blocks.4.norm2.bias",
      "blocks.4.mlp.fc1.bias",
      "blocks.4.mlp.fc2.bias"
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    "lr": 3.983060161132814e-05,
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    "params": [
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    "params": [
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      "blocks.5.norm2.bias",
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    "lr": 6.127784863281252e-05,
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  "layer_6_decay": {
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    "params": [
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    "lr": 6.127784863281252e-05,
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  "layer_7_no_decay": {
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    "weight_decay": 0.0,
    "params": [
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      "blocks.6.norm1.weight",
      "blocks.6.norm1.bias",
      "blocks.6.attn.q_bias",
      "blocks.6.attn.v_bias",
      "blocks.6.attn.proj.bias",
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      "blocks.6.norm2.bias",
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    "lr": 9.427361328125001e-05,
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  "layer_7_decay": {
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    "params": [
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    "lr": 9.427361328125001e-05,
    "lr_scale": 0.07541889062500001
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  "layer_8_no_decay": {
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    "params": [
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  "layer_9_decay": {
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  "layer_10_no_decay": {
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    "params": [
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    "lr": 0.00034328125,
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  "layer_10_decay": {
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    "params": [
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  "layer_11_no_decay": {
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    "params": [
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    "lr": 0.0005281250000000001,
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  "layer_11_decay": {
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    "weight_decay": 0.05,
    "params": [
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    "lr": 0.0005281250000000001,
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  "layer_12_no_decay": {
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    "params": [
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    "lr": 0.0008125000000000001,
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  "layer_12_decay": {
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    "weight_decay": 0.05,
    "params": [
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      "blocks.11.mlp.fc2.weight"
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    "lr": 0.0008125000000000001,
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  },
  "layer_13_no_decay": {
    "group_name": "layer_13_no_decay",
    "weight_decay": 0.0,
    "params": [
      "fc_norm.weight",
      "fc_norm.bias",
      "head.bias"
    ],
    "lr": 0.00125,
    "lr_scale": 1.0
  },
  "layer_13_decay": {
    "group_name": "layer_13_decay",
    "weight_decay": 0.05,
    "params": [
      "head.weight"
    ],
    "lr": 0.00125,
    "lr_scale": 1.0
  }
}
[2025-04-02 18:51:55 simmim_finetune] (optimizer.py 105): INFO AdamW (
Parameter Group 0
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_0_no_decay
    lr: 4.621507363773394e-06
    lr_scale: 0.003697205891018715
    maximize: False
    weight_decay: 0.0

Parameter Group 1
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_0_decay
    lr: 4.621507363773394e-06
    lr_scale: 0.003697205891018715
    maximize: False
    weight_decay: 0.05

Parameter Group 2
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_1_no_decay
    lr: 7.110011328882144e-06
    lr_scale: 0.005688009063105715
    maximize: False
    weight_decay: 0.0

Parameter Group 3
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_1_decay
    lr: 7.110011328882144e-06
    lr_scale: 0.005688009063105715
    maximize: False
    weight_decay: 0.05

Parameter Group 4
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_2_no_decay
    lr: 1.093847896751099e-05
    lr_scale: 0.008750783174008792
    maximize: False
    weight_decay: 0.0

Parameter Group 5
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_2_decay
    lr: 1.093847896751099e-05
    lr_scale: 0.008750783174008792
    maximize: False
    weight_decay: 0.05

Parameter Group 6
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_3_no_decay
    lr: 1.682842918078614e-05
    lr_scale: 0.013462743344628911
    maximize: False
    weight_decay: 0.0

Parameter Group 7
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_3_decay
    lr: 1.682842918078614e-05
    lr_scale: 0.013462743344628911
    maximize: False
    weight_decay: 0.05

Parameter Group 8
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_4_no_decay
    lr: 2.588989104736329e-05
    lr_scale: 0.02071191283789063
    maximize: False
    weight_decay: 0.0

Parameter Group 9
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_4_decay
    lr: 2.588989104736329e-05
    lr_scale: 0.02071191283789063
    maximize: False
    weight_decay: 0.05

Parameter Group 10
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_5_no_decay
    lr: 3.983060161132814e-05
    lr_scale: 0.03186448128906251
    maximize: False
    weight_decay: 0.0

Parameter Group 11
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_5_decay
    lr: 3.983060161132814e-05
    lr_scale: 0.03186448128906251
    maximize: False
    weight_decay: 0.05

Parameter Group 12
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_6_no_decay
    lr: 6.127784863281252e-05
    lr_scale: 0.049022278906250015
    maximize: False
    weight_decay: 0.0

Parameter Group 13
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_6_decay
    lr: 6.127784863281252e-05
    lr_scale: 0.049022278906250015
    maximize: False
    weight_decay: 0.05

Parameter Group 14
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_7_no_decay
    lr: 9.427361328125001e-05
    lr_scale: 0.07541889062500001
    maximize: False
    weight_decay: 0.0

Parameter Group 15
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_7_decay
    lr: 9.427361328125001e-05
    lr_scale: 0.07541889062500001
    maximize: False
    weight_decay: 0.05

Parameter Group 16
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_8_no_decay
    lr: 0.00014503632812500002
    lr_scale: 0.11602906250000002
    maximize: False
    weight_decay: 0.0

Parameter Group 17
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_8_decay
    lr: 0.00014503632812500002
    lr_scale: 0.11602906250000002
    maximize: False
    weight_decay: 0.05

Parameter Group 18
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_9_no_decay
    lr: 0.00022313281250000005
    lr_scale: 0.17850625000000003
    maximize: False
    weight_decay: 0.0

Parameter Group 19
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_9_decay
    lr: 0.00022313281250000005
    lr_scale: 0.17850625000000003
    maximize: False
    weight_decay: 0.05

Parameter Group 20
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_10_no_decay
    lr: 0.00034328125
    lr_scale: 0.274625
    maximize: False
    weight_decay: 0.0

Parameter Group 21
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_10_decay
    lr: 0.00034328125
    lr_scale: 0.274625
    maximize: False
    weight_decay: 0.05

Parameter Group 22
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_11_no_decay
    lr: 0.0005281250000000001
    lr_scale: 0.42250000000000004
    maximize: False
    weight_decay: 0.0

Parameter Group 23
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_11_decay
    lr: 0.0005281250000000001
    lr_scale: 0.42250000000000004
    maximize: False
    weight_decay: 0.05

Parameter Group 24
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_12_no_decay
    lr: 0.0008125000000000001
    lr_scale: 0.65
    maximize: False
    weight_decay: 0.0

Parameter Group 25
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_12_decay
    lr: 0.0008125000000000001
    lr_scale: 0.65
    maximize: False
    weight_decay: 0.05

Parameter Group 26
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_13_no_decay
    lr: 0.00125
    lr_scale: 1.0
    maximize: False
    weight_decay: 0.0

Parameter Group 27
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_13_decay
    lr: 0.00125
    lr_scale: 1.0
    maximize: False
    weight_decay: 0.05
)
[2025-04-02 18:51:55 simmim_finetune] (main_finetune.py 116): INFO number of params: 85763522
[2025-04-02 18:51:55 simmim_finetune] (utils.py 81): INFO All checkpoints founded in checkpoint/face: []
[2025-04-02 18:51:55 simmim_finetune] (main_finetune.py 146): INFO no checkpoint found in checkpoint/face, ignoring auto resume
[2025-04-02 18:51:55 simmim_finetune] (utils.py 99): INFO >>>>>>>>>> Fine-tuned from pretrain/simmim_pretrain__vit_base__img224__800ep.pth ..........
[2025-04-02 18:51:55 simmim_finetune] (utils.py 105): INFO Detect pre-trained model, remove [encoder.] prefix.
[2025-04-02 18:51:55 simmim_finetune] (utils.py 113): INFO >>>>>>>>>> Remapping pre-trained keys for VIT ..........
[2025-04-02 18:51:55 simmim_finetune] (utils.py 210): INFO Expand the shared relative position embedding to each transformer block.
[2025-04-02 18:51:55 simmim_finetune] (utils.py 119): INFO _IncompatibleKeys(missing_keys=['blocks.0.attn.relative_position_index', 'blocks.1.attn.relative_position_index', 'blocks.2.attn.relative_position_index', 'blocks.3.attn.relative_position_index', 'blocks.4.attn.relative_position_index', 'blocks.5.attn.relative_position_index', 'blocks.6.attn.relative_position_index', 'blocks.7.attn.relative_position_index', 'blocks.8.attn.relative_position_index', 'blocks.9.attn.relative_position_index', 'blocks.10.attn.relative_position_index', 'blocks.11.attn.relative_position_index', 'fc_norm.weight', 'fc_norm.bias', 'head.weight', 'head.bias'], unexpected_keys=['mask_token', 'norm.weight', 'norm.bias'])
[2025-04-02 18:51:55 simmim_finetune] (utils.py 123): INFO >>>>>>>>>> loaded successfully 'pretrain/simmim_pretrain__vit_base__img224__800ep.pth'
[2025-04-02 18:51:55 simmim_finetune] (main_finetune.py 161): INFO Start training
[2025-04-02 18:51:55 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07, 2.5e-07]
[2025-04-02 18:52:01 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][0/234]	eta 0:20:39 lr 0.000000	time 5.2972 (5.2972)	loss 0.6931 (0.6931)	grad_norm 2.3795 (2.3795)	mem 19956MB
[2025-04-02 18:52:02 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][2/234]	eta 0:09:05 lr 0.000004	time 0.8770 (2.3526)	loss 0.6931 (0.6931)	grad_norm 4.2057 (4.2945)	mem 20675MB
[2025-04-02 18:52:04 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][4/234]	eta 0:06:45 lr 0.000007	time 0.8770 (1.7626)	loss 0.6927 (0.6930)	grad_norm 4.4481 (4.2904)	mem 20675MB
[2025-04-02 18:52:06 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][6/234]	eta 0:05:44 lr 0.000011	time 0.8768 (1.5098)	loss 0.6915 (0.6927)	grad_norm 5.0726 (4.3683)	mem 20675MB
[2025-04-02 18:52:08 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][8/234]	eta 0:05:09 lr 0.000014	time 0.8770 (1.3694)	loss 0.6903 (0.6924)	grad_norm 4.8166 (4.1187)	mem 20675MB
[2025-04-02 18:52:09 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][10/234]	eta 0:04:46 lr 0.000018	time 0.8766 (1.2799)	loss 0.6912 (0.6919)	grad_norm 1.6859 (4.0860)	mem 20675MB
[2025-04-02 18:52:11 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][12/234]	eta 0:04:30 lr 0.000022	time 0.8772 (1.2181)	loss 0.6850 (0.6909)	grad_norm 4.3175 (4.1408)	mem 20675MB
[2025-04-02 18:52:13 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][14/234]	eta 0:04:18 lr 0.000025	time 0.8773 (1.1728)	loss 0.6781 (0.6897)	grad_norm 5.3720 (4.1980)	mem 20675MB
[2025-04-02 18:52:15 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][16/234]	eta 0:04:08 lr 0.000029	time 0.8772 (1.1381)	loss 0.6726 (0.6879)	grad_norm 4.8147 (4.2871)	mem 20675MB
[2025-04-02 18:52:16 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][18/234]	eta 0:03:59 lr 0.000032	time 0.8773 (1.1108)	loss 0.6737 (0.6868)	grad_norm 2.8585 (4.1046)	mem 20675MB
[2025-04-02 18:52:18 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][20/234]	eta 0:03:52 lr 0.000036	time 0.8772 (1.0886)	loss 0.6584 (0.6845)	grad_norm 3.6543 (4.0582)	mem 20675MB
[2025-04-02 18:52:20 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][22/234]	eta 0:03:46 lr 0.000039	time 0.8775 (1.0703)	loss 0.6591 (0.6831)	grad_norm 2.3579 (3.8493)	mem 20675MB
[2025-04-02 18:52:22 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][24/234]	eta 0:03:41 lr 0.000043	time 0.8772 (1.0549)	loss 0.6508 (0.6811)	grad_norm 1.7484 (3.6505)	mem 20675MB
[2025-04-02 18:52:23 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][26/234]	eta 0:03:36 lr 0.000047	time 0.8777 (1.0418)	loss 0.6837 (0.6810)	grad_norm 1.4942 (3.4788)	mem 20675MB
[2025-04-02 18:52:25 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][28/234]	eta 0:03:32 lr 0.000050	time 0.8774 (1.0305)	loss 0.6710 (0.6811)	grad_norm 1.1467 (3.3627)	mem 20675MB
[2025-04-02 18:52:27 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][30/234]	eta 0:03:28 lr 0.000054	time 0.8774 (1.0207)	loss 0.6347 (0.6796)	grad_norm 1.0514 (3.2518)	mem 20675MB
[2025-04-02 18:52:29 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][32/234]	eta 0:03:24 lr 0.000057	time 0.8774 (1.0121)	loss 0.6724 (0.6783)	grad_norm 1.4223 (3.1176)	mem 20675MB
[2025-04-02 18:52:30 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][34/234]	eta 0:03:20 lr 0.000061	time 0.8771 (1.0044)	loss 0.6320 (0.6768)	grad_norm 0.9475 (3.0139)	mem 20675MB
[2025-04-02 18:52:32 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][36/234]	eta 0:03:17 lr 0.000064	time 0.8769 (0.9976)	loss 0.6497 (0.6764)	grad_norm 0.7151 (2.9403)	mem 20675MB
[2025-04-02 18:52:34 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][38/234]	eta 0:03:14 lr 0.000068	time 0.8770 (0.9914)	loss 0.6472 (0.6753)	grad_norm 0.8360 (2.8413)	mem 20675MB
[2025-04-02 18:52:36 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][40/234]	eta 0:03:11 lr 0.000071	time 0.8768 (0.9859)	loss 0.6327 (0.6745)	grad_norm 3.0064 (2.8228)	mem 20675MB
[2025-04-02 18:52:37 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][42/234]	eta 0:03:08 lr 0.000075	time 0.8770 (0.9809)	loss 0.6711 (0.6732)	grad_norm 1.0155 (2.7938)	mem 20675MB
[2025-04-02 18:52:39 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][44/234]	eta 0:03:05 lr 0.000079	time 0.8773 (0.9763)	loss 0.6561 (0.6727)	grad_norm 0.6298 (2.7547)	mem 20675MB
[2025-04-02 18:52:41 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][46/234]	eta 0:03:02 lr 0.000082	time 0.8774 (0.9721)	loss 0.6737 (0.6727)	grad_norm 0.8970 (2.6800)	mem 20675MB
[2025-04-02 18:52:43 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][48/234]	eta 0:03:00 lr 0.000086	time 0.8768 (0.9683)	loss 0.6583 (0.6718)	grad_norm 1.5515 (2.6234)	mem 20675MB
[2025-04-02 18:52:44 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][50/234]	eta 0:02:57 lr 0.000089	time 0.8773 (0.9647)	loss 0.6623 (0.6711)	grad_norm 0.6025 (2.5658)	mem 20675MB
[2025-04-02 18:52:46 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][52/234]	eta 0:02:54 lr 0.000093	time 0.8772 (0.9614)	loss 0.6166 (0.6700)	grad_norm 0.8836 (2.5159)	mem 20675MB
[2025-04-02 18:52:48 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][54/234]	eta 0:02:52 lr 0.000096	time 0.8772 (0.9584)	loss 0.6314 (0.6696)	grad_norm 1.1176 (2.4721)	mem 20675MB
[2025-04-02 18:52:50 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][56/234]	eta 0:02:50 lr 0.000100	time 0.8778 (0.9556)	loss 0.6591 (0.6693)	grad_norm 1.0999 (2.4305)	mem 20675MB
[2025-04-02 18:52:52 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][58/234]	eta 0:02:47 lr 0.000104	time 0.8772 (0.9530)	loss 0.6471 (0.6683)	grad_norm 3.1273 (2.4286)	mem 20675MB
[2025-04-02 18:52:53 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][60/234]	eta 0:02:45 lr 0.000107	time 0.8770 (0.9505)	loss 0.6286 (0.6676)	grad_norm 1.4825 (2.4019)	mem 20675MB
[2025-04-02 18:52:55 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][62/234]	eta 0:02:43 lr 0.000111	time 0.8768 (0.9482)	loss 0.6193 (0.6666)	grad_norm 1.5809 (2.4049)	mem 20675MB
[2025-04-02 18:52:57 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][64/234]	eta 0:02:40 lr 0.000114	time 0.8768 (0.9460)	loss 0.6258 (0.6654)	grad_norm 1.5817 (2.3928)	mem 20675MB
[2025-04-02 18:52:59 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][66/234]	eta 0:02:38 lr 0.000118	time 0.8770 (0.9440)	loss 0.6104 (0.6639)	grad_norm 1.8032 (2.3737)	mem 20675MB
[2025-04-02 18:53:00 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][68/234]	eta 0:02:36 lr 0.000121	time 0.8768 (0.9421)	loss 0.6372 (0.6629)	grad_norm 1.6773 (2.3662)	mem 20675MB
[2025-04-02 18:53:02 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][70/234]	eta 0:02:34 lr 0.000125	time 0.8767 (0.9403)	loss 0.5886 (0.6614)	grad_norm 2.5995 (2.3834)	mem 20675MB
[2025-04-02 18:53:04 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][72/234]	eta 0:02:32 lr 0.000128	time 0.8766 (0.9386)	loss 0.6766 (0.6613)	grad_norm 3.0946 (2.3862)	mem 20675MB
[2025-04-02 18:53:06 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][74/234]	eta 0:02:29 lr 0.000132	time 0.8767 (0.9369)	loss 0.5752 (0.6595)	grad_norm 3.8134 (2.4208)	mem 20675MB
[2025-04-02 18:53:07 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][76/234]	eta 0:02:27 lr 0.000136	time 0.8766 (0.9354)	loss 0.7085 (0.6597)	grad_norm 5.5458 (2.4477)	mem 20675MB
[2025-04-02 18:53:09 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][78/234]	eta 0:02:25 lr 0.000139	time 0.8767 (0.9339)	loss 0.6532 (0.6597)	grad_norm 3.7712 (2.4965)	mem 20675MB
[2025-04-02 18:53:11 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][80/234]	eta 0:02:23 lr 0.000143	time 0.8767 (0.9325)	loss 0.6565 (0.6596)	grad_norm 2.1552 (2.4893)	mem 20675MB
[2025-04-02 18:53:13 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][82/234]	eta 0:02:21 lr 0.000146	time 0.8768 (0.9312)	loss 0.6359 (0.6590)	grad_norm 4.5732 (2.5112)	mem 20675MB
[2025-04-02 18:53:14 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][84/234]	eta 0:02:19 lr 0.000150	time 0.8767 (0.9299)	loss 0.6571 (0.6590)	grad_norm 4.6320 (2.5605)	mem 20675MB
[2025-04-02 18:53:16 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][86/234]	eta 0:02:17 lr 0.000153	time 0.8765 (0.9287)	loss 0.6217 (0.6579)	grad_norm 2.1303 (2.5710)	mem 20675MB
[2025-04-02 18:53:18 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][88/234]	eta 0:02:15 lr 0.000157	time 0.8767 (0.9276)	loss 0.6345 (0.6572)	grad_norm 3.4009 (2.5787)	mem 20675MB
[2025-04-02 18:53:20 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][90/234]	eta 0:02:13 lr 0.000160	time 0.8769 (0.9265)	loss 0.6497 (0.6573)	grad_norm 2.5485 (2.5918)	mem 20675MB
[2025-04-02 18:53:21 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][92/234]	eta 0:02:11 lr 0.000164	time 0.8773 (0.9254)	loss 0.6330 (0.6561)	grad_norm 1.6591 (2.5868)	mem 20675MB
[2025-04-02 18:53:23 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][94/234]	eta 0:02:09 lr 0.000168	time 0.8766 (0.9244)	loss 0.6732 (0.6559)	grad_norm 2.7944 (2.5847)	mem 20675MB
[2025-04-02 18:53:25 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][96/234]	eta 0:02:07 lr 0.000171	time 0.8766 (0.9235)	loss 0.6410 (0.6555)	grad_norm 4.1972 (2.6045)	mem 20675MB
[2025-04-02 18:53:27 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][98/234]	eta 0:02:05 lr 0.000175	time 0.8767 (0.9225)	loss 0.5901 (0.6550)	grad_norm 1.3466 (2.6265)	mem 20675MB
[2025-04-02 18:53:28 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][100/234]	eta 0:02:03 lr 0.000178	time 0.8768 (0.9216)	loss 0.6376 (0.6547)	grad_norm 2.3202 (2.6210)	mem 20675MB
[2025-04-02 18:53:30 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][102/234]	eta 0:02:01 lr 0.000182	time 0.8767 (0.9208)	loss 0.6334 (0.6546)	grad_norm 2.6778 (2.6209)	mem 20675MB
[2025-04-02 18:53:32 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][104/234]	eta 0:01:59 lr 0.000185	time 0.8765 (0.9200)	loss 0.6218 (0.6539)	grad_norm 2.6055 (2.6107)	mem 20675MB
[2025-04-02 18:53:34 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][106/234]	eta 0:01:57 lr 0.000189	time 0.8768 (0.9192)	loss 0.6275 (0.6536)	grad_norm 1.9506 (2.5936)	mem 20675MB
[2025-04-02 18:53:35 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][108/234]	eta 0:01:55 lr 0.000193	time 0.8768 (0.9184)	loss 0.6501 (0.6532)	grad_norm 1.3839 (2.5823)	mem 20675MB
[2025-04-02 18:53:37 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][110/234]	eta 0:01:53 lr 0.000196	time 0.8771 (0.9177)	loss 0.6038 (0.6520)	grad_norm 3.1418 (2.5972)	mem 20675MB
[2025-04-02 18:53:39 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][112/234]	eta 0:01:51 lr 0.000200	time 0.8769 (0.9170)	loss 0.5970 (0.6513)	grad_norm 2.5950 (2.5942)	mem 20675MB
[2025-04-02 18:53:41 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][114/234]	eta 0:01:49 lr 0.000203	time 0.8768 (0.9163)	loss 0.6380 (0.6513)	grad_norm 5.5075 (2.6228)	mem 20675MB
[2025-04-02 18:53:42 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][116/234]	eta 0:01:48 lr 0.000207	time 0.8766 (0.9156)	loss 0.6202 (0.6508)	grad_norm 3.6578 (2.6417)	mem 20675MB
[2025-04-02 18:53:44 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][118/234]	eta 0:01:46 lr 0.000210	time 0.8766 (0.9150)	loss 0.6623 (0.6505)	grad_norm 5.4730 (2.6689)	mem 20675MB
[2025-04-02 18:53:46 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][120/234]	eta 0:01:44 lr 0.000214	time 0.8768 (0.9143)	loss 0.6322 (0.6505)	grad_norm 5.9187 (2.6983)	mem 20675MB
[2025-04-02 18:53:48 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][122/234]	eta 0:01:42 lr 0.000217	time 0.8767 (0.9138)	loss 0.6175 (0.6502)	grad_norm 2.7802 (2.7074)	mem 20675MB
[2025-04-02 18:53:49 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][124/234]	eta 0:01:40 lr 0.000221	time 0.8774 (0.9132)	loss 0.5877 (0.6494)	grad_norm 3.1060 (2.7052)	mem 20675MB
[2025-04-02 18:53:51 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][126/234]	eta 0:01:38 lr 0.000225	time 0.8767 (0.9126)	loss 0.6545 (0.6489)	grad_norm 2.6611 (2.7065)	mem 20675MB
[2025-04-02 18:53:53 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][128/234]	eta 0:01:36 lr 0.000228	time 0.8768 (0.9121)	loss 0.6259 (0.6486)	grad_norm 2.2965 (2.7064)	mem 20675MB
[2025-04-02 18:53:55 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][130/234]	eta 0:01:34 lr 0.000232	time 0.8765 (0.9115)	loss 0.6023 (0.6478)	grad_norm 3.6058 (2.7166)	mem 20675MB
[2025-04-02 18:53:56 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][132/234]	eta 0:01:32 lr 0.000235	time 0.8765 (0.9110)	loss 0.6242 (0.6475)	grad_norm 2.1133 (2.7036)	mem 20675MB
[2025-04-02 18:53:58 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][134/234]	eta 0:01:31 lr 0.000239	time 0.8770 (0.9105)	loss 0.6076 (0.6469)	grad_norm 3.2554 (2.7045)	mem 20675MB
[2025-04-02 18:54:00 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][136/234]	eta 0:01:29 lr 0.000242	time 0.8778 (0.9101)	loss 0.5454 (0.6455)	grad_norm 3.3599 (2.7053)	mem 20675MB
[2025-04-02 18:54:02 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][138/234]	eta 0:01:27 lr 0.000246	time 0.8792 (0.9096)	loss 0.5773 (0.6446)	grad_norm 2.0058 (2.7056)	mem 20675MB
[2025-04-02 18:54:03 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][140/234]	eta 0:01:25 lr 0.000249	time 0.8786 (0.9092)	loss 0.5910 (0.6435)	grad_norm 4.1668 (2.7262)	mem 20675MB
[2025-04-02 18:54:05 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][142/234]	eta 0:01:23 lr 0.000253	time 0.8775 (0.9088)	loss 0.6388 (0.6434)	grad_norm 2.4667 (2.7229)	mem 20675MB
[2025-04-02 18:54:07 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][144/234]	eta 0:01:21 lr 0.000257	time 0.8780 (0.9083)	loss 0.6427 (0.6428)	grad_norm 4.1406 (2.7333)	mem 20675MB
[2025-04-02 18:54:09 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][146/234]	eta 0:01:19 lr 0.000260	time 0.8779 (0.9080)	loss 0.6091 (0.6422)	grad_norm 3.6699 (2.7507)	mem 20675MB
[2025-04-02 18:54:11 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][148/234]	eta 0:01:18 lr 0.000264	time 0.8778 (0.9076)	loss 0.5954 (0.6419)	grad_norm 4.5836 (2.7607)	mem 20675MB
[2025-04-02 18:54:12 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][150/234]	eta 0:01:16 lr 0.000267	time 0.8776 (0.9072)	loss 0.5406 (0.6413)	grad_norm 4.1143 (2.7604)	mem 20675MB
[2025-04-02 18:54:14 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][152/234]	eta 0:01:14 lr 0.000271	time 0.8771 (0.9068)	loss 0.5697 (0.6409)	grad_norm 3.2336 (2.7593)	mem 20675MB
[2025-04-02 18:54:16 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][154/234]	eta 0:01:12 lr 0.000274	time 0.8774 (0.9064)	loss 0.6307 (0.6409)	grad_norm 3.2293 (2.7656)	mem 20675MB
[2025-04-02 18:54:18 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][156/234]	eta 0:01:10 lr 0.000278	time 0.8775 (0.9061)	loss 0.6166 (0.6404)	grad_norm 2.4471 (2.7576)	mem 20675MB
[2025-04-02 18:54:19 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][158/234]	eta 0:01:08 lr 0.000282	time 0.8776 (0.9057)	loss 0.6181 (0.6399)	grad_norm 3.6397 (2.7599)	mem 20675MB
[2025-04-02 18:54:21 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][160/234]	eta 0:01:06 lr 0.000285	time 0.8765 (0.9054)	loss 0.6058 (0.6394)	grad_norm 1.8393 (2.7539)	mem 20675MB
[2025-04-02 18:54:23 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][162/234]	eta 0:01:05 lr 0.000289	time 0.8772 (0.9050)	loss 0.5564 (0.6386)	grad_norm 4.2667 (2.7561)	mem 20675MB
[2025-04-02 18:54:25 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][164/234]	eta 0:01:03 lr 0.000292	time 0.8768 (0.9047)	loss 0.5675 (0.6382)	grad_norm 2.0132 (2.7656)	mem 20675MB
[2025-04-02 18:54:26 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][166/234]	eta 0:01:01 lr 0.000296	time 0.8769 (0.9044)	loss 0.5473 (0.6371)	grad_norm 2.9507 (2.7671)	mem 20675MB
[2025-04-02 18:54:28 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][168/234]	eta 0:00:59 lr 0.000299	time 0.8764 (0.9041)	loss 0.6021 (0.6370)	grad_norm 2.4282 (2.7677)	mem 20675MB
[2025-04-02 18:54:30 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][170/234]	eta 0:00:57 lr 0.000303	time 0.8765 (0.9037)	loss 0.5898 (0.6364)	grad_norm 2.8738 (2.7755)	mem 20675MB
[2025-04-02 18:54:32 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][172/234]	eta 0:00:56 lr 0.000306	time 0.8767 (0.9034)	loss 0.6729 (0.6364)	grad_norm 5.5680 (2.8125)	mem 20675MB
[2025-04-02 18:54:33 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][174/234]	eta 0:00:54 lr 0.000310	time 0.8771 (0.9031)	loss 0.5700 (0.6353)	grad_norm 2.9390 (2.8139)	mem 20675MB
[2025-04-02 18:54:35 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][176/234]	eta 0:00:52 lr 0.000314	time 0.8764 (0.9029)	loss 0.5991 (0.6346)	grad_norm 4.9389 (2.8313)	mem 20675MB
[2025-04-02 18:54:37 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][178/234]	eta 0:00:50 lr 0.000317	time 0.8771 (0.9026)	loss 0.6727 (0.6343)	grad_norm 9.5097 (2.8764)	mem 20675MB
[2025-04-02 18:54:39 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][180/234]	eta 0:00:48 lr 0.000321	time 0.8764 (0.9023)	loss 0.5966 (0.6342)	grad_norm 4.6542 (2.9254)	mem 20675MB
[2025-04-02 18:54:40 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][182/234]	eta 0:00:46 lr 0.000324	time 0.8773 (0.9020)	loss 0.6379 (0.6342)	grad_norm 4.1678 (2.9283)	mem 20675MB
[2025-04-02 18:54:42 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][184/234]	eta 0:00:45 lr 0.000328	time 0.8764 (0.9018)	loss 0.6597 (0.6340)	grad_norm 4.9601 (2.9369)	mem 20675MB
[2025-04-02 18:54:44 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][186/234]	eta 0:00:43 lr 0.000331	time 0.8773 (0.9015)	loss 0.5973 (0.6336)	grad_norm 2.7189 (2.9384)	mem 20675MB
[2025-04-02 18:54:46 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][188/234]	eta 0:00:41 lr 0.000335	time 0.8766 (0.9013)	loss 0.6605 (0.6336)	grad_norm 4.2872 (2.9492)	mem 20675MB
[2025-04-02 18:54:47 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][190/234]	eta 0:00:39 lr 0.000339	time 0.8764 (0.9010)	loss 0.6021 (0.6334)	grad_norm 2.8367 (2.9499)	mem 20675MB
[2025-04-02 18:54:49 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][192/234]	eta 0:00:37 lr 0.000342	time 0.8768 (0.9008)	loss 0.5993 (0.6331)	grad_norm 2.5127 (2.9488)	mem 20675MB
[2025-04-02 18:54:51 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][194/234]	eta 0:00:36 lr 0.000346	time 0.8763 (0.9005)	loss 0.5396 (0.6323)	grad_norm 3.7659 (2.9528)	mem 20675MB
[2025-04-02 18:54:53 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][196/234]	eta 0:00:34 lr 0.000349	time 0.8767 (0.9003)	loss 0.6576 (0.6322)	grad_norm 4.3564 (2.9700)	mem 20675MB
[2025-04-02 18:54:54 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][198/234]	eta 0:00:32 lr 0.000353	time 0.8763 (0.9001)	loss 0.4705 (0.6308)	grad_norm 3.4398 (2.9875)	mem 20675MB
[2025-04-02 18:54:56 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][200/234]	eta 0:00:30 lr 0.000356	time 0.8764 (0.8998)	loss 0.5309 (0.6300)	grad_norm 3.4189 (2.9917)	mem 20675MB
[2025-04-02 18:54:58 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][202/234]	eta 0:00:28 lr 0.000360	time 0.8774 (0.8996)	loss 0.5260 (0.6295)	grad_norm 7.0555 (3.0172)	mem 20675MB
[2025-04-02 18:55:00 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][204/234]	eta 0:00:26 lr 0.000363	time 0.8773 (0.8994)	loss 0.5229 (0.6290)	grad_norm 5.7066 (3.0399)	mem 20675MB
[2025-04-02 18:55:01 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][206/234]	eta 0:00:25 lr 0.000367	time 0.8770 (0.8992)	loss 0.6040 (0.6281)	grad_norm 4.8419 (3.0594)	mem 20675MB
[2025-04-02 18:55:03 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][208/234]	eta 0:00:23 lr 0.000371	time 0.8776 (0.8990)	loss 0.6510 (0.6284)	grad_norm 6.0813 (3.0973)	mem 20675MB
[2025-04-02 18:55:05 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][210/234]	eta 0:00:21 lr 0.000374	time 0.8763 (0.8988)	loss 0.5995 (0.6281)	grad_norm 2.5940 (3.0945)	mem 20675MB
[2025-04-02 18:55:07 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][212/234]	eta 0:00:19 lr 0.000378	time 0.8763 (0.8986)	loss 0.5607 (0.6277)	grad_norm 5.0387 (3.1080)	mem 20675MB
[2025-04-02 18:55:08 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][214/234]	eta 0:00:17 lr 0.000381	time 0.8774 (0.8984)	loss 0.5545 (0.6272)	grad_norm 2.7019 (3.1070)	mem 20675MB
[2025-04-02 18:55:10 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][216/234]	eta 0:00:16 lr 0.000385	time 0.8771 (0.8982)	loss 0.5923 (0.6265)	grad_norm 3.4998 (3.1132)	mem 20675MB
[2025-04-02 18:55:12 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][218/234]	eta 0:00:14 lr 0.000388	time 0.8773 (0.8980)	loss 0.6039 (0.6264)	grad_norm 3.6707 (3.1135)	mem 20675MB
[2025-04-02 18:55:14 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][220/234]	eta 0:00:12 lr 0.000392	time 0.8773 (0.8978)	loss 0.6601 (0.6262)	grad_norm 6.7862 (3.1345)	mem 20675MB
[2025-04-02 18:55:15 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][222/234]	eta 0:00:10 lr 0.000395	time 0.8771 (0.8977)	loss 0.5696 (0.6259)	grad_norm 4.4652 (3.1423)	mem 20675MB
[2025-04-02 18:55:17 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][224/234]	eta 0:00:08 lr 0.000399	time 0.8778 (0.8975)	loss 0.5428 (0.6254)	grad_norm 2.3372 (3.1333)	mem 20675MB
[2025-04-02 18:55:19 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][226/234]	eta 0:00:07 lr 0.000403	time 0.8768 (0.8973)	loss 0.5991 (0.6251)	grad_norm 3.2132 (3.1344)	mem 20675MB
[2025-04-02 18:55:21 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][228/234]	eta 0:00:05 lr 0.000406	time 0.8770 (0.8972)	loss 0.4697 (0.6244)	grad_norm 5.3311 (3.1387)	mem 20675MB
[2025-04-02 18:55:22 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][230/234]	eta 0:00:03 lr 0.000410	time 0.8786 (0.8970)	loss 0.4604 (0.6236)	grad_norm 3.5369 (3.1380)	mem 20675MB
[2025-04-02 18:55:24 simmim_finetune] (main_finetune.py 252): INFO Train: [0/30][232/234]	eta 0:00:01 lr 0.000413	time 0.8787 (0.8969)	loss 0.5194 (0.6230)	grad_norm 3.1328 (3.1460)	mem 20675MB
[2025-04-02 18:55:25 simmim_finetune] (main_finetune.py 260): INFO EPOCH 0 training takes 0:03:29
[2025-04-02 18:55:25 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt0.pth saving......
[2025-04-02 18:55:28 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt0.pth saved !!!
[2025-04-02 18:55:29 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.076 (1.076)	Loss 1.4270 (1.4270)	Acc@1 34.375 (34.375)	Mem 20675MB
[2025-04-02 18:55:30 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 53.591
[2025-04-02 18:55:30 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 53.6%
[2025-04-02 18:55:30 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 53.59%
[2025-04-02 18:55:30 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [1.7009419027908842e-06, 1.7009419027908842e-06, 2.5268983470506262e-06, 2.5268983470506262e-06, 3.797600568988691e-06, 3.797600568988691e-06, 5.752527064278021e-06, 5.752527064278021e-06, 8.760106287800066e-06, 8.760106287800066e-06, 1.3387151247064752e-05, 1.3387151247064752e-05, 2.0505681953625807e-05, 2.0505681953625807e-05, 3.145726765602742e-05, 3.145726765602742e-05, 4.830586104433761e-05, 4.830586104433761e-05, 7.422677394943022e-05, 7.422677394943022e-05, 0.00011410510149572652, 0.00011410510149572652, 0.00017545637464387467, 0.00017545637464387467, 0.0002698429487179487, 0.0002698429487179487, 0.00041505306267806264, 0.00041505306267806264]
[2025-04-02 18:55:32 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][0/234]	eta 0:07:14 lr 0.000417	time 1.8586 (1.8586)	loss 0.6565 (0.6565)	grad_norm 4.1297 (4.1297)	mem 20675MB
[2025-04-02 18:55:33 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][2/234]	eta 0:04:39 lr 0.000420	time 0.8775 (1.2058)	loss 0.5694 (0.5881)	grad_norm 4.7149 (4.4303)	mem 20675MB
[2025-04-02 18:55:35 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][4/234]	eta 0:04:07 lr 0.000424	time 0.8780 (1.0751)	loss 0.5392 (0.5898)	grad_norm 4.6079 (4.9156)	mem 20675MB
[2025-04-02 18:55:37 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][6/234]	eta 0:03:52 lr 0.000428	time 0.8771 (1.0188)	loss 0.5995 (0.5894)	grad_norm 7.6449 (5.1592)	mem 20675MB
[2025-04-02 18:55:39 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][8/234]	eta 0:03:43 lr 0.000431	time 0.8768 (0.9875)	loss 0.6145 (0.5903)	grad_norm 3.8965 (4.7938)	mem 20675MB
[2025-04-02 18:55:40 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][10/234]	eta 0:03:36 lr 0.000435	time 0.8768 (0.9675)	loss 0.6187 (0.5881)	grad_norm 3.5095 (4.5704)	mem 20675MB
[2025-04-02 18:55:42 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][12/234]	eta 0:03:31 lr 0.000438	time 0.8778 (0.9538)	loss 0.5700 (0.5829)	grad_norm 2.4601 (4.2878)	mem 20675MB
[2025-04-02 18:55:44 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][14/234]	eta 0:03:27 lr 0.000442	time 0.8775 (0.9438)	loss 0.6445 (0.5805)	grad_norm 9.7326 (4.6312)	mem 20675MB
[2025-04-02 18:55:46 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][16/234]	eta 0:03:24 lr 0.000445	time 0.8771 (0.9362)	loss 0.6368 (0.5811)	grad_norm 4.8503 (4.8944)	mem 20675MB
[2025-04-02 18:55:47 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][18/234]	eta 0:03:20 lr 0.000449	time 0.8775 (0.9301)	loss 0.5890 (0.5897)	grad_norm 3.8509 (5.0709)	mem 20675MB
[2025-04-02 18:55:49 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][20/234]	eta 0:03:18 lr 0.000452	time 0.8779 (0.9252)	loss 0.5619 (0.5849)	grad_norm 4.9167 (5.0060)	mem 20675MB
[2025-04-02 18:55:51 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][22/234]	eta 0:03:15 lr 0.000456	time 0.8775 (0.9212)	loss 0.5209 (0.5802)	grad_norm 5.7674 (4.9620)	mem 20675MB
[2025-04-02 18:55:53 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][24/234]	eta 0:03:12 lr 0.000460	time 0.8771 (0.9177)	loss 0.6245 (0.5827)	grad_norm 3.1825 (4.8028)	mem 20675MB
[2025-04-02 18:55:54 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][26/234]	eta 0:03:10 lr 0.000463	time 0.8765 (0.9147)	loss 0.5479 (0.5767)	grad_norm 3.1957 (4.7379)	mem 20675MB
[2025-04-02 18:55:56 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][28/234]	eta 0:03:07 lr 0.000467	time 0.8789 (0.9123)	loss 0.5606 (0.5763)	grad_norm 6.2917 (4.7299)	mem 20675MB
[2025-04-02 18:55:58 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][30/234]	eta 0:03:05 lr 0.000470	time 0.8777 (0.9101)	loss 0.4452 (0.5723)	grad_norm 4.1230 (4.6675)	mem 20675MB
[2025-04-02 18:56:00 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][32/234]	eta 0:03:03 lr 0.000474	time 0.8775 (0.9082)	loss 0.4612 (0.5677)	grad_norm 3.0578 (4.5599)	mem 20675MB
[2025-04-02 18:56:01 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][34/234]	eta 0:03:01 lr 0.000477	time 0.8775 (0.9065)	loss 0.5179 (0.5684)	grad_norm 7.6969 (4.6187)	mem 20675MB
[2025-04-02 18:56:03 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][36/234]	eta 0:02:59 lr 0.000481	time 0.8774 (0.9050)	loss 0.6433 (0.5710)	grad_norm 4.3562 (4.5507)	mem 20675MB
[2025-04-02 18:56:05 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][38/234]	eta 0:02:57 lr 0.000484	time 0.8775 (0.9037)	loss 0.6580 (0.5734)	grad_norm 2.3155 (4.4901)	mem 20675MB
[2025-04-02 18:56:07 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][40/234]	eta 0:02:55 lr 0.000488	time 0.8771 (0.9024)	loss 0.5523 (0.5722)	grad_norm 2.8671 (4.4012)	mem 20675MB
[2025-04-02 18:56:08 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][42/234]	eta 0:02:53 lr 0.000492	time 0.8776 (0.9013)	loss 0.5881 (0.5721)	grad_norm 3.9023 (4.3244)	mem 20675MB
[2025-04-02 18:56:10 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][44/234]	eta 0:02:51 lr 0.000495	time 0.8771 (0.9003)	loss 0.5320 (0.5691)	grad_norm 2.1844 (4.2535)	mem 20675MB
[2025-04-02 18:56:12 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][46/234]	eta 0:02:49 lr 0.000499	time 0.8778 (0.8994)	loss 0.5292 (0.5693)	grad_norm 4.8402 (4.2467)	mem 20675MB
[2025-04-02 18:56:14 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][48/234]	eta 0:02:47 lr 0.000502	time 0.8767 (0.8985)	loss 0.6273 (0.5696)	grad_norm 7.5420 (4.3402)	mem 20675MB
[2025-04-02 18:56:15 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][50/234]	eta 0:02:45 lr 0.000506	time 0.8768 (0.8977)	loss 0.5819 (0.5680)	grad_norm 3.5170 (4.3565)	mem 20675MB
[2025-04-02 18:56:17 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][52/234]	eta 0:02:43 lr 0.000509	time 0.8781 (0.8970)	loss 0.6493 (0.5684)	grad_norm 4.7831 (4.3454)	mem 20675MB
[2025-04-02 18:56:19 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][54/234]	eta 0:02:41 lr 0.000513	time 0.8783 (0.8963)	loss 0.5794 (0.5697)	grad_norm 2.2002 (4.2850)	mem 20675MB
[2025-04-02 18:56:21 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][56/234]	eta 0:02:39 lr 0.000517	time 0.8775 (0.8956)	loss 0.5048 (0.5684)	grad_norm 3.5105 (4.2507)	mem 20675MB
[2025-04-02 18:56:23 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][58/234]	eta 0:02:37 lr 0.000520	time 0.8773 (0.8950)	loss 0.5447 (0.5682)	grad_norm 2.0493 (4.1776)	mem 20675MB
[2025-04-02 18:56:24 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][60/234]	eta 0:02:35 lr 0.000524	time 0.8768 (0.8945)	loss 0.5984 (0.5673)	grad_norm 2.9012 (4.1301)	mem 20675MB
[2025-04-02 18:56:26 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][62/234]	eta 0:02:33 lr 0.000527	time 0.8774 (0.8939)	loss 0.4545 (0.5643)	grad_norm 4.5816 (4.1549)	mem 20675MB
[2025-04-02 18:56:28 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][64/234]	eta 0:02:31 lr 0.000531	time 0.8762 (0.8934)	loss 0.6647 (0.5647)	grad_norm 5.6873 (4.1791)	mem 20675MB
[2025-04-02 18:56:30 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][66/234]	eta 0:02:30 lr 0.000534	time 0.8772 (0.8930)	loss 0.6131 (0.5647)	grad_norm 7.1432 (4.2296)	mem 20675MB
[2025-04-02 18:56:31 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][68/234]	eta 0:02:28 lr 0.000538	time 0.8783 (0.8926)	loss 0.6253 (0.5662)	grad_norm 4.8494 (4.2928)	mem 20675MB
[2025-04-02 18:56:33 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][70/234]	eta 0:02:26 lr 0.000541	time 0.8772 (0.8922)	loss 0.5248 (0.5663)	grad_norm 3.1877 (4.2789)	mem 20675MB
[2025-04-02 18:56:35 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][72/234]	eta 0:02:24 lr 0.000545	time 0.8768 (0.8918)	loss 0.6180 (0.5657)	grad_norm 1.7404 (4.2361)	mem 20675MB
[2025-04-02 18:56:37 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][74/234]	eta 0:02:22 lr 0.000549	time 0.8763 (0.8914)	loss 0.6146 (0.5658)	grad_norm 2.0499 (4.1924)	mem 20675MB
[2025-04-02 18:56:38 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][76/234]	eta 0:02:20 lr 0.000552	time 0.8773 (0.8910)	loss 0.6209 (0.5662)	grad_norm 2.7510 (4.1648)	mem 20675MB
[2025-04-02 18:56:40 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][78/234]	eta 0:02:18 lr 0.000556	time 0.8779 (0.8907)	loss 0.6997 (0.5687)	grad_norm 8.9092 (4.2187)	mem 20675MB
[2025-04-02 18:56:42 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][80/234]	eta 0:02:17 lr 0.000559	time 0.8780 (0.8904)	loss 0.5907 (0.5692)	grad_norm 1.8999 (4.2018)	mem 20675MB
[2025-04-02 18:56:44 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][82/234]	eta 0:02:15 lr 0.000563	time 0.8782 (0.8901)	loss 0.6205 (0.5704)	grad_norm 4.3918 (4.2061)	mem 20675MB
[2025-04-02 18:56:45 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][84/234]	eta 0:02:13 lr 0.000566	time 0.8780 (0.8898)	loss 0.6080 (0.5716)	grad_norm 1.7270 (4.1967)	mem 20675MB
[2025-04-02 18:56:47 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][86/234]	eta 0:02:11 lr 0.000570	time 0.8780 (0.8896)	loss 0.6266 (0.5727)	grad_norm 2.3812 (4.1507)	mem 20675MB
[2025-04-02 18:56:49 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][88/234]	eta 0:02:09 lr 0.000573	time 0.8763 (0.8893)	loss 0.6201 (0.5743)	grad_norm 5.3053 (4.1800)	mem 20675MB
[2025-04-02 18:56:51 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][90/234]	eta 0:02:08 lr 0.000577	time 0.8774 (0.8891)	loss 0.6514 (0.5752)	grad_norm 3.8017 (4.1709)	mem 20675MB
[2025-04-02 18:56:52 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][92/234]	eta 0:02:06 lr 0.000581	time 0.8782 (0.8889)	loss 0.4625 (0.5742)	grad_norm 3.2175 (4.1618)	mem 20675MB
[2025-04-02 18:56:54 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][94/234]	eta 0:02:04 lr 0.000584	time 0.8778 (0.8886)	loss 0.5473 (0.5743)	grad_norm 4.7751 (4.1735)	mem 20675MB
[2025-04-02 18:56:56 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][96/234]	eta 0:02:02 lr 0.000588	time 0.8791 (0.8885)	loss 0.4579 (0.5730)	grad_norm 5.7188 (4.1861)	mem 20675MB
[2025-04-02 18:56:58 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][98/234]	eta 0:02:00 lr 0.000591	time 0.8789 (0.8883)	loss 0.6230 (0.5736)	grad_norm 2.4272 (4.1539)	mem 20675MB
[2025-04-02 18:56:59 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][100/234]	eta 0:01:59 lr 0.000595	time 0.8788 (0.8881)	loss 0.5613 (0.5732)	grad_norm 2.5338 (4.1315)	mem 20675MB
[2025-04-02 18:57:01 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][102/234]	eta 0:01:57 lr 0.000598	time 0.8779 (0.8879)	loss 0.5056 (0.5726)	grad_norm 3.7904 (4.1295)	mem 20675MB
[2025-04-02 18:57:03 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][104/234]	eta 0:01:55 lr 0.000602	time 0.8768 (0.8877)	loss 0.5434 (0.5722)	grad_norm 2.7905 (4.1139)	mem 20675MB
[2025-04-02 18:57:05 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][106/234]	eta 0:01:53 lr 0.000606	time 0.8777 (0.8876)	loss 0.5760 (0.5721)	grad_norm 5.4777 (4.1256)	mem 20675MB
[2025-04-02 18:57:06 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][108/234]	eta 0:01:51 lr 0.000609	time 0.8767 (0.8874)	loss 0.5503 (0.5720)	grad_norm 2.2592 (4.1104)	mem 20675MB
[2025-04-02 18:57:08 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][110/234]	eta 0:01:50 lr 0.000613	time 0.8778 (0.8872)	loss 0.5646 (0.5722)	grad_norm 3.8756 (4.1062)	mem 20675MB
[2025-04-02 18:57:10 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][112/234]	eta 0:01:48 lr 0.000616	time 0.8779 (0.8871)	loss 0.4981 (0.5716)	grad_norm 3.6355 (4.0910)	mem 20675MB
[2025-04-02 18:57:12 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][114/234]	eta 0:01:46 lr 0.000620	time 0.8786 (0.8869)	loss 0.5819 (0.5715)	grad_norm 2.6398 (4.0765)	mem 20675MB
[2025-04-02 18:57:13 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][116/234]	eta 0:01:44 lr 0.000623	time 0.8777 (0.8868)	loss 0.6247 (0.5714)	grad_norm 3.5574 (4.0594)	mem 20675MB
[2025-04-02 18:57:15 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][118/234]	eta 0:01:42 lr 0.000627	time 0.8769 (0.8866)	loss 0.5226 (0.5709)	grad_norm 3.9110 (4.0476)	mem 20675MB
[2025-04-02 18:57:17 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][120/234]	eta 0:01:41 lr 0.000630	time 0.8772 (0.8865)	loss 0.5355 (0.5704)	grad_norm 3.8478 (4.0304)	mem 20675MB
[2025-04-02 18:57:19 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][122/234]	eta 0:01:39 lr 0.000634	time 0.8772 (0.8864)	loss 0.5185 (0.5696)	grad_norm 4.1973 (4.0273)	mem 20675MB
[2025-04-02 18:57:20 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][124/234]	eta 0:01:37 lr 0.000638	time 0.8788 (0.8862)	loss 0.5537 (0.5695)	grad_norm 6.1142 (4.0439)	mem 20675MB
[2025-04-02 18:57:22 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][126/234]	eta 0:01:35 lr 0.000641	time 0.8776 (0.8861)	loss 0.4853 (0.5695)	grad_norm 3.9720 (4.0542)	mem 20675MB
[2025-04-02 18:57:24 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][128/234]	eta 0:01:33 lr 0.000645	time 0.8771 (0.8860)	loss 0.5534 (0.5700)	grad_norm 2.0442 (4.0407)	mem 20675MB
[2025-04-02 18:57:26 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][130/234]	eta 0:01:32 lr 0.000648	time 0.8784 (0.8859)	loss 0.5924 (0.5701)	grad_norm 2.8798 (4.0416)	mem 20675MB
[2025-04-02 18:57:28 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][132/234]	eta 0:01:30 lr 0.000652	time 0.8778 (0.8858)	loss 0.5115 (0.5699)	grad_norm 5.1703 (4.0367)	mem 20675MB
[2025-04-02 18:57:29 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][134/234]	eta 0:01:28 lr 0.000655	time 0.8778 (0.8857)	loss 0.6233 (0.5698)	grad_norm 4.8465 (4.0408)	mem 20675MB
[2025-04-02 18:57:31 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][136/234]	eta 0:01:26 lr 0.000659	time 0.8786 (0.8856)	loss 0.4966 (0.5699)	grad_norm 3.4606 (4.0292)	mem 20675MB
[2025-04-02 18:57:33 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][138/234]	eta 0:01:25 lr 0.000663	time 0.8774 (0.8855)	loss 0.6204 (0.5702)	grad_norm 6.1295 (4.0575)	mem 20675MB
[2025-04-02 18:57:35 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][140/234]	eta 0:01:23 lr 0.000666	time 0.8772 (0.8854)	loss 0.5360 (0.5705)	grad_norm 8.1191 (4.1053)	mem 20675MB
[2025-04-02 18:57:36 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][142/234]	eta 0:01:21 lr 0.000670	time 0.8788 (0.8853)	loss 0.5862 (0.5702)	grad_norm 4.7046 (4.1296)	mem 20675MB
[2025-04-02 18:57:38 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][144/234]	eta 0:01:19 lr 0.000673	time 0.8791 (0.8852)	loss 0.6005 (0.5705)	grad_norm 3.7547 (4.1108)	mem 20675MB
[2025-04-02 18:57:40 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][146/234]	eta 0:01:17 lr 0.000677	time 0.8796 (0.8851)	loss 0.5038 (0.5698)	grad_norm 5.5152 (4.1062)	mem 20675MB
[2025-04-02 18:57:42 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][148/234]	eta 0:01:16 lr 0.000680	time 0.8785 (0.8850)	loss 0.4583 (0.5694)	grad_norm 4.1654 (4.0979)	mem 20675MB
[2025-04-02 18:57:43 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][150/234]	eta 0:01:14 lr 0.000684	time 0.8769 (0.8850)	loss 0.4770 (0.5678)	grad_norm 3.8711 (4.0912)	mem 20675MB
[2025-04-02 18:57:45 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][152/234]	eta 0:01:12 lr 0.000687	time 0.8771 (0.8849)	loss 0.6169 (0.5683)	grad_norm 4.5915 (4.1074)	mem 20675MB
[2025-04-02 18:57:47 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][154/234]	eta 0:01:10 lr 0.000691	time 0.8774 (0.8848)	loss 0.6316 (0.5682)	grad_norm 5.7923 (4.1193)	mem 20675MB
[2025-04-02 18:57:49 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][156/234]	eta 0:01:09 lr 0.000695	time 0.8784 (0.8847)	loss 0.5710 (0.5678)	grad_norm 5.1484 (4.1353)	mem 20675MB
[2025-04-02 18:57:50 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][158/234]	eta 0:01:07 lr 0.000698	time 0.8775 (0.8846)	loss 0.5955 (0.5678)	grad_norm 1.7595 (4.1244)	mem 20675MB
[2025-04-02 18:57:52 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][160/234]	eta 0:01:05 lr 0.000702	time 0.8777 (0.8845)	loss 0.6519 (0.5685)	grad_norm 2.9378 (4.1059)	mem 20675MB
[2025-04-02 18:57:54 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][162/234]	eta 0:01:03 lr 0.000705	time 0.8778 (0.8845)	loss 0.5471 (0.5686)	grad_norm 2.9332 (4.0860)	mem 20675MB
[2025-04-02 18:57:56 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][164/234]	eta 0:01:01 lr 0.000709	time 0.8774 (0.8844)	loss 0.5774 (0.5686)	grad_norm 1.8651 (4.0595)	mem 20675MB
[2025-04-02 18:57:57 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][166/234]	eta 0:01:00 lr 0.000712	time 0.8768 (0.8843)	loss 0.5906 (0.5687)	grad_norm 3.4891 (4.0518)	mem 20675MB
[2025-04-02 18:57:59 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][168/234]	eta 0:00:58 lr 0.000716	time 0.8769 (0.8843)	loss 0.5586 (0.5689)	grad_norm 3.1107 (4.0382)	mem 20675MB
[2025-04-02 18:58:01 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][170/234]	eta 0:00:56 lr 0.000719	time 0.8768 (0.8842)	loss 0.5259 (0.5685)	grad_norm 2.0755 (4.0266)	mem 20675MB
[2025-04-02 18:58:03 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][172/234]	eta 0:00:54 lr 0.000723	time 0.8775 (0.8841)	loss 0.5592 (0.5685)	grad_norm 4.5799 (4.0259)	mem 20675MB
[2025-04-02 18:58:04 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][174/234]	eta 0:00:53 lr 0.000727	time 0.8773 (0.8840)	loss 0.4908 (0.5685)	grad_norm 2.8988 (4.0090)	mem 20675MB
[2025-04-02 18:58:06 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][176/234]	eta 0:00:51 lr 0.000730	time 0.8770 (0.8840)	loss 0.5939 (0.5683)	grad_norm 3.2544 (3.9965)	mem 20675MB
[2025-04-02 18:58:08 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][178/234]	eta 0:00:49 lr 0.000734	time 0.8778 (0.8839)	loss 0.5863 (0.5686)	grad_norm 1.9266 (3.9845)	mem 20675MB
[2025-04-02 18:58:10 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][180/234]	eta 0:00:47 lr 0.000737	time 0.8773 (0.8838)	loss 0.5863 (0.5685)	grad_norm 4.7042 (3.9957)	mem 20675MB
[2025-04-02 18:58:11 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][182/234]	eta 0:00:45 lr 0.000741	time 0.8776 (0.8838)	loss 0.4653 (0.5673)	grad_norm 3.4817 (3.9875)	mem 20675MB
[2025-04-02 18:58:13 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][184/234]	eta 0:00:44 lr 0.000744	time 0.8780 (0.8837)	loss 0.4578 (0.5668)	grad_norm 8.8124 (4.0118)	mem 20675MB
[2025-04-02 18:58:15 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][186/234]	eta 0:00:42 lr 0.000748	time 0.8767 (0.8837)	loss 0.6445 (0.5673)	grad_norm 5.1653 (4.0308)	mem 20675MB
[2025-04-02 18:58:17 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][188/234]	eta 0:00:40 lr 0.000752	time 0.8768 (0.8836)	loss 0.4865 (0.5668)	grad_norm 3.6338 (4.0341)	mem 20675MB
[2025-04-02 18:58:18 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][190/234]	eta 0:00:38 lr 0.000755	time 0.8767 (0.8835)	loss 0.5754 (0.5672)	grad_norm 3.4950 (4.0337)	mem 20675MB
[2025-04-02 18:58:20 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][192/234]	eta 0:00:37 lr 0.000759	time 0.8762 (0.8835)	loss 0.6225 (0.5675)	grad_norm 3.9764 (4.0226)	mem 20675MB
[2025-04-02 18:58:22 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][194/234]	eta 0:00:35 lr 0.000762	time 0.8778 (0.8834)	loss 0.5845 (0.5676)	grad_norm 3.5898 (4.0139)	mem 20675MB
[2025-04-02 18:58:24 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][196/234]	eta 0:00:33 lr 0.000766	time 0.8769 (0.8834)	loss 0.5531 (0.5676)	grad_norm 1.7064 (3.9960)	mem 20675MB
[2025-04-02 18:58:25 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][198/234]	eta 0:00:31 lr 0.000769	time 0.8770 (0.8833)	loss 0.6196 (0.5676)	grad_norm 4.1149 (3.9893)	mem 20675MB
[2025-04-02 18:58:27 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][200/234]	eta 0:00:30 lr 0.000773	time 0.8775 (0.8833)	loss 0.6211 (0.5680)	grad_norm 4.3815 (3.9837)	mem 20675MB
[2025-04-02 18:58:29 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][202/234]	eta 0:00:28 lr 0.000776	time 0.8778 (0.8832)	loss 0.5684 (0.5678)	grad_norm 3.9467 (3.9827)	mem 20675MB
[2025-04-02 18:58:31 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][204/234]	eta 0:00:26 lr 0.000780	time 0.8783 (0.8832)	loss 0.5906 (0.5680)	grad_norm 3.9364 (3.9763)	mem 20675MB
[2025-04-02 18:58:33 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][206/234]	eta 0:00:24 lr 0.000784	time 0.8783 (0.8831)	loss 0.5921 (0.5680)	grad_norm 2.5321 (3.9623)	mem 20675MB
[2025-04-02 18:58:34 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][208/234]	eta 0:00:22 lr 0.000787	time 0.8798 (0.8831)	loss 0.5382 (0.5676)	grad_norm 2.0741 (3.9448)	mem 20675MB
[2025-04-02 18:58:36 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][210/234]	eta 0:00:21 lr 0.000791	time 0.8773 (0.8831)	loss 0.5831 (0.5678)	grad_norm 3.3450 (3.9379)	mem 20675MB
[2025-04-02 18:58:38 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][212/234]	eta 0:00:19 lr 0.000794	time 0.8772 (0.8830)	loss 0.4891 (0.5674)	grad_norm 3.5702 (3.9291)	mem 20675MB
[2025-04-02 18:58:40 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][214/234]	eta 0:00:17 lr 0.000798	time 0.8777 (0.8830)	loss 0.5288 (0.5669)	grad_norm 2.3245 (3.9163)	mem 20675MB
[2025-04-02 18:58:41 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][216/234]	eta 0:00:15 lr 0.000801	time 0.8792 (0.8830)	loss 0.5087 (0.5667)	grad_norm 3.6007 (3.9147)	mem 20675MB
[2025-04-02 18:58:43 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][218/234]	eta 0:00:14 lr 0.000805	time 0.8785 (0.8829)	loss 0.4299 (0.5657)	grad_norm 4.0722 (3.9147)	mem 20675MB
[2025-04-02 18:58:45 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][220/234]	eta 0:00:12 lr 0.000808	time 0.8772 (0.8829)	loss 0.4963 (0.5654)	grad_norm 7.0577 (3.9253)	mem 20675MB
[2025-04-02 18:58:47 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][222/234]	eta 0:00:10 lr 0.000812	time 0.8775 (0.8828)	loss 0.4264 (0.5646)	grad_norm 7.1271 (3.9401)	mem 20675MB
[2025-04-02 18:58:48 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][224/234]	eta 0:00:08 lr 0.000816	time 0.8778 (0.8828)	loss 0.5385 (0.5646)	grad_norm 3.8120 (3.9498)	mem 20675MB
[2025-04-02 18:58:50 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][226/234]	eta 0:00:07 lr 0.000819	time 0.8783 (0.8828)	loss 0.5749 (0.5645)	grad_norm 2.4186 (3.9377)	mem 20675MB
[2025-04-02 18:58:52 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][228/234]	eta 0:00:05 lr 0.000823	time 0.8777 (0.8827)	loss 0.5297 (0.5641)	grad_norm 4.4527 (3.9380)	mem 20675MB
[2025-04-02 18:58:54 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][230/234]	eta 0:00:03 lr 0.000826	time 0.8768 (0.8827)	loss 0.4729 (0.5639)	grad_norm 3.1643 (3.9347)	mem 20675MB
[2025-04-02 18:58:55 simmim_finetune] (main_finetune.py 252): INFO Train: [1/30][232/234]	eta 0:00:01 lr 0.000830	time 0.8776 (0.8827)	loss 0.5469 (0.5635)	grad_norm 6.9206 (3.9465)	mem 20675MB
[2025-04-02 18:58:56 simmim_finetune] (main_finetune.py 260): INFO EPOCH 1 training takes 0:03:26
[2025-04-02 18:58:57 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.057 (1.057)	Loss 1.5256 (1.5256)	Acc@1 32.031 (32.031)	Mem 20675MB
[2025-04-02 18:58:58 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 51.934
[2025-04-02 18:58:58 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 51.9%
[2025-04-02 18:58:58 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 53.59%
[2025-04-02 18:58:58 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.1581110240486824e-06, 3.1581110240486824e-06, 4.8135687900113405e-06, 4.8135687900113405e-06, 7.360426891492354e-06, 7.360426891492354e-06, 1.1278670124540068e-05, 1.1278670124540068e-05, 1.7306736636921163e-05, 1.7306736636921163e-05, 2.6580685117507465e-05, 2.6580685117507465e-05, 4.084829816456332e-05, 4.084829816456332e-05, 6.279847208311077e-05, 6.279847208311077e-05, 9.656797041933762e-05, 9.656797041933762e-05, 0.00014852104478276355, 0.00014852104478276355, 0.00022844885149572654, 0.00022844885149572654, 0.000351414707977208, 0.000351414707977208, 0.0005405929487179488, 0.0005405929487179488, 0.0008316363960113961, 0.0008316363960113961]
[2025-04-02 18:59:00 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][0/234]	eta 0:07:20 lr 0.000833	time 1.8827 (1.8827)	loss 0.4407 (0.4407)	grad_norm 3.6991 (3.6991)	mem 20675MB
[2025-04-02 18:59:01 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][2/234]	eta 0:04:41 lr 0.000837	time 0.8777 (1.2130)	loss 0.5175 (0.4990)	grad_norm 7.9113 (6.5050)	mem 20675MB
[2025-04-02 18:59:03 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][4/234]	eta 0:04:08 lr 0.000841	time 0.8779 (1.0792)	loss 0.5221 (0.5221)	grad_norm 5.2215 (6.2225)	mem 20675MB
[2025-04-02 18:59:05 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][6/234]	eta 0:03:53 lr 0.000844	time 0.8782 (1.0220)	loss 0.5502 (0.5411)	grad_norm 3.5540 (5.7649)	mem 20675MB
[2025-04-02 18:59:07 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][8/234]	eta 0:03:43 lr 0.000848	time 0.8770 (0.9900)	loss 0.6142 (0.5408)	grad_norm 5.5925 (5.8409)	mem 20675MB
[2025-04-02 18:59:08 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][10/234]	eta 0:03:37 lr 0.000851	time 0.8781 (0.9697)	loss 0.5836 (0.5389)	grad_norm 2.4127 (5.2958)	mem 20675MB
[2025-04-02 18:59:10 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][12/234]	eta 0:03:32 lr 0.000855	time 0.8781 (0.9557)	loss 0.5235 (0.5336)	grad_norm 1.9199 (4.8440)	mem 20675MB
[2025-04-02 18:59:12 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][14/234]	eta 0:03:27 lr 0.000858	time 0.8779 (0.9454)	loss 0.6552 (0.5433)	grad_norm 4.2417 (4.6752)	mem 20675MB
[2025-04-02 18:59:14 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][16/234]	eta 0:03:24 lr 0.000862	time 0.8778 (0.9375)	loss 0.5797 (0.5419)	grad_norm 2.6479 (4.5222)	mem 20675MB
[2025-04-02 18:59:15 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][18/234]	eta 0:03:21 lr 0.000865	time 0.8779 (0.9313)	loss 0.4803 (0.5436)	grad_norm 1.7863 (4.4592)	mem 20675MB
[2025-04-02 18:59:17 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][20/234]	eta 0:03:18 lr 0.000869	time 0.8774 (0.9263)	loss 0.5151 (0.5437)	grad_norm 3.0634 (4.3812)	mem 20675MB
[2025-04-02 18:59:19 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][22/234]	eta 0:03:15 lr 0.000873	time 0.8775 (0.9221)	loss 0.5833 (0.5435)	grad_norm 1.3250 (4.1844)	mem 20675MB
[2025-04-02 18:59:21 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][24/234]	eta 0:03:12 lr 0.000876	time 0.8770 (0.9186)	loss 0.5291 (0.5433)	grad_norm 5.1615 (4.1485)	mem 20675MB
[2025-04-02 18:59:22 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][26/234]	eta 0:03:10 lr 0.000880	time 0.8771 (0.9156)	loss 0.5465 (0.5421)	grad_norm 2.0611 (4.0159)	mem 20675MB
[2025-04-02 18:59:24 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][28/234]	eta 0:03:08 lr 0.000883	time 0.8771 (0.9130)	loss 0.5890 (0.5436)	grad_norm 3.7507 (4.0223)	mem 20675MB
[2025-04-02 18:59:26 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][30/234]	eta 0:03:05 lr 0.000887	time 0.8775 (0.9107)	loss 0.5597 (0.5464)	grad_norm 2.2627 (3.9576)	mem 20675MB
[2025-04-02 18:59:28 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][32/234]	eta 0:03:03 lr 0.000890	time 0.8768 (0.9087)	loss 0.6103 (0.5464)	grad_norm 4.4845 (3.9488)	mem 20675MB
[2025-04-02 18:59:29 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][34/234]	eta 0:03:01 lr 0.000894	time 0.8780 (0.9070)	loss 0.4737 (0.5447)	grad_norm 3.2885 (3.9216)	mem 20675MB
[2025-04-02 18:59:31 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][36/234]	eta 0:02:59 lr 0.000898	time 0.8769 (0.9055)	loss 0.5179 (0.5435)	grad_norm 2.3832 (3.8903)	mem 20675MB
[2025-04-02 18:59:33 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][38/234]	eta 0:02:57 lr 0.000901	time 0.8779 (0.9041)	loss 0.5425 (0.5434)	grad_norm 4.3225 (3.8602)	mem 20675MB
[2025-04-02 18:59:35 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][40/234]	eta 0:02:55 lr 0.000905	time 0.8780 (0.9029)	loss 0.6634 (0.5496)	grad_norm 6.8355 (4.0007)	mem 20675MB
[2025-04-02 18:59:36 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][42/234]	eta 0:02:53 lr 0.000908	time 0.8791 (0.9018)	loss 0.5680 (0.5497)	grad_norm 3.0219 (3.9314)	mem 20675MB
[2025-04-02 18:59:38 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][44/234]	eta 0:02:51 lr 0.000912	time 0.8787 (0.9008)	loss 0.5797 (0.5510)	grad_norm 3.1779 (3.8849)	mem 20675MB
[2025-04-02 18:59:40 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][46/234]	eta 0:02:49 lr 0.000915	time 0.8785 (0.8999)	loss 0.5432 (0.5519)	grad_norm 1.7128 (3.7890)	mem 20675MB
[2025-04-02 18:59:42 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][48/234]	eta 0:02:47 lr 0.000919	time 0.8786 (0.8991)	loss 0.5291 (0.5516)	grad_norm 1.7843 (3.7518)	mem 20675MB
[2025-04-02 18:59:43 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][50/234]	eta 0:02:45 lr 0.000922	time 0.8787 (0.8983)	loss 0.4441 (0.5498)	grad_norm 3.6426 (3.7136)	mem 20675MB
[2025-04-02 18:59:45 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][52/234]	eta 0:02:43 lr 0.000926	time 0.8788 (0.8976)	loss 0.5059 (0.5483)	grad_norm 5.4021 (3.7239)	mem 20675MB
[2025-04-02 18:59:47 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][54/234]	eta 0:02:41 lr 0.000930	time 0.8763 (0.8969)	loss 0.5279 (0.5458)	grad_norm 4.8565 (3.7769)	mem 20675MB
[2025-04-02 18:59:49 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][56/234]	eta 0:02:39 lr 0.000933	time 0.8784 (0.8963)	loss 0.6254 (0.5452)	grad_norm 5.1828 (3.8227)	mem 20675MB
[2025-04-02 18:59:50 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][58/234]	eta 0:02:37 lr 0.000937	time 0.8779 (0.8957)	loss 0.5735 (0.5423)	grad_norm 3.7556 (3.8292)	mem 20675MB
[2025-04-02 18:59:52 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][60/234]	eta 0:02:35 lr 0.000940	time 0.8786 (0.8951)	loss 0.5642 (0.5405)	grad_norm 2.7043 (3.8122)	mem 20675MB
[2025-04-02 18:59:54 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][62/234]	eta 0:02:33 lr 0.000944	time 0.8782 (0.8946)	loss 0.6016 (0.5416)	grad_norm 4.0115 (3.8105)	mem 20675MB
[2025-04-02 18:59:56 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][64/234]	eta 0:02:32 lr 0.000947	time 0.8777 (0.8941)	loss 0.5321 (0.5426)	grad_norm 2.9848 (3.7799)	mem 20675MB
[2025-04-02 18:59:58 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][66/234]	eta 0:02:30 lr 0.000951	time 0.8777 (0.8937)	loss 0.4870 (0.5409)	grad_norm 3.5756 (3.7619)	mem 20675MB
[2025-04-02 18:59:59 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][68/234]	eta 0:02:28 lr 0.000954	time 0.8785 (0.8932)	loss 0.4425 (0.5394)	grad_norm 3.9953 (3.7462)	mem 20675MB
[2025-04-02 19:00:01 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][70/234]	eta 0:02:26 lr 0.000958	time 0.8779 (0.8928)	loss 0.5799 (0.5407)	grad_norm 3.0875 (3.7286)	mem 20675MB
[2025-04-02 19:00:03 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][72/234]	eta 0:02:24 lr 0.000962	time 0.8776 (0.8924)	loss 0.5618 (0.5423)	grad_norm 3.7391 (3.7456)	mem 20675MB
[2025-04-02 19:00:05 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][74/234]	eta 0:02:22 lr 0.000965	time 0.8784 (0.8921)	loss 0.5344 (0.5418)	grad_norm 2.4107 (3.7241)	mem 20675MB
[2025-04-02 19:00:06 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][76/234]	eta 0:02:20 lr 0.000969	time 0.8778 (0.8918)	loss 0.4533 (0.5413)	grad_norm 3.0047 (3.7014)	mem 20675MB
[2025-04-02 19:00:08 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][78/234]	eta 0:02:19 lr 0.000972	time 0.8804 (0.8915)	loss 0.6115 (0.5412)	grad_norm 3.5680 (3.6868)	mem 20675MB
[2025-04-02 19:00:10 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][80/234]	eta 0:02:17 lr 0.000976	time 0.8798 (0.8912)	loss 0.6302 (0.5425)	grad_norm 1.9195 (3.6502)	mem 20675MB
[2025-04-02 19:00:12 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][82/234]	eta 0:02:15 lr 0.000979	time 0.8793 (0.8909)	loss 0.5623 (0.5413)	grad_norm 2.7991 (3.6412)	mem 20675MB
[2025-04-02 19:00:13 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][84/234]	eta 0:02:13 lr 0.000983	time 0.8799 (0.8907)	loss 0.5712 (0.5432)	grad_norm 3.3801 (3.6397)	mem 20675MB
[2025-04-02 19:00:15 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][86/234]	eta 0:02:11 lr 0.000987	time 0.8792 (0.8904)	loss 0.5574 (0.5435)	grad_norm 1.8454 (3.5999)	mem 20675MB
[2025-04-02 19:00:17 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][88/234]	eta 0:02:09 lr 0.000990	time 0.8784 (0.8902)	loss 0.4652 (0.5430)	grad_norm 3.3365 (3.5750)	mem 20675MB
[2025-04-02 19:00:19 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][90/234]	eta 0:02:08 lr 0.000994	time 0.8781 (0.8900)	loss 0.5928 (0.5442)	grad_norm 2.0169 (3.5644)	mem 20675MB
[2025-04-02 19:00:20 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][92/234]	eta 0:02:06 lr 0.000997	time 0.8788 (0.8897)	loss 0.5788 (0.5449)	grad_norm 5.2862 (3.5688)	mem 20675MB
[2025-04-02 19:00:22 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][94/234]	eta 0:02:04 lr 0.001001	time 0.8776 (0.8895)	loss 0.5016 (0.5453)	grad_norm 2.1607 (3.5539)	mem 20675MB
[2025-04-02 19:00:24 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][96/234]	eta 0:02:02 lr 0.001004	time 0.8779 (0.8893)	loss 0.5747 (0.5459)	grad_norm 4.7464 (3.5948)	mem 20675MB
[2025-04-02 19:00:26 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][98/234]	eta 0:02:00 lr 0.001008	time 0.8781 (0.8891)	loss 0.5769 (0.5455)	grad_norm 4.9034 (3.6209)	mem 20675MB
[2025-04-02 19:00:27 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][100/234]	eta 0:01:59 lr 0.001011	time 0.8789 (0.8889)	loss 0.5334 (0.5461)	grad_norm 8.1186 (3.7025)	mem 20675MB
[2025-04-02 19:00:29 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][102/234]	eta 0:01:57 lr 0.001015	time 0.8789 (0.8887)	loss 0.5824 (0.5466)	grad_norm 2.7296 (3.6859)	mem 20675MB
[2025-04-02 19:00:31 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][104/234]	eta 0:01:55 lr 0.001019	time 0.8792 (0.8885)	loss 0.4992 (0.5456)	grad_norm 3.3024 (3.6895)	mem 20675MB
[2025-04-02 19:00:33 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][106/234]	eta 0:01:53 lr 0.001022	time 0.8781 (0.8884)	loss 0.5783 (0.5460)	grad_norm 2.9907 (3.6715)	mem 20675MB
[2025-04-02 19:00:34 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][108/234]	eta 0:01:51 lr 0.001026	time 0.8796 (0.8882)	loss 0.5419 (0.5452)	grad_norm 1.7301 (3.6620)	mem 20675MB
[2025-04-02 19:00:36 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][110/234]	eta 0:01:50 lr 0.001029	time 0.8783 (0.8880)	loss 0.5540 (0.5457)	grad_norm 2.3143 (3.6533)	mem 20675MB
[2025-04-02 19:00:38 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][112/234]	eta 0:01:48 lr 0.001033	time 0.8791 (0.8879)	loss 0.6026 (0.5454)	grad_norm 4.2519 (3.6502)	mem 20675MB
[2025-04-02 19:00:40 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][114/234]	eta 0:01:46 lr 0.001036	time 0.8787 (0.8878)	loss 0.5194 (0.5452)	grad_norm 1.6295 (3.6411)	mem 20675MB
[2025-04-02 19:00:41 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][116/234]	eta 0:01:44 lr 0.001040	time 0.8784 (0.8876)	loss 0.5140 (0.5443)	grad_norm 3.6218 (3.6444)	mem 20675MB
[2025-04-02 19:00:43 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][118/234]	eta 0:01:42 lr 0.001043	time 0.8788 (0.8875)	loss 0.5436 (0.5445)	grad_norm 2.9102 (3.6355)	mem 20675MB
[2025-04-02 19:00:45 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][120/234]	eta 0:01:41 lr 0.001047	time 0.8797 (0.8874)	loss 0.4705 (0.5446)	grad_norm 3.2981 (3.6436)	mem 20675MB
[2025-04-02 19:00:47 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][122/234]	eta 0:01:39 lr 0.001051	time 0.8775 (0.8872)	loss 0.5534 (0.5448)	grad_norm 4.5650 (3.6422)	mem 20675MB
[2025-04-02 19:00:49 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][124/234]	eta 0:01:37 lr 0.001054	time 0.8800 (0.8871)	loss 0.4725 (0.5439)	grad_norm 2.2469 (3.6156)	mem 20675MB
[2025-04-02 19:00:50 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][126/234]	eta 0:01:35 lr 0.001058	time 0.8803 (0.8870)	loss 0.5833 (0.5440)	grad_norm 2.9714 (3.6278)	mem 20675MB
[2025-04-02 19:00:52 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][128/234]	eta 0:01:34 lr 0.001061	time 0.8804 (0.8869)	loss 0.6944 (0.5441)	grad_norm 7.4090 (3.6502)	mem 20675MB
[2025-04-02 19:00:54 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][130/234]	eta 0:01:32 lr 0.001065	time 0.8802 (0.8868)	loss 0.5230 (0.5437)	grad_norm 2.7246 (3.6393)	mem 20675MB
[2025-04-02 19:00:56 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][132/234]	eta 0:01:30 lr 0.001068	time 0.8830 (0.8868)	loss 0.4436 (0.5430)	grad_norm 3.8022 (3.6384)	mem 20675MB
[2025-04-02 19:00:57 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][134/234]	eta 0:01:28 lr 0.001072	time 0.8809 (0.8867)	loss 0.4031 (0.5422)	grad_norm 4.3152 (3.6433)	mem 20675MB
[2025-04-02 19:00:59 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][136/234]	eta 0:01:26 lr 0.001076	time 0.8796 (0.8866)	loss 0.5508 (0.5421)	grad_norm 2.5357 (3.6248)	mem 20675MB
[2025-04-02 19:01:01 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][138/234]	eta 0:01:25 lr 0.001079	time 0.8795 (0.8865)	loss 0.5560 (0.5415)	grad_norm 4.9884 (3.6367)	mem 20675MB
[2025-04-02 19:01:03 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][140/234]	eta 0:01:23 lr 0.001083	time 0.8808 (0.8864)	loss 0.6062 (0.5418)	grad_norm 3.6562 (3.6292)	mem 20675MB
[2025-04-02 19:01:04 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][142/234]	eta 0:01:21 lr 0.001086	time 0.8795 (0.8864)	loss 0.5124 (0.5416)	grad_norm 1.9915 (3.6438)	mem 20675MB
[2025-04-02 19:01:06 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][144/234]	eta 0:01:19 lr 0.001090	time 0.8798 (0.8863)	loss 0.5047 (0.5409)	grad_norm 2.9998 (3.6437)	mem 20675MB
[2025-04-02 19:01:08 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][146/234]	eta 0:01:17 lr 0.001093	time 0.8800 (0.8862)	loss 0.6955 (0.5422)	grad_norm 4.0675 (3.6678)	mem 20675MB
[2025-04-02 19:01:10 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][148/234]	eta 0:01:16 lr 0.001097	time 0.8801 (0.8861)	loss 0.5664 (0.5423)	grad_norm 1.6943 (3.6477)	mem 20675MB
[2025-04-02 19:01:11 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][150/234]	eta 0:01:14 lr 0.001100	time 0.8805 (0.8861)	loss 0.5522 (0.5422)	grad_norm 2.1575 (3.6291)	mem 20675MB
[2025-04-02 19:01:13 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][152/234]	eta 0:01:12 lr 0.001104	time 0.8800 (0.8860)	loss 0.6232 (0.5428)	grad_norm 2.4283 (3.6088)	mem 20675MB
[2025-04-02 19:01:15 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][154/234]	eta 0:01:10 lr 0.001108	time 0.8788 (0.8859)	loss 0.5907 (0.5430)	grad_norm 2.7091 (3.5969)	mem 20675MB
[2025-04-02 19:01:17 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][156/234]	eta 0:01:09 lr 0.001111	time 0.8779 (0.8858)	loss 0.5227 (0.5431)	grad_norm 3.6197 (3.5862)	mem 20675MB
[2025-04-02 19:01:18 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][158/234]	eta 0:01:07 lr 0.001115	time 0.8783 (0.8858)	loss 0.5255 (0.5423)	grad_norm 3.8150 (3.5800)	mem 20675MB
[2025-04-02 19:01:20 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][160/234]	eta 0:01:05 lr 0.001118	time 0.8779 (0.8857)	loss 0.5294 (0.5424)	grad_norm 3.3537 (3.5837)	mem 20675MB
[2025-04-02 19:01:22 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][162/234]	eta 0:01:03 lr 0.001122	time 0.8797 (0.8856)	loss 0.5978 (0.5433)	grad_norm 4.0617 (3.6036)	mem 20675MB
[2025-04-02 19:01:24 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][164/234]	eta 0:01:01 lr 0.001125	time 0.8785 (0.8855)	loss 0.5703 (0.5437)	grad_norm 3.8891 (3.5942)	mem 20675MB
[2025-04-02 19:01:26 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][166/234]	eta 0:01:00 lr 0.001129	time 0.8797 (0.8855)	loss 0.5087 (0.5433)	grad_norm 2.0092 (3.5764)	mem 20675MB
[2025-04-02 19:01:27 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][168/234]	eta 0:00:58 lr 0.001133	time 0.8792 (0.8854)	loss 0.5055 (0.5429)	grad_norm 2.1304 (3.5609)	mem 20675MB
[2025-04-02 19:01:29 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][170/234]	eta 0:00:56 lr 0.001136	time 0.8797 (0.8854)	loss 0.6184 (0.5437)	grad_norm 3.2082 (3.5544)	mem 20675MB
[2025-04-02 19:01:31 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][172/234]	eta 0:00:54 lr 0.001140	time 0.8787 (0.8853)	loss 0.4260 (0.5423)	grad_norm 3.1397 (3.5470)	mem 20675MB
[2025-04-02 19:01:33 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][174/234]	eta 0:00:53 lr 0.001143	time 0.8784 (0.8852)	loss 0.5897 (0.5418)	grad_norm 3.0957 (3.5515)	mem 20675MB
[2025-04-02 19:01:34 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][176/234]	eta 0:00:51 lr 0.001147	time 0.8790 (0.8852)	loss 0.4340 (0.5409)	grad_norm 3.2927 (3.5600)	mem 20675MB
[2025-04-02 19:01:36 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][178/234]	eta 0:00:49 lr 0.001150	time 0.8781 (0.8851)	loss 0.5653 (0.5411)	grad_norm 4.3221 (3.5775)	mem 20675MB
[2025-04-02 19:01:38 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][180/234]	eta 0:00:47 lr 0.001154	time 0.8798 (0.8850)	loss 0.5028 (0.5405)	grad_norm 4.7956 (3.5778)	mem 20675MB
[2025-04-02 19:01:40 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][182/234]	eta 0:00:46 lr 0.001157	time 0.8794 (0.8850)	loss 0.5820 (0.5409)	grad_norm 7.1955 (3.5970)	mem 20675MB
[2025-04-02 19:01:41 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][184/234]	eta 0:00:44 lr 0.001161	time 0.8777 (0.8849)	loss 0.6596 (0.5408)	grad_norm 7.6140 (3.6195)	mem 20675MB
[2025-04-02 19:01:43 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][186/234]	eta 0:00:42 lr 0.001165	time 0.8795 (0.8848)	loss 0.4947 (0.5407)	grad_norm 2.5267 (3.6177)	mem 20675MB
[2025-04-02 19:01:45 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][188/234]	eta 0:00:40 lr 0.001168	time 0.8791 (0.8848)	loss 0.5689 (0.5404)	grad_norm 6.1811 (3.6265)	mem 20675MB
[2025-04-02 19:01:47 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][190/234]	eta 0:00:38 lr 0.001172	time 0.8781 (0.8847)	loss 0.5464 (0.5404)	grad_norm 2.5182 (3.6530)	mem 20675MB
[2025-04-02 19:01:48 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][192/234]	eta 0:00:37 lr 0.001175	time 0.8777 (0.8847)	loss 0.4110 (0.5393)	grad_norm 3.5224 (3.6468)	mem 20675MB
[2025-04-02 19:01:50 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][194/234]	eta 0:00:35 lr 0.001179	time 0.8777 (0.8846)	loss 0.5281 (0.5399)	grad_norm 3.2245 (3.6512)	mem 20675MB
[2025-04-02 19:01:52 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][196/234]	eta 0:00:33 lr 0.001182	time 0.8783 (0.8846)	loss 0.5035 (0.5400)	grad_norm 4.2294 (3.6527)	mem 20675MB
[2025-04-02 19:01:54 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][198/234]	eta 0:00:31 lr 0.001186	time 0.8773 (0.8845)	loss 0.5494 (0.5400)	grad_norm 1.8946 (3.6446)	mem 20675MB
[2025-04-02 19:01:55 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][200/234]	eta 0:00:30 lr 0.001189	time 0.8778 (0.8844)	loss 0.6152 (0.5405)	grad_norm 3.7362 (3.6351)	mem 20675MB
[2025-04-02 19:01:57 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][202/234]	eta 0:00:28 lr 0.001193	time 0.8784 (0.8844)	loss 0.5744 (0.5408)	grad_norm 1.3406 (3.6123)	mem 20675MB
[2025-04-02 19:01:59 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][204/234]	eta 0:00:26 lr 0.001197	time 0.8798 (0.8844)	loss 0.5719 (0.5408)	grad_norm 1.9148 (3.5994)	mem 20675MB
[2025-04-02 19:02:01 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][206/234]	eta 0:00:24 lr 0.001200	time 0.8796 (0.8843)	loss 0.5621 (0.5411)	grad_norm 1.8157 (3.5851)	mem 20675MB
[2025-04-02 19:02:02 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][208/234]	eta 0:00:22 lr 0.001204	time 0.8792 (0.8843)	loss 0.5490 (0.5409)	grad_norm 3.3847 (3.5792)	mem 20675MB
[2025-04-02 19:02:04 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][210/234]	eta 0:00:21 lr 0.001207	time 0.8792 (0.8842)	loss 0.4817 (0.5408)	grad_norm 2.8052 (3.5759)	mem 20675MB
[2025-04-02 19:02:06 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][212/234]	eta 0:00:19 lr 0.001211	time 0.8776 (0.8842)	loss 0.5926 (0.5405)	grad_norm 3.0123 (3.5692)	mem 20675MB
[2025-04-02 19:02:08 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][214/234]	eta 0:00:17 lr 0.001214	time 0.8790 (0.8841)	loss 0.4656 (0.5405)	grad_norm 2.2408 (3.5748)	mem 20675MB
[2025-04-02 19:02:09 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][216/234]	eta 0:00:15 lr 0.001218	time 0.8804 (0.8841)	loss 0.5592 (0.5408)	grad_norm 3.6054 (3.5723)	mem 20675MB
[2025-04-02 19:02:11 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][218/234]	eta 0:00:14 lr 0.001222	time 0.8803 (0.8841)	loss 0.5140 (0.5405)	grad_norm 2.4983 (3.5615)	mem 20675MB
[2025-04-02 19:02:13 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][220/234]	eta 0:00:12 lr 0.001225	time 0.8802 (0.8841)	loss 0.4693 (0.5400)	grad_norm 4.3856 (3.5625)	mem 20675MB
[2025-04-02 19:02:15 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][222/234]	eta 0:00:10 lr 0.001229	time 0.8797 (0.8840)	loss 0.4314 (0.5392)	grad_norm 4.8987 (3.5644)	mem 20675MB
[2025-04-02 19:02:17 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][224/234]	eta 0:00:08 lr 0.001232	time 0.8802 (0.8840)	loss 0.6511 (0.5402)	grad_norm 3.2430 (3.5637)	mem 20675MB
[2025-04-02 19:02:18 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][226/234]	eta 0:00:07 lr 0.001236	time 0.8798 (0.8840)	loss 0.5639 (0.5405)	grad_norm 2.9082 (3.5622)	mem 20675MB
[2025-04-02 19:02:20 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][228/234]	eta 0:00:05 lr 0.001239	time 0.8793 (0.8839)	loss 0.3920 (0.5393)	grad_norm 2.7916 (3.5546)	mem 20675MB
[2025-04-02 19:02:22 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][230/234]	eta 0:00:03 lr 0.001243	time 0.8797 (0.8839)	loss 0.4204 (0.5388)	grad_norm 2.8291 (3.5542)	mem 20675MB
[2025-04-02 19:02:24 simmim_finetune] (main_finetune.py 252): INFO Train: [2/30][232/234]	eta 0:00:01 lr 0.001246	time 0.8790 (0.8839)	loss 0.5435 (0.5386)	grad_norm 2.4874 (3.5476)	mem 20675MB
[2025-04-02 19:02:25 simmim_finetune] (main_finetune.py 260): INFO EPOCH 2 training takes 0:03:26
[2025-04-02 19:02:26 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.143 (1.143)	Loss 1.4000 (1.4000)	Acc@1 32.031 (32.031)	Mem 20675MB
[2025-04-02 19:02:26 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 51.934
[2025-04-02 19:02:26 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 51.9%
[2025-04-02 19:02:26 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 53.59%
[2025-04-02 19:02:26 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [4.61528014530648e-06, 4.61528014530648e-06, 7.100239232972055e-06, 7.100239232972055e-06, 1.0923253213996017e-05, 1.0923253213996017e-05, 1.6804813184802116e-05, 1.6804813184802116e-05, 2.585336698604226e-05, 2.585336698604226e-05, 3.977421898795018e-05, 3.977421898795018e-05, 6.119091437550083e-05, 6.119091437550083e-05, 9.413967651019411e-05, 9.413967651019411e-05, 0.00014483007979433762, 0.00014483007979433762, 0.0002228153156160969, 0.0002228153156160969, 0.0003427926014957265, 0.0003427926014957265, 0.0005273730413105414, 0.0005273730413105414, 0.0008113429487179488, 0.0008113429487179488, 0.0012482197293447294, 0.0012482197293447294]
[2025-04-02 19:02:28 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][0/234]	eta 0:08:53 lr 0.001219	time 2.2787 (2.2787)	loss 0.6053 (0.6053)	grad_norm 4.1916 (4.1916)	mem 20675MB
[2025-04-02 19:02:30 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][2/234]	eta 0:05:12 lr 0.001219	time 0.8783 (1.3455)	loss 0.6278 (0.5990)	grad_norm 3.1152 (3.1661)	mem 20675MB
[2025-04-02 19:02:32 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][4/234]	eta 0:04:26 lr 0.001219	time 0.8788 (1.1588)	loss 0.4362 (0.5592)	grad_norm 2.9863 (2.7996)	mem 20675MB
[2025-04-02 19:02:34 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][6/234]	eta 0:04:05 lr 0.001219	time 0.8779 (1.0787)	loss 0.5079 (0.5506)	grad_norm 2.5641 (2.7818)	mem 20675MB
[2025-04-02 19:02:35 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][8/234]	eta 0:03:53 lr 0.001219	time 0.8787 (1.0344)	loss 0.3857 (0.5280)	grad_norm 3.0563 (2.7359)	mem 20675MB
[2025-04-02 19:02:37 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][10/234]	eta 0:03:45 lr 0.001219	time 0.8794 (1.0063)	loss 0.4955 (0.5114)	grad_norm 2.9232 (2.7758)	mem 20675MB
[2025-04-02 19:02:39 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][12/234]	eta 0:03:39 lr 0.001218	time 0.8784 (0.9868)	loss 0.6612 (0.5159)	grad_norm 4.9130 (3.1061)	mem 20675MB
[2025-04-02 19:02:41 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][14/234]	eta 0:03:33 lr 0.001218	time 0.8787 (0.9724)	loss 0.3952 (0.5128)	grad_norm 3.6402 (3.2010)	mem 20675MB
[2025-04-02 19:02:42 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][16/234]	eta 0:03:29 lr 0.001218	time 0.8782 (0.9615)	loss 0.5505 (0.5190)	grad_norm 3.8519 (3.3060)	mem 20675MB
[2025-04-02 19:02:44 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][18/234]	eta 0:03:25 lr 0.001218	time 0.8787 (0.9528)	loss 0.6625 (0.5226)	grad_norm 6.8895 (3.4715)	mem 20675MB
[2025-04-02 19:02:46 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][20/234]	eta 0:03:22 lr 0.001218	time 0.8802 (0.9459)	loss 0.5610 (0.5267)	grad_norm 3.8927 (3.4197)	mem 20675MB
[2025-04-02 19:02:48 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][22/234]	eta 0:03:19 lr 0.001217	time 0.8788 (0.9402)	loss 0.5349 (0.5286)	grad_norm 3.5401 (3.3824)	mem 20675MB
[2025-04-02 19:02:49 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][24/234]	eta 0:03:16 lr 0.001217	time 0.8780 (0.9353)	loss 0.4963 (0.5279)	grad_norm 5.8528 (3.4526)	mem 20675MB
[2025-04-02 19:02:51 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][26/234]	eta 0:03:13 lr 0.001217	time 0.8783 (0.9311)	loss 0.6148 (0.5323)	grad_norm 5.4160 (3.4955)	mem 20675MB
[2025-04-02 19:02:53 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][28/234]	eta 0:03:11 lr 0.001217	time 0.8792 (0.9276)	loss 0.5594 (0.5345)	grad_norm 2.2309 (3.4041)	mem 20675MB
[2025-04-02 19:02:55 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][30/234]	eta 0:03:08 lr 0.001217	time 0.8790 (0.9245)	loss 0.5300 (0.5363)	grad_norm 2.3003 (3.3724)	mem 20675MB
[2025-04-02 19:02:56 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][32/234]	eta 0:03:06 lr 0.001217	time 0.8786 (0.9217)	loss 0.5656 (0.5382)	grad_norm 4.5606 (3.3618)	mem 20675MB
[2025-04-02 19:02:58 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][34/234]	eta 0:03:03 lr 0.001216	time 0.8778 (0.9193)	loss 0.5991 (0.5400)	grad_norm 2.5566 (3.3079)	mem 20675MB
[2025-04-02 19:03:00 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][36/234]	eta 0:03:01 lr 0.001216	time 0.8782 (0.9171)	loss 0.5199 (0.5407)	grad_norm 4.8271 (3.3579)	mem 20675MB
[2025-04-02 19:03:02 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][38/234]	eta 0:02:59 lr 0.001216	time 0.8772 (0.9151)	loss 0.5964 (0.5422)	grad_norm 4.9539 (3.3598)	mem 20675MB
[2025-04-02 19:03:03 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][40/234]	eta 0:02:57 lr 0.001216	time 0.8785 (0.9133)	loss 0.5564 (0.5428)	grad_norm 5.2299 (3.5325)	mem 20675MB
[2025-04-02 19:03:05 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][42/234]	eta 0:02:55 lr 0.001216	time 0.8793 (0.9118)	loss 0.5688 (0.5405)	grad_norm 4.4792 (3.5462)	mem 20675MB
[2025-04-02 19:03:07 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][44/234]	eta 0:02:52 lr 0.001215	time 0.8788 (0.9103)	loss 0.6250 (0.5453)	grad_norm 3.8205 (3.6229)	mem 20675MB
[2025-04-02 19:03:09 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][46/234]	eta 0:02:50 lr 0.001215	time 0.8786 (0.9090)	loss 0.4693 (0.5398)	grad_norm 2.8263 (3.5937)	mem 20675MB
[2025-04-02 19:03:10 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][48/234]	eta 0:02:48 lr 0.001215	time 0.8776 (0.9078)	loss 0.5138 (0.5392)	grad_norm 2.1941 (3.5539)	mem 20675MB
[2025-04-02 19:03:12 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][50/234]	eta 0:02:46 lr 0.001215	time 0.8783 (0.9067)	loss 0.4094 (0.5379)	grad_norm 3.0057 (3.5340)	mem 20675MB
[2025-04-02 19:03:14 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][52/234]	eta 0:02:44 lr 0.001215	time 0.8796 (0.9057)	loss 0.3849 (0.5330)	grad_norm 3.6786 (3.5299)	mem 20675MB
[2025-04-02 19:03:16 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][54/234]	eta 0:02:42 lr 0.001215	time 0.8792 (0.9048)	loss 0.4919 (0.5326)	grad_norm 2.2014 (3.4972)	mem 20675MB
[2025-04-02 19:03:17 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][56/234]	eta 0:02:40 lr 0.001214	time 0.8780 (0.9038)	loss 0.5806 (0.5352)	grad_norm 3.4073 (3.5040)	mem 20675MB
[2025-04-02 19:03:19 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][58/234]	eta 0:02:38 lr 0.001214	time 0.8780 (0.9030)	loss 0.5701 (0.5365)	grad_norm 2.5052 (3.4760)	mem 20675MB
[2025-04-02 19:03:21 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][60/234]	eta 0:02:36 lr 0.001214	time 0.8780 (0.9022)	loss 0.5568 (0.5374)	grad_norm 6.8129 (3.5047)	mem 20675MB
[2025-04-02 19:03:23 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][62/234]	eta 0:02:35 lr 0.001214	time 0.8771 (0.9015)	loss 0.5737 (0.5377)	grad_norm 3.3486 (3.4779)	mem 20675MB
[2025-04-02 19:03:24 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][64/234]	eta 0:02:33 lr 0.001214	time 0.8768 (0.9008)	loss 0.4702 (0.5376)	grad_norm 4.2567 (3.4829)	mem 20675MB
[2025-04-02 19:03:26 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][66/234]	eta 0:02:31 lr 0.001213	time 0.8779 (0.9001)	loss 0.5305 (0.5378)	grad_norm 2.5641 (3.4554)	mem 20675MB
[2025-04-02 19:03:28 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][68/234]	eta 0:02:29 lr 0.001213	time 0.8784 (0.8995)	loss 0.5956 (0.5388)	grad_norm 4.8134 (3.4809)	mem 20675MB
[2025-04-02 19:03:30 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][70/234]	eta 0:02:27 lr 0.001213	time 0.8787 (0.8989)	loss 0.6354 (0.5403)	grad_norm 6.0648 (3.5047)	mem 20675MB
[2025-04-02 19:03:32 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][72/234]	eta 0:02:25 lr 0.001213	time 0.8771 (0.8983)	loss 0.4383 (0.5397)	grad_norm 2.7724 (3.5028)	mem 20675MB
[2025-04-02 19:03:33 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][74/234]	eta 0:02:23 lr 0.001213	time 0.8775 (0.8978)	loss 0.4913 (0.5388)	grad_norm 3.9757 (3.5040)	mem 20675MB
[2025-04-02 19:03:35 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][76/234]	eta 0:02:21 lr 0.001213	time 0.8783 (0.8973)	loss 0.4720 (0.5389)	grad_norm 3.9203 (3.5247)	mem 20675MB
[2025-04-02 19:03:37 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][78/234]	eta 0:02:19 lr 0.001212	time 0.8773 (0.8968)	loss 0.5611 (0.5400)	grad_norm 1.3694 (3.5020)	mem 20675MB
[2025-04-02 19:03:39 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][80/234]	eta 0:02:18 lr 0.001212	time 0.8777 (0.8964)	loss 0.5687 (0.5400)	grad_norm 2.5184 (3.4900)	mem 20675MB
[2025-04-02 19:03:40 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][82/234]	eta 0:02:16 lr 0.001212	time 0.8777 (0.8960)	loss 0.4305 (0.5369)	grad_norm 4.1257 (3.5000)	mem 20675MB
[2025-04-02 19:03:42 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][84/234]	eta 0:02:14 lr 0.001212	time 0.8785 (0.8956)	loss 0.5668 (0.5358)	grad_norm 2.9887 (3.4882)	mem 20675MB
[2025-04-02 19:03:44 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][86/234]	eta 0:02:12 lr 0.001212	time 0.8792 (0.8952)	loss 0.5540 (0.5369)	grad_norm 4.3788 (3.4999)	mem 20675MB
[2025-04-02 19:03:46 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][88/234]	eta 0:02:10 lr 0.001211	time 0.8785 (0.8949)	loss 0.4848 (0.5356)	grad_norm 2.9436 (3.4927)	mem 20675MB
[2025-04-02 19:03:47 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][90/234]	eta 0:02:08 lr 0.001211	time 0.8793 (0.8945)	loss 0.6037 (0.5362)	grad_norm 2.9782 (3.4761)	mem 20675MB
[2025-04-02 19:03:49 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][92/234]	eta 0:02:06 lr 0.001211	time 0.8785 (0.8942)	loss 0.4711 (0.5354)	grad_norm 3.4581 (3.4563)	mem 20675MB
[2025-04-02 19:03:51 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][94/234]	eta 0:02:05 lr 0.001211	time 0.8780 (0.8939)	loss 0.5305 (0.5335)	grad_norm 2.9933 (3.4572)	mem 20675MB
[2025-04-02 19:03:53 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][96/234]	eta 0:02:03 lr 0.001211	time 0.8780 (0.8936)	loss 0.5507 (0.5338)	grad_norm 4.7197 (3.4640)	mem 20675MB
[2025-04-02 19:03:54 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][98/234]	eta 0:02:01 lr 0.001210	time 0.8774 (0.8933)	loss 0.5598 (0.5333)	grad_norm 5.2800 (3.4858)	mem 20675MB
[2025-04-02 19:03:56 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][100/234]	eta 0:01:59 lr 0.001210	time 0.8774 (0.8930)	loss 0.4270 (0.5313)	grad_norm 2.6925 (3.4952)	mem 20675MB
[2025-04-02 19:03:58 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][102/234]	eta 0:01:57 lr 0.001210	time 0.8772 (0.8927)	loss 0.6390 (0.5316)	grad_norm 6.1543 (3.5175)	mem 20675MB
[2025-04-02 19:04:00 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][104/234]	eta 0:01:56 lr 0.001210	time 0.8772 (0.8924)	loss 0.3619 (0.5284)	grad_norm 3.3690 (3.5178)	mem 20675MB
[2025-04-02 19:04:01 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][106/234]	eta 0:01:54 lr 0.001210	time 0.8784 (0.8921)	loss 0.3964 (0.5278)	grad_norm 2.9349 (3.5338)	mem 20675MB
[2025-04-02 19:04:03 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][108/234]	eta 0:01:52 lr 0.001209	time 0.8778 (0.8919)	loss 0.4519 (0.5265)	grad_norm 2.8092 (3.5417)	mem 20675MB
[2025-04-02 19:04:05 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][110/234]	eta 0:01:50 lr 0.001209	time 0.8782 (0.8917)	loss 0.4141 (0.5260)	grad_norm 4.7411 (3.5485)	mem 20675MB
[2025-04-02 19:04:07 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][112/234]	eta 0:01:48 lr 0.001209	time 0.8773 (0.8914)	loss 0.6092 (0.5268)	grad_norm 3.2697 (3.5394)	mem 20675MB
[2025-04-02 19:04:08 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][114/234]	eta 0:01:46 lr 0.001209	time 0.8783 (0.8912)	loss 0.6142 (0.5274)	grad_norm 4.7246 (3.5372)	mem 20675MB
[2025-04-02 19:04:10 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][116/234]	eta 0:01:45 lr 0.001209	time 0.8788 (0.8910)	loss 0.5798 (0.5282)	grad_norm 3.7578 (3.5248)	mem 20675MB
[2025-04-02 19:04:12 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][118/234]	eta 0:01:43 lr 0.001208	time 0.8790 (0.8908)	loss 0.6153 (0.5281)	grad_norm 5.3732 (3.5415)	mem 20675MB
[2025-04-02 19:04:14 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][120/234]	eta 0:01:41 lr 0.001208	time 0.8774 (0.8906)	loss 0.4330 (0.5269)	grad_norm 4.1243 (3.5554)	mem 20675MB
[2025-04-02 19:04:15 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][122/234]	eta 0:01:39 lr 0.001208	time 0.8781 (0.8904)	loss 0.4272 (0.5261)	grad_norm 6.1446 (3.5783)	mem 20675MB
[2025-04-02 19:04:17 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][124/234]	eta 0:01:37 lr 0.001208	time 0.8781 (0.8902)	loss 0.4420 (0.5248)	grad_norm 4.2420 (3.5954)	mem 20675MB
[2025-04-02 19:04:19 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][126/234]	eta 0:01:36 lr 0.001208	time 0.8775 (0.8901)	loss 0.5652 (0.5259)	grad_norm 6.3863 (3.6255)	mem 20675MB
[2025-04-02 19:04:21 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][128/234]	eta 0:01:34 lr 0.001207	time 0.8767 (0.8899)	loss 0.4175 (0.5251)	grad_norm 3.2310 (3.6223)	mem 20675MB
[2025-04-02 19:04:22 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][130/234]	eta 0:01:32 lr 0.001207	time 0.8770 (0.8897)	loss 0.5398 (0.5252)	grad_norm 5.5291 (3.6359)	mem 20675MB
[2025-04-02 19:04:24 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][132/234]	eta 0:01:30 lr 0.001207	time 0.8765 (0.8895)	loss 0.4715 (0.5245)	grad_norm 2.7691 (3.6230)	mem 20675MB
[2025-04-02 19:04:26 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][134/234]	eta 0:01:28 lr 0.001207	time 0.8783 (0.8893)	loss 0.4345 (0.5244)	grad_norm 3.2298 (3.6099)	mem 20675MB
[2025-04-02 19:04:28 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][136/234]	eta 0:01:27 lr 0.001207	time 0.8783 (0.8892)	loss 0.5282 (0.5248)	grad_norm 3.3902 (3.5930)	mem 20675MB
[2025-04-02 19:04:30 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][138/234]	eta 0:01:25 lr 0.001206	time 0.8772 (0.8890)	loss 0.5554 (0.5246)	grad_norm 3.0738 (3.5897)	mem 20675MB
[2025-04-02 19:04:31 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][140/234]	eta 0:01:23 lr 0.001206	time 0.8777 (0.8889)	loss 0.5803 (0.5245)	grad_norm 4.2005 (3.5999)	mem 20675MB
[2025-04-02 19:04:33 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][142/234]	eta 0:01:21 lr 0.001206	time 0.8774 (0.8887)	loss 0.3798 (0.5225)	grad_norm 3.6545 (3.6261)	mem 20675MB
[2025-04-02 19:04:35 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][144/234]	eta 0:01:19 lr 0.001206	time 0.8782 (0.8886)	loss 0.5525 (0.5239)	grad_norm 2.6417 (3.6295)	mem 20675MB
[2025-04-02 19:04:37 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][146/234]	eta 0:01:18 lr 0.001206	time 0.8768 (0.8885)	loss 0.5488 (0.5233)	grad_norm 2.2119 (3.6328)	mem 20675MB
[2025-04-02 19:04:38 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][148/234]	eta 0:01:16 lr 0.001205	time 0.8784 (0.8884)	loss 0.5704 (0.5243)	grad_norm 5.1087 (3.6451)	mem 20675MB
[2025-04-02 19:04:40 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][150/234]	eta 0:01:14 lr 0.001205	time 0.8782 (0.8882)	loss 0.4679 (0.5235)	grad_norm 2.5679 (3.6308)	mem 20675MB
[2025-04-02 19:04:42 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][152/234]	eta 0:01:12 lr 0.001205	time 0.8780 (0.8881)	loss 0.5978 (0.5243)	grad_norm 3.7500 (3.6216)	mem 20675MB
[2025-04-02 19:04:44 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][154/234]	eta 0:01:11 lr 0.001205	time 0.8776 (0.8880)	loss 0.4828 (0.5245)	grad_norm 2.6445 (3.6333)	mem 20675MB
[2025-04-02 19:04:45 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][156/234]	eta 0:01:09 lr 0.001204	time 0.8772 (0.8879)	loss 0.5519 (0.5240)	grad_norm 5.8623 (3.6400)	mem 20675MB
[2025-04-02 19:04:47 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][158/234]	eta 0:01:07 lr 0.001204	time 0.8775 (0.8878)	loss 0.5430 (0.5239)	grad_norm 1.7822 (3.6381)	mem 20675MB
[2025-04-02 19:04:49 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][160/234]	eta 0:01:05 lr 0.001204	time 0.8781 (0.8876)	loss 0.3620 (0.5228)	grad_norm 4.3496 (3.6349)	mem 20675MB
[2025-04-02 19:04:51 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][162/234]	eta 0:01:03 lr 0.001204	time 0.8782 (0.8876)	loss 0.5584 (0.5230)	grad_norm 4.8996 (3.6405)	mem 20675MB
[2025-04-02 19:04:52 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][164/234]	eta 0:01:02 lr 0.001204	time 0.8808 (0.8875)	loss 0.5159 (0.5232)	grad_norm 5.1951 (3.6494)	mem 20675MB
[2025-04-02 19:04:54 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][166/234]	eta 0:01:00 lr 0.001203	time 0.8771 (0.8874)	loss 0.5723 (0.5238)	grad_norm 6.0502 (3.6612)	mem 20675MB
[2025-04-02 19:04:56 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][168/234]	eta 0:00:58 lr 0.001203	time 0.8773 (0.8873)	loss 0.5573 (0.5241)	grad_norm 2.3672 (3.6461)	mem 20675MB
[2025-04-02 19:04:58 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][170/234]	eta 0:00:56 lr 0.001203	time 0.8780 (0.8872)	loss 0.5455 (0.5237)	grad_norm 3.0415 (3.6376)	mem 20675MB
[2025-04-02 19:04:59 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][172/234]	eta 0:00:54 lr 0.001203	time 0.8775 (0.8871)	loss 0.5437 (0.5238)	grad_norm 2.4791 (3.6199)	mem 20675MB
[2025-04-02 19:05:01 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][174/234]	eta 0:00:53 lr 0.001203	time 0.8787 (0.8870)	loss 0.4237 (0.5232)	grad_norm 4.4875 (3.6174)	mem 20675MB
[2025-04-02 19:05:03 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][176/234]	eta 0:00:51 lr 0.001202	time 0.8786 (0.8869)	loss 0.5310 (0.5232)	grad_norm 4.2914 (3.6197)	mem 20675MB
[2025-04-02 19:05:05 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][178/234]	eta 0:00:49 lr 0.001202	time 0.8778 (0.8868)	loss 0.5711 (0.5228)	grad_norm 2.9867 (3.6192)	mem 20675MB
[2025-04-02 19:05:06 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][180/234]	eta 0:00:47 lr 0.001202	time 0.8777 (0.8867)	loss 0.5169 (0.5221)	grad_norm 3.6339 (3.6197)	mem 20675MB
[2025-04-02 19:05:08 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][182/234]	eta 0:00:46 lr 0.001202	time 0.8786 (0.8866)	loss 0.4656 (0.5225)	grad_norm 5.4578 (3.6404)	mem 20675MB
[2025-04-02 19:05:10 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][184/234]	eta 0:00:44 lr 0.001202	time 0.8780 (0.8865)	loss 0.5350 (0.5230)	grad_norm 5.8663 (3.6535)	mem 20675MB
[2025-04-02 19:05:12 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][186/234]	eta 0:00:42 lr 0.001201	time 0.8779 (0.8865)	loss 0.5776 (0.5237)	grad_norm 6.9525 (3.6855)	mem 20675MB
[2025-04-02 19:05:13 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][188/234]	eta 0:00:40 lr 0.001201	time 0.8778 (0.8864)	loss 0.4392 (0.5228)	grad_norm 2.8429 (3.6881)	mem 20675MB
[2025-04-02 19:05:15 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][190/234]	eta 0:00:38 lr 0.001201	time 0.8786 (0.8863)	loss 0.5619 (0.5233)	grad_norm 5.9418 (3.6941)	mem 20675MB
[2025-04-02 19:05:17 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][192/234]	eta 0:00:37 lr 0.001201	time 0.8789 (0.8862)	loss 0.6099 (0.5242)	grad_norm 4.3481 (3.7056)	mem 20675MB
[2025-04-02 19:05:19 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][194/234]	eta 0:00:35 lr 0.001200	time 0.8776 (0.8861)	loss 0.4928 (0.5241)	grad_norm 2.5768 (3.6971)	mem 20675MB
[2025-04-02 19:05:21 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][196/234]	eta 0:00:33 lr 0.001200	time 0.8772 (0.8861)	loss 0.4337 (0.5232)	grad_norm 3.8242 (3.7015)	mem 20675MB
[2025-04-02 19:05:22 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][198/234]	eta 0:00:31 lr 0.001200	time 0.8780 (0.8860)	loss 0.5457 (0.5243)	grad_norm 1.8425 (3.7111)	mem 20675MB
[2025-04-02 19:05:24 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][200/234]	eta 0:00:30 lr 0.001200	time 0.8776 (0.8859)	loss 0.5562 (0.5250)	grad_norm 4.1458 (3.7168)	mem 20675MB
[2025-04-02 19:05:26 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][202/234]	eta 0:00:28 lr 0.001200	time 0.8779 (0.8859)	loss 0.5441 (0.5248)	grad_norm 1.8117 (3.7045)	mem 20675MB
[2025-04-02 19:05:28 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][204/234]	eta 0:00:26 lr 0.001199	time 0.8772 (0.8858)	loss 0.4352 (0.5247)	grad_norm 3.1995 (3.6920)	mem 20675MB
[2025-04-02 19:05:29 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][206/234]	eta 0:00:24 lr 0.001199	time 0.8784 (0.8857)	loss 0.6162 (0.5250)	grad_norm 5.0402 (3.7020)	mem 20675MB
[2025-04-02 19:05:31 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][208/234]	eta 0:00:23 lr 0.001199	time 0.8774 (0.8856)	loss 0.4723 (0.5251)	grad_norm 3.7656 (3.7008)	mem 20675MB
[2025-04-02 19:05:33 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][210/234]	eta 0:00:21 lr 0.001199	time 0.8772 (0.8856)	loss 0.5934 (0.5249)	grad_norm 3.0387 (3.7041)	mem 20675MB
[2025-04-02 19:05:35 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][212/234]	eta 0:00:19 lr 0.001198	time 0.8779 (0.8855)	loss 0.5630 (0.5247)	grad_norm 2.4237 (3.6959)	mem 20675MB
[2025-04-02 19:05:36 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][214/234]	eta 0:00:17 lr 0.001198	time 0.8768 (0.8854)	loss 0.5132 (0.5247)	grad_norm 2.7112 (3.6914)	mem 20675MB
[2025-04-02 19:05:38 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][216/234]	eta 0:00:15 lr 0.001198	time 0.8771 (0.8854)	loss 0.5146 (0.5245)	grad_norm 2.9062 (3.6816)	mem 20675MB
[2025-04-02 19:05:40 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][218/234]	eta 0:00:14 lr 0.001198	time 0.8779 (0.8853)	loss 0.5630 (0.5253)	grad_norm 2.3170 (3.6710)	mem 20675MB
[2025-04-02 19:05:42 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][220/234]	eta 0:00:12 lr 0.001198	time 0.8786 (0.8853)	loss 0.5505 (0.5256)	grad_norm 4.0030 (3.6691)	mem 20675MB
[2025-04-02 19:05:43 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][222/234]	eta 0:00:10 lr 0.001197	time 0.8772 (0.8852)	loss 0.5239 (0.5259)	grad_norm 2.2660 (3.6594)	mem 20675MB
[2025-04-02 19:05:45 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][224/234]	eta 0:00:08 lr 0.001197	time 0.8778 (0.8851)	loss 0.5175 (0.5259)	grad_norm 3.8623 (3.6510)	mem 20675MB
[2025-04-02 19:05:47 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][226/234]	eta 0:00:07 lr 0.001197	time 0.8770 (0.8851)	loss 0.6483 (0.5266)	grad_norm 5.2625 (3.6616)	mem 20675MB
[2025-04-02 19:05:49 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][228/234]	eta 0:00:05 lr 0.001197	time 0.8774 (0.8850)	loss 0.5551 (0.5268)	grad_norm 4.0401 (3.6586)	mem 20675MB
[2025-04-02 19:05:50 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][230/234]	eta 0:00:03 lr 0.001196	time 0.8776 (0.8849)	loss 0.5121 (0.5269)	grad_norm 5.1820 (3.6763)	mem 20675MB
[2025-04-02 19:05:52 simmim_finetune] (main_finetune.py 252): INFO Train: [3/30][232/234]	eta 0:00:01 lr 0.001196	time 0.8772 (0.8849)	loss 0.4264 (0.5263)	grad_norm 3.0560 (3.6709)	mem 20675MB
[2025-04-02 19:05:53 simmim_finetune] (main_finetune.py 260): INFO EPOCH 3 training takes 0:03:27
[2025-04-02 19:05:54 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.038 (1.038)	Loss 1.3826 (1.3826)	Acc@1 30.469 (30.469)	Mem 20675MB
[2025-04-02 19:05:54 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 50.829
[2025-04-02 19:05:54 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 50.8%
[2025-04-02 19:05:54 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 53.59%
[2025-04-02 19:05:54 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [4.4329366867865275e-06, 4.4329366867865275e-06, 6.814095784705118e-06, 6.814095784705118e-06, 1.0477417473810641e-05, 1.0477417473810641e-05, 1.611329699551145e-05, 1.611329699551145e-05, 2.478388087505115e-05, 2.478388087505115e-05, 3.8123240689727617e-05, 3.8123240689727617e-05, 5.864533271230679e-05, 5.864533271230679e-05, 9.02177819778132e-05, 9.02177819778132e-05, 0.00013879078084782308, 0.00013879078084782308, 0.00021351847141706906, 0.00021351847141706906, 0.0003284841492159089, 0.0003284841492159089, 0.0005053544227525858, 0.0005053544227525858, 0.0007774625358859347, 0.0007774625358859347, 0.001196090402244933, 0.001196090402244933]
[2025-04-02 19:05:57 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][0/234]	eta 0:08:21 lr 0.001196	time 2.1419 (2.1419)	loss 0.5840 (0.5840)	grad_norm 3.1020 (3.1020)	mem 20675MB
[2025-04-02 19:05:58 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][2/234]	eta 0:05:01 lr 0.001196	time 0.8779 (1.2994)	loss 0.5532 (0.5364)	grad_norm 3.0890 (3.3153)	mem 20675MB
[2025-04-02 19:06:00 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][4/234]	eta 0:04:20 lr 0.001196	time 0.8779 (1.1314)	loss 0.5163 (0.5233)	grad_norm 2.4648 (3.2641)	mem 20675MB
[2025-04-02 19:06:02 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][6/234]	eta 0:04:01 lr 0.001195	time 0.8768 (1.0588)	loss 0.3923 (0.4874)	grad_norm 2.5373 (3.1907)	mem 20675MB
[2025-04-02 19:06:04 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][8/234]	eta 0:03:50 lr 0.001195	time 0.8772 (1.0185)	loss 0.5721 (0.4967)	grad_norm 3.6138 (3.4538)	mem 20675MB
[2025-04-02 19:06:05 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][10/234]	eta 0:03:42 lr 0.001195	time 0.8781 (0.9930)	loss 0.3725 (0.4890)	grad_norm 2.3570 (3.2286)	mem 20675MB
[2025-04-02 19:06:07 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][12/234]	eta 0:03:36 lr 0.001195	time 0.8775 (0.9754)	loss 0.3822 (0.4835)	grad_norm 4.3881 (3.2576)	mem 20675MB
[2025-04-02 19:06:09 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][14/234]	eta 0:03:31 lr 0.001194	time 0.8771 (0.9624)	loss 0.4152 (0.4768)	grad_norm 3.2807 (3.3600)	mem 20675MB
[2025-04-02 19:06:11 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][16/234]	eta 0:03:27 lr 0.001194	time 0.8784 (0.9526)	loss 0.5112 (0.4839)	grad_norm 3.3355 (3.2865)	mem 20675MB
[2025-04-02 19:06:12 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][18/234]	eta 0:03:24 lr 0.001194	time 0.8764 (0.9447)	loss 0.5814 (0.4855)	grad_norm 2.7659 (3.3138)	mem 20675MB
[2025-04-02 19:06:14 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][20/234]	eta 0:03:20 lr 0.001194	time 0.8764 (0.9383)	loss 0.4911 (0.4877)	grad_norm 3.2188 (3.3359)	mem 20675MB
[2025-04-02 19:06:16 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][22/234]	eta 0:03:17 lr 0.001193	time 0.8771 (0.9330)	loss 0.3903 (0.4842)	grad_norm 5.0725 (3.4129)	mem 20675MB
[2025-04-02 19:06:18 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][24/234]	eta 0:03:14 lr 0.001193	time 0.8767 (0.9286)	loss 0.5636 (0.4916)	grad_norm 2.4204 (3.4385)	mem 20675MB
[2025-04-02 19:06:19 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][26/234]	eta 0:03:12 lr 0.001193	time 0.8764 (0.9248)	loss 0.5798 (0.4962)	grad_norm 3.3978 (3.5056)	mem 20675MB
[2025-04-02 19:06:21 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][28/234]	eta 0:03:09 lr 0.001193	time 0.8770 (0.9215)	loss 0.4798 (0.4968)	grad_norm 2.0561 (3.4987)	mem 20675MB
[2025-04-02 19:06:23 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][30/234]	eta 0:03:07 lr 0.001193	time 0.8773 (0.9187)	loss 0.5846 (0.5033)	grad_norm 5.2166 (3.5462)	mem 20675MB
[2025-04-02 19:06:25 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][32/234]	eta 0:03:05 lr 0.001192	time 0.8765 (0.9162)	loss 0.5306 (0.5065)	grad_norm 1.7858 (3.5358)	mem 20675MB
[2025-04-02 19:06:26 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][34/234]	eta 0:03:02 lr 0.001192	time 0.8764 (0.9140)	loss 0.5315 (0.5077)	grad_norm 2.5182 (3.4633)	mem 20675MB
[2025-04-02 19:06:28 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][36/234]	eta 0:03:00 lr 0.001192	time 0.8777 (0.9121)	loss 0.5982 (0.5138)	grad_norm 4.8937 (3.5294)	mem 20675MB
[2025-04-02 19:06:30 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][38/234]	eta 0:02:58 lr 0.001192	time 0.8777 (0.9103)	loss 0.5537 (0.5148)	grad_norm 1.6615 (3.4371)	mem 20675MB
[2025-04-02 19:06:32 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][40/234]	eta 0:02:56 lr 0.001191	time 0.8770 (0.9087)	loss 0.4224 (0.5121)	grad_norm 3.7441 (3.3977)	mem 20675MB
[2025-04-02 19:06:33 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][42/234]	eta 0:02:54 lr 0.001191	time 0.8771 (0.9073)	loss 0.4835 (0.5086)	grad_norm 3.5719 (3.4127)	mem 20675MB
[2025-04-02 19:06:35 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][44/234]	eta 0:02:52 lr 0.001191	time 0.8771 (0.9060)	loss 0.5611 (0.5138)	grad_norm 2.8908 (3.4404)	mem 20675MB
[2025-04-02 19:06:37 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][46/234]	eta 0:02:50 lr 0.001191	time 0.8777 (0.9048)	loss 0.4054 (0.5135)	grad_norm 4.0489 (3.4830)	mem 20675MB
[2025-04-02 19:06:39 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][48/234]	eta 0:02:48 lr 0.001190	time 0.8768 (0.9037)	loss 0.5673 (0.5150)	grad_norm 2.2170 (3.4304)	mem 20675MB
[2025-04-02 19:06:40 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][50/234]	eta 0:02:46 lr 0.001190	time 0.8772 (0.9027)	loss 0.6845 (0.5163)	grad_norm 3.6102 (3.4296)	mem 20675MB
[2025-04-02 19:06:42 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][52/234]	eta 0:02:44 lr 0.001190	time 0.8767 (0.9017)	loss 0.5657 (0.5154)	grad_norm 3.3828 (3.4549)	mem 20675MB
[2025-04-02 19:06:44 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][54/234]	eta 0:02:42 lr 0.001190	time 0.8768 (0.9009)	loss 0.5739 (0.5174)	grad_norm 2.0243 (3.4172)	mem 20675MB
[2025-04-02 19:06:46 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][56/234]	eta 0:02:40 lr 0.001189	time 0.8792 (0.9001)	loss 0.4705 (0.5173)	grad_norm 3.7302 (3.3937)	mem 20675MB
[2025-04-02 19:06:47 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][58/234]	eta 0:02:38 lr 0.001189	time 0.8778 (0.8993)	loss 0.5325 (0.5168)	grad_norm 2.8909 (3.3638)	mem 20675MB
[2025-04-02 19:06:49 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][60/234]	eta 0:02:36 lr 0.001189	time 0.8777 (0.8987)	loss 0.4133 (0.5147)	grad_norm 4.4351 (3.3664)	mem 20675MB
[2025-04-02 19:06:51 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][62/234]	eta 0:02:34 lr 0.001189	time 0.8789 (0.8981)	loss 0.5104 (0.5149)	grad_norm 3.1996 (3.3424)	mem 20675MB
[2025-04-02 19:06:53 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][64/234]	eta 0:02:32 lr 0.001188	time 0.8773 (0.8975)	loss 0.5041 (0.5143)	grad_norm 5.1183 (3.3689)	mem 20675MB
[2025-04-02 19:06:54 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][66/234]	eta 0:02:30 lr 0.001188	time 0.8786 (0.8969)	loss 0.5239 (0.5141)	grad_norm 3.0736 (3.3606)	mem 20675MB
[2025-04-02 19:06:56 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][68/234]	eta 0:02:28 lr 0.001188	time 0.8771 (0.8963)	loss 0.4700 (0.5135)	grad_norm 6.2631 (3.4074)	mem 20675MB
[2025-04-02 19:06:58 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][70/234]	eta 0:02:26 lr 0.001188	time 0.8771 (0.8958)	loss 0.5580 (0.5136)	grad_norm 3.0462 (3.3931)	mem 20675MB
[2025-04-02 19:07:00 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][72/234]	eta 0:02:25 lr 0.001187	time 0.8781 (0.8954)	loss 0.5764 (0.5158)	grad_norm 4.5860 (3.4010)	mem 20675MB
[2025-04-02 19:07:01 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][74/234]	eta 0:02:23 lr 0.001187	time 0.8769 (0.8949)	loss 0.5716 (0.5174)	grad_norm 4.9014 (3.4118)	mem 20675MB
[2025-04-02 19:07:03 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][76/234]	eta 0:02:21 lr 0.001187	time 0.8778 (0.8945)	loss 0.5534 (0.5182)	grad_norm 1.7411 (3.3787)	mem 20675MB
[2025-04-02 19:07:05 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][78/234]	eta 0:02:19 lr 0.001187	time 0.8786 (0.8941)	loss 0.5319 (0.5187)	grad_norm 2.6596 (3.3512)	mem 20675MB
[2025-04-02 19:07:07 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][80/234]	eta 0:02:17 lr 0.001187	time 0.8785 (0.8937)	loss 0.4287 (0.5166)	grad_norm 3.6088 (3.3518)	mem 20675MB
[2025-04-02 19:07:09 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][82/234]	eta 0:02:15 lr 0.001186	time 0.8771 (0.8934)	loss 0.4858 (0.5154)	grad_norm 4.2004 (3.3718)	mem 20675MB
[2025-04-02 19:07:10 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][84/234]	eta 0:02:13 lr 0.001186	time 0.8779 (0.8930)	loss 0.5685 (0.5179)	grad_norm 3.0455 (3.3748)	mem 20675MB
[2025-04-02 19:07:12 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][86/234]	eta 0:02:12 lr 0.001186	time 0.8778 (0.8927)	loss 0.3430 (0.5160)	grad_norm 4.5677 (3.3826)	mem 20675MB
[2025-04-02 19:07:14 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][88/234]	eta 0:02:10 lr 0.001186	time 0.8777 (0.8924)	loss 0.3723 (0.5136)	grad_norm 2.5696 (3.3855)	mem 20675MB
[2025-04-02 19:07:16 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][90/234]	eta 0:02:08 lr 0.001185	time 0.8783 (0.8921)	loss 0.5290 (0.5135)	grad_norm 2.6277 (3.3675)	mem 20675MB
[2025-04-02 19:07:17 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][92/234]	eta 0:02:06 lr 0.001185	time 0.8785 (0.8918)	loss 0.4534 (0.5118)	grad_norm 2.6924 (3.3499)	mem 20675MB
[2025-04-02 19:07:19 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][94/234]	eta 0:02:04 lr 0.001185	time 0.8777 (0.8915)	loss 0.5599 (0.5116)	grad_norm 2.8744 (3.3441)	mem 20675MB
[2025-04-02 19:07:21 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][96/234]	eta 0:02:02 lr 0.001185	time 0.8778 (0.8912)	loss 0.4050 (0.5105)	grad_norm 2.9898 (3.3372)	mem 20675MB
[2025-04-02 19:07:23 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][98/234]	eta 0:02:01 lr 0.001184	time 0.8771 (0.8910)	loss 0.5369 (0.5109)	grad_norm 2.0467 (3.3143)	mem 20675MB
[2025-04-02 19:07:24 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][100/234]	eta 0:01:59 lr 0.001184	time 0.8778 (0.8907)	loss 0.5293 (0.5118)	grad_norm 2.7366 (3.3006)	mem 20675MB
[2025-04-02 19:07:26 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][102/234]	eta 0:01:57 lr 0.001184	time 0.8765 (0.8905)	loss 0.5040 (0.5124)	grad_norm 2.5572 (3.2859)	mem 20675MB
[2025-04-02 19:07:28 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][104/234]	eta 0:01:55 lr 0.001184	time 0.8775 (0.8903)	loss 0.5760 (0.5127)	grad_norm 1.9539 (3.2621)	mem 20675MB
[2025-04-02 19:07:30 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][106/234]	eta 0:01:53 lr 0.001183	time 0.8784 (0.8900)	loss 0.5252 (0.5133)	grad_norm 2.1152 (3.2460)	mem 20675MB
[2025-04-02 19:07:31 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][108/234]	eta 0:01:52 lr 0.001183	time 0.8785 (0.8898)	loss 0.5077 (0.5120)	grad_norm 2.2105 (3.2316)	mem 20675MB
[2025-04-02 19:07:33 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][110/234]	eta 0:01:50 lr 0.001183	time 0.8798 (0.8897)	loss 0.4887 (0.5118)	grad_norm 3.5991 (3.2547)	mem 20675MB
[2025-04-02 19:07:35 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][112/234]	eta 0:01:48 lr 0.001183	time 0.8792 (0.8895)	loss 0.5806 (0.5114)	grad_norm 3.8367 (3.2474)	mem 20675MB
[2025-04-02 19:07:37 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][114/234]	eta 0:01:46 lr 0.001182	time 0.8786 (0.8893)	loss 0.4134 (0.5116)	grad_norm 3.1640 (3.3021)	mem 20675MB
[2025-04-02 19:07:38 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][116/234]	eta 0:01:44 lr 0.001182	time 0.8772 (0.8891)	loss 0.5238 (0.5120)	grad_norm 2.0692 (3.2859)	mem 20675MB
[2025-04-02 19:07:40 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][118/234]	eta 0:01:43 lr 0.001182	time 0.8784 (0.8889)	loss 0.6130 (0.5134)	grad_norm 7.0960 (3.3346)	mem 20675MB
[2025-04-02 19:07:42 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][120/234]	eta 0:01:41 lr 0.001182	time 0.8795 (0.8888)	loss 0.5464 (0.5138)	grad_norm 1.5233 (3.3318)	mem 20675MB
[2025-04-02 19:07:44 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][122/234]	eta 0:01:39 lr 0.001181	time 0.8780 (0.8886)	loss 0.5557 (0.5153)	grad_norm 3.3537 (3.3410)	mem 20675MB
[2025-04-02 19:07:45 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][124/234]	eta 0:01:37 lr 0.001181	time 0.8788 (0.8885)	loss 0.4987 (0.5154)	grad_norm 4.9243 (3.3603)	mem 20675MB
[2025-04-02 19:07:47 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][126/234]	eta 0:01:35 lr 0.001181	time 0.8785 (0.8883)	loss 0.5125 (0.5158)	grad_norm 3.0371 (3.3419)	mem 20675MB
[2025-04-02 19:07:49 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][128/234]	eta 0:01:34 lr 0.001180	time 0.8775 (0.8882)	loss 0.6256 (0.5171)	grad_norm 4.9638 (3.3460)	mem 20675MB
[2025-04-02 19:07:51 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][130/234]	eta 0:01:32 lr 0.001180	time 0.8784 (0.8880)	loss 0.4131 (0.5155)	grad_norm 2.4589 (3.3542)	mem 20675MB
[2025-04-02 19:07:52 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][132/234]	eta 0:01:30 lr 0.001180	time 0.8786 (0.8879)	loss 0.3058 (0.5146)	grad_norm 3.5118 (3.3655)	mem 20675MB
[2025-04-02 19:07:54 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][134/234]	eta 0:01:28 lr 0.001180	time 0.8780 (0.8878)	loss 0.5551 (0.5143)	grad_norm 2.9739 (3.3681)	mem 20675MB
[2025-04-02 19:07:56 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][136/234]	eta 0:01:26 lr 0.001179	time 0.8776 (0.8876)	loss 0.5116 (0.5131)	grad_norm 4.7119 (3.3852)	mem 20675MB
[2025-04-02 19:07:58 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][138/234]	eta 0:01:25 lr 0.001179	time 0.8775 (0.8875)	loss 0.5109 (0.5137)	grad_norm 4.4740 (3.3941)	mem 20675MB
[2025-04-02 19:07:59 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][140/234]	eta 0:01:23 lr 0.001179	time 0.8769 (0.8874)	loss 0.5734 (0.5136)	grad_norm 4.1973 (3.4037)	mem 20675MB
[2025-04-02 19:08:01 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][142/234]	eta 0:01:21 lr 0.001179	time 0.8787 (0.8872)	loss 0.5519 (0.5144)	grad_norm 1.7244 (3.3932)	mem 20675MB
[2025-04-02 19:08:03 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][144/234]	eta 0:01:19 lr 0.001178	time 0.8779 (0.8871)	loss 0.4198 (0.5140)	grad_norm 4.0258 (3.3917)	mem 20675MB
[2025-04-02 19:08:05 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][146/234]	eta 0:01:18 lr 0.001178	time 0.8774 (0.8870)	loss 0.5612 (0.5149)	grad_norm 1.8264 (3.3671)	mem 20675MB
[2025-04-02 19:08:07 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][148/234]	eta 0:01:16 lr 0.001178	time 0.8779 (0.8869)	loss 0.4158 (0.5142)	grad_norm 3.1018 (3.3636)	mem 20675MB
[2025-04-02 19:08:08 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][150/234]	eta 0:01:14 lr 0.001178	time 0.8767 (0.8868)	loss 0.4981 (0.5138)	grad_norm 2.0560 (3.3445)	mem 20675MB
[2025-04-02 19:08:10 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][152/234]	eta 0:01:12 lr 0.001177	time 0.8780 (0.8867)	loss 0.5236 (0.5126)	grad_norm 5.2048 (3.3555)	mem 20675MB
[2025-04-02 19:08:12 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][154/234]	eta 0:01:10 lr 0.001177	time 0.8777 (0.8866)	loss 0.5383 (0.5133)	grad_norm 2.7827 (3.3571)	mem 20675MB
[2025-04-02 19:08:14 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][156/234]	eta 0:01:09 lr 0.001177	time 0.8763 (0.8864)	loss 0.4991 (0.5126)	grad_norm 2.7104 (3.3489)	mem 20675MB
[2025-04-02 19:08:15 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][158/234]	eta 0:01:07 lr 0.001177	time 0.8767 (0.8863)	loss 0.4517 (0.5115)	grad_norm 4.2455 (3.3515)	mem 20675MB
[2025-04-02 19:08:17 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][160/234]	eta 0:01:05 lr 0.001176	time 0.8781 (0.8862)	loss 0.6018 (0.5127)	grad_norm 5.1634 (3.3657)	mem 20675MB
[2025-04-02 19:08:19 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][162/234]	eta 0:01:03 lr 0.001176	time 0.8788 (0.8862)	loss 0.3602 (0.5114)	grad_norm 3.1141 (3.3588)	mem 20675MB
[2025-04-02 19:08:21 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][164/234]	eta 0:01:02 lr 0.001176	time 0.8777 (0.8861)	loss 0.5199 (0.5113)	grad_norm 3.1084 (3.3485)	mem 20675MB
[2025-04-02 19:08:22 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][166/234]	eta 0:01:00 lr 0.001176	time 0.8779 (0.8860)	loss 0.5773 (0.5115)	grad_norm 2.6862 (3.3378)	mem 20675MB
[2025-04-02 19:08:24 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][168/234]	eta 0:00:58 lr 0.001175	time 0.8781 (0.8859)	loss 0.5103 (0.5116)	grad_norm 2.6441 (3.3252)	mem 20675MB
[2025-04-02 19:08:26 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][170/234]	eta 0:00:56 lr 0.001175	time 0.8781 (0.8858)	loss 0.5606 (0.5117)	grad_norm 2.5858 (3.3171)	mem 20675MB
[2025-04-02 19:08:28 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][172/234]	eta 0:00:54 lr 0.001175	time 0.8780 (0.8857)	loss 0.4196 (0.5110)	grad_norm 3.1081 (3.3138)	mem 20675MB
[2025-04-02 19:08:29 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][174/234]	eta 0:00:53 lr 0.001174	time 0.8776 (0.8856)	loss 0.5859 (0.5116)	grad_norm 3.9850 (3.3131)	mem 20675MB
[2025-04-02 19:08:31 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][176/234]	eta 0:00:51 lr 0.001174	time 0.8784 (0.8856)	loss 0.5181 (0.5122)	grad_norm 2.6819 (3.3199)	mem 20675MB
[2025-04-02 19:08:33 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][178/234]	eta 0:00:49 lr 0.001174	time 0.8771 (0.8855)	loss 0.5794 (0.5128)	grad_norm 5.8223 (3.3371)	mem 20675MB
[2025-04-02 19:08:35 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][180/234]	eta 0:00:47 lr 0.001174	time 0.8767 (0.8854)	loss 0.5917 (0.5132)	grad_norm 4.9208 (3.3413)	mem 20675MB
[2025-04-02 19:08:36 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][182/234]	eta 0:00:46 lr 0.001173	time 0.8783 (0.8853)	loss 0.5440 (0.5137)	grad_norm 1.0183 (3.3263)	mem 20675MB
[2025-04-02 19:08:38 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][184/234]	eta 0:00:44 lr 0.001173	time 0.8781 (0.8852)	loss 0.4612 (0.5135)	grad_norm 2.4555 (3.3314)	mem 20675MB
[2025-04-02 19:08:40 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][186/234]	eta 0:00:42 lr 0.001173	time 0.8773 (0.8852)	loss 0.5438 (0.5136)	grad_norm 5.9395 (3.3449)	mem 20675MB
[2025-04-02 19:08:42 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][188/234]	eta 0:00:40 lr 0.001173	time 0.8777 (0.8851)	loss 0.6209 (0.5138)	grad_norm 4.2901 (3.3621)	mem 20675MB
[2025-04-02 19:08:43 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][190/234]	eta 0:00:38 lr 0.001172	time 0.8766 (0.8850)	loss 0.5405 (0.5145)	grad_norm 6.0464 (3.3847)	mem 20675MB
[2025-04-02 19:08:45 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][192/234]	eta 0:00:37 lr 0.001172	time 0.8782 (0.8849)	loss 0.5254 (0.5143)	grad_norm 3.4883 (3.3877)	mem 20675MB
[2025-04-02 19:08:47 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][194/234]	eta 0:00:35 lr 0.001172	time 0.8784 (0.8849)	loss 0.6074 (0.5152)	grad_norm 3.6595 (3.3991)	mem 20675MB
[2025-04-02 19:08:49 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][196/234]	eta 0:00:33 lr 0.001172	time 0.8773 (0.8848)	loss 0.4211 (0.5141)	grad_norm 2.8559 (3.4006)	mem 20675MB
[2025-04-02 19:08:50 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][198/234]	eta 0:00:31 lr 0.001171	time 0.8768 (0.8847)	loss 0.5050 (0.5137)	grad_norm 4.0618 (3.3992)	mem 20675MB
[2025-04-02 19:08:52 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][200/234]	eta 0:00:30 lr 0.001171	time 0.8780 (0.8847)	loss 0.4837 (0.5138)	grad_norm 4.3977 (3.4134)	mem 20675MB
[2025-04-02 19:08:54 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][202/234]	eta 0:00:28 lr 0.001171	time 0.8772 (0.8846)	loss 0.5716 (0.5141)	grad_norm 6.3122 (3.4359)	mem 20675MB
[2025-04-02 19:08:56 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][204/234]	eta 0:00:26 lr 0.001170	time 0.8765 (0.8846)	loss 0.4371 (0.5136)	grad_norm 3.3732 (3.4429)	mem 20675MB
[2025-04-02 19:08:57 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][206/234]	eta 0:00:24 lr 0.001170	time 0.8767 (0.8845)	loss 0.5413 (0.5141)	grad_norm 5.0544 (3.4512)	mem 20675MB
[2025-04-02 19:08:59 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][208/234]	eta 0:00:22 lr 0.001170	time 0.8769 (0.8844)	loss 0.4530 (0.5140)	grad_norm 2.5052 (3.4440)	mem 20675MB
[2025-04-02 19:09:01 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][210/234]	eta 0:00:21 lr 0.001170	time 0.8771 (0.8844)	loss 0.5670 (0.5142)	grad_norm 1.9689 (3.4375)	mem 20675MB
[2025-04-02 19:09:03 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][212/234]	eta 0:00:19 lr 0.001169	time 0.8773 (0.8843)	loss 0.4981 (0.5143)	grad_norm 4.7463 (3.4401)	mem 20675MB
[2025-04-02 19:09:04 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][214/234]	eta 0:00:17 lr 0.001169	time 0.8780 (0.8843)	loss 0.4867 (0.5138)	grad_norm 2.4231 (3.4405)	mem 20675MB
[2025-04-02 19:09:06 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][216/234]	eta 0:00:15 lr 0.001169	time 0.8785 (0.8842)	loss 0.5250 (0.5138)	grad_norm 4.4529 (3.4403)	mem 20675MB
[2025-04-02 19:09:08 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][218/234]	eta 0:00:14 lr 0.001169	time 0.8773 (0.8842)	loss 0.6004 (0.5138)	grad_norm 6.8320 (3.4578)	mem 20675MB
[2025-04-02 19:09:10 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][220/234]	eta 0:00:12 lr 0.001168	time 0.8778 (0.8841)	loss 0.4942 (0.5139)	grad_norm 3.9025 (3.4581)	mem 20675MB
[2025-04-02 19:09:12 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][222/234]	eta 0:00:10 lr 0.001168	time 0.8779 (0.8841)	loss 0.5560 (0.5133)	grad_norm 5.3579 (3.4630)	mem 20675MB
[2025-04-02 19:09:13 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][224/234]	eta 0:00:08 lr 0.001168	time 0.8764 (0.8840)	loss 0.5998 (0.5131)	grad_norm 3.0621 (3.4632)	mem 20675MB
[2025-04-02 19:09:15 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][226/234]	eta 0:00:07 lr 0.001167	time 0.8763 (0.8840)	loss 0.4059 (0.5123)	grad_norm 3.3427 (3.4596)	mem 20675MB
[2025-04-02 19:09:17 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][228/234]	eta 0:00:05 lr 0.001167	time 0.8774 (0.8839)	loss 0.5176 (0.5119)	grad_norm 1.6714 (3.4624)	mem 20675MB
[2025-04-02 19:09:19 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][230/234]	eta 0:00:03 lr 0.001167	time 0.8761 (0.8839)	loss 0.5119 (0.5121)	grad_norm 3.7401 (3.4638)	mem 20675MB
[2025-04-02 19:09:20 simmim_finetune] (main_finetune.py 252): INFO Train: [4/30][232/234]	eta 0:00:01 lr 0.001167	time 0.8778 (0.8838)	loss 0.5469 (0.5126)	grad_norm 3.3108 (3.4703)	mem 20675MB
[2025-04-02 19:09:21 simmim_finetune] (main_finetune.py 260): INFO EPOCH 4 training takes 0:03:26
[2025-04-02 19:09:22 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.048 (1.048)	Loss 1.1591 (1.1591)	Acc@1 33.594 (33.594)	Mem 20675MB
[2025-04-02 19:09:23 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 53.039
[2025-04-02 19:09:23 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 53.0%
[2025-04-02 19:09:23 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 53.59%
[2025-04-02 19:09:23 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [4.3291607918427586e-06, 4.3291607918427586e-06, 6.651244906109174e-06, 6.651244906109174e-06, 1.0223682004980582e-05, 1.0223682004980582e-05, 1.5719739080167367e-05, 1.5719739080167367e-05, 2.4175211503531643e-05, 2.4175211503531643e-05, 3.718363061639977e-05, 3.718363061639977e-05, 5.719658309773534e-05, 5.719658309773534e-05, 8.798574076132852e-05, 8.798574076132852e-05, 0.00013535367562839495, 0.00013535367562839495, 0.00020822742157772795, 0.00020822742157772795, 0.00032034087688439405, 0.00032034087688439405, 0.0004928231158177266, 0.0004928231158177266, 0.0007581804064843919, 0.0007581804064843919, 0.0011664223921254155, 0.0011664223921254155]
[2025-04-02 19:09:25 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][0/234]	eta 0:08:05 lr 0.001166	time 2.0737 (2.0737)	loss 0.4925 (0.4925)	grad_norm 4.0525 (4.0525)	mem 20675MB
[2025-04-02 19:09:26 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][2/234]	eta 0:04:56 lr 0.001166	time 0.8795 (1.2780)	loss 0.5519 (0.5088)	grad_norm 1.9685 (2.9656)	mem 20675MB
[2025-04-02 19:09:28 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][4/234]	eta 0:04:17 lr 0.001166	time 0.8768 (1.1179)	loss 0.5888 (0.5430)	grad_norm 1.5864 (2.4363)	mem 20675MB
[2025-04-02 19:09:30 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][6/234]	eta 0:03:59 lr 0.001165	time 0.8770 (1.0493)	loss 0.5817 (0.5570)	grad_norm 2.4692 (2.3312)	mem 20675MB
[2025-04-02 19:09:32 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][8/234]	eta 0:03:48 lr 0.001165	time 0.8772 (1.0113)	loss 0.5897 (0.5516)	grad_norm 2.8121 (2.3925)	mem 20675MB
[2025-04-02 19:09:33 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][10/234]	eta 0:03:41 lr 0.001165	time 0.8779 (0.9872)	loss 0.5215 (0.5508)	grad_norm 2.6835 (2.3300)	mem 20675MB
[2025-04-02 19:09:35 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][12/234]	eta 0:03:35 lr 0.001165	time 0.8766 (0.9704)	loss 0.5442 (0.5512)	grad_norm 2.4251 (2.4274)	mem 20675MB
[2025-04-02 19:09:37 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][14/234]	eta 0:03:30 lr 0.001164	time 0.8770 (0.9580)	loss 0.4787 (0.5420)	grad_norm 1.6756 (2.3449)	mem 20675MB
[2025-04-02 19:09:39 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][16/234]	eta 0:03:26 lr 0.001164	time 0.8764 (0.9485)	loss 0.5810 (0.5473)	grad_norm 2.1325 (2.3808)	mem 20675MB
[2025-04-02 19:09:40 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][18/234]	eta 0:03:23 lr 0.001164	time 0.8777 (0.9411)	loss 0.5118 (0.5425)	grad_norm 2.7247 (2.4552)	mem 20675MB
[2025-04-02 19:09:42 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][20/234]	eta 0:03:20 lr 0.001163	time 0.8775 (0.9351)	loss 0.4390 (0.5388)	grad_norm 2.7192 (2.4470)	mem 20675MB
[2025-04-02 19:09:44 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][22/234]	eta 0:03:17 lr 0.001163	time 0.8770 (0.9302)	loss 0.4716 (0.5315)	grad_norm 2.1297 (2.4215)	mem 20675MB
[2025-04-02 19:09:46 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][24/234]	eta 0:03:14 lr 0.001163	time 0.8766 (0.9260)	loss 0.4385 (0.5270)	grad_norm 2.7262 (2.4223)	mem 20675MB
[2025-04-02 19:09:47 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][26/234]	eta 0:03:11 lr 0.001163	time 0.8781 (0.9225)	loss 0.5678 (0.5263)	grad_norm 3.2533 (2.4230)	mem 20675MB
[2025-04-02 19:09:49 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][28/234]	eta 0:03:09 lr 0.001162	time 0.8787 (0.9195)	loss 0.5308 (0.5301)	grad_norm 1.9718 (2.4727)	mem 20675MB
[2025-04-02 19:09:51 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][30/234]	eta 0:03:07 lr 0.001162	time 0.8771 (0.9169)	loss 0.5932 (0.5330)	grad_norm 6.0239 (2.6088)	mem 20675MB
[2025-04-02 19:09:53 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][32/234]	eta 0:03:04 lr 0.001162	time 0.8779 (0.9145)	loss 0.5190 (0.5282)	grad_norm 2.0191 (2.6101)	mem 20675MB
[2025-04-02 19:09:54 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][34/234]	eta 0:03:02 lr 0.001161	time 0.8784 (0.9125)	loss 0.5444 (0.5284)	grad_norm 2.6027 (2.6030)	mem 20675MB
[2025-04-02 19:09:56 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][36/234]	eta 0:03:00 lr 0.001161	time 0.8783 (0.9106)	loss 0.5152 (0.5271)	grad_norm 1.7136 (2.5780)	mem 20675MB
[2025-04-02 19:09:58 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][38/234]	eta 0:02:58 lr 0.001161	time 0.8785 (0.9090)	loss 0.3738 (0.5220)	grad_norm 2.3990 (2.5638)	mem 20675MB
[2025-04-02 19:10:00 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][40/234]	eta 0:02:56 lr 0.001161	time 0.8787 (0.9075)	loss 0.4013 (0.5171)	grad_norm 3.4271 (2.5949)	mem 20675MB
[2025-04-02 19:10:02 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][42/234]	eta 0:02:53 lr 0.001160	time 0.8786 (0.9062)	loss 0.5170 (0.5190)	grad_norm 6.1108 (2.7137)	mem 20675MB
[2025-04-02 19:10:03 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][44/234]	eta 0:02:51 lr 0.001160	time 0.8779 (0.9050)	loss 0.7457 (0.5224)	grad_norm 4.4238 (2.7531)	mem 20675MB
[2025-04-02 19:10:05 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][46/234]	eta 0:02:49 lr 0.001160	time 0.8776 (0.9038)	loss 0.5883 (0.5211)	grad_norm 4.3729 (2.8229)	mem 20675MB
[2025-04-02 19:10:07 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][48/234]	eta 0:02:47 lr 0.001159	time 0.8768 (0.9028)	loss 0.3459 (0.5173)	grad_norm 3.5679 (2.8458)	mem 20675MB
[2025-04-02 19:10:09 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][50/234]	eta 0:02:45 lr 0.001159	time 0.8766 (0.9018)	loss 0.4140 (0.5157)	grad_norm 3.2722 (2.8404)	mem 20675MB
[2025-04-02 19:10:10 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][52/234]	eta 0:02:43 lr 0.001159	time 0.8765 (0.9009)	loss 0.5566 (0.5166)	grad_norm 1.5440 (2.8237)	mem 20675MB
[2025-04-02 19:10:12 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][54/234]	eta 0:02:42 lr 0.001159	time 0.8769 (0.9000)	loss 0.5736 (0.5190)	grad_norm 4.0234 (2.8637)	mem 20675MB
[2025-04-02 19:10:14 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][56/234]	eta 0:02:40 lr 0.001158	time 0.8772 (0.8993)	loss 0.4539 (0.5160)	grad_norm 3.9279 (2.8816)	mem 20675MB
[2025-04-02 19:10:16 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][58/234]	eta 0:02:38 lr 0.001158	time 0.8770 (0.8985)	loss 0.4567 (0.5127)	grad_norm 3.5102 (2.8978)	mem 20675MB
[2025-04-02 19:10:17 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][60/234]	eta 0:02:36 lr 0.001158	time 0.8768 (0.8978)	loss 0.5415 (0.5140)	grad_norm 2.0360 (2.8763)	mem 20675MB
[2025-04-02 19:10:19 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][62/234]	eta 0:02:34 lr 0.001157	time 0.8776 (0.8972)	loss 0.5780 (0.5132)	grad_norm 4.5014 (2.9696)	mem 20675MB
[2025-04-02 19:10:21 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][64/234]	eta 0:02:32 lr 0.001157	time 0.8769 (0.8966)	loss 0.5559 (0.5134)	grad_norm 3.8205 (2.9820)	mem 20675MB
[2025-04-02 19:10:23 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][66/234]	eta 0:02:30 lr 0.001157	time 0.8773 (0.8961)	loss 0.5625 (0.5122)	grad_norm 3.8361 (3.0307)	mem 20675MB
[2025-04-02 19:10:24 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][68/234]	eta 0:02:28 lr 0.001157	time 0.8777 (0.8955)	loss 0.5392 (0.5125)	grad_norm 3.3852 (3.0339)	mem 20675MB
[2025-04-02 19:10:26 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][70/234]	eta 0:02:26 lr 0.001156	time 0.8788 (0.8951)	loss 0.5045 (0.5122)	grad_norm 2.7040 (3.0142)	mem 20675MB
[2025-04-02 19:10:28 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][72/234]	eta 0:02:24 lr 0.001156	time 0.8780 (0.8946)	loss 0.5737 (0.5139)	grad_norm 1.5475 (2.9758)	mem 20675MB
[2025-04-02 19:10:30 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][74/234]	eta 0:02:23 lr 0.001156	time 0.8793 (0.8943)	loss 0.4417 (0.5126)	grad_norm 3.0547 (2.9781)	mem 20675MB
[2025-04-02 19:10:31 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][76/234]	eta 0:02:21 lr 0.001155	time 0.8785 (0.8939)	loss 0.5759 (0.5138)	grad_norm 2.2981 (2.9717)	mem 20675MB
[2025-04-02 19:10:33 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][78/234]	eta 0:02:19 lr 0.001155	time 0.8778 (0.8935)	loss 0.4197 (0.5115)	grad_norm 3.6560 (2.9927)	mem 20675MB
[2025-04-02 19:10:35 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][80/234]	eta 0:02:17 lr 0.001155	time 0.8774 (0.8931)	loss 0.5302 (0.5106)	grad_norm 4.7388 (3.0706)	mem 20675MB
[2025-04-02 19:10:37 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][82/234]	eta 0:02:15 lr 0.001154	time 0.8775 (0.8927)	loss 0.5986 (0.5115)	grad_norm 4.2659 (3.0714)	mem 20675MB
[2025-04-02 19:10:38 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][84/234]	eta 0:02:13 lr 0.001154	time 0.8783 (0.8924)	loss 0.5215 (0.5111)	grad_norm 5.6443 (3.1136)	mem 20675MB
[2025-04-02 19:10:40 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][86/234]	eta 0:02:12 lr 0.001154	time 0.8773 (0.8921)	loss 0.4265 (0.5117)	grad_norm 2.3256 (3.1187)	mem 20675MB
[2025-04-02 19:10:42 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][88/234]	eta 0:02:10 lr 0.001154	time 0.8781 (0.8918)	loss 0.5975 (0.5136)	grad_norm 5.0710 (3.1777)	mem 20675MB
[2025-04-02 19:10:44 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][90/234]	eta 0:02:08 lr 0.001153	time 0.8778 (0.8915)	loss 0.4997 (0.5141)	grad_norm 2.1651 (3.1822)	mem 20675MB
[2025-04-02 19:10:45 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][92/234]	eta 0:02:06 lr 0.001153	time 0.8778 (0.8912)	loss 0.5770 (0.5146)	grad_norm 3.1870 (3.1721)	mem 20675MB
[2025-04-02 19:10:47 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][94/234]	eta 0:02:04 lr 0.001153	time 0.8780 (0.8909)	loss 0.5986 (0.5164)	grad_norm 2.5855 (3.1917)	mem 20675MB
[2025-04-02 19:10:49 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][96/234]	eta 0:02:02 lr 0.001152	time 0.8789 (0.8907)	loss 0.5590 (0.5172)	grad_norm 1.9667 (3.1658)	mem 20675MB
[2025-04-02 19:10:51 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][98/234]	eta 0:02:01 lr 0.001152	time 0.8778 (0.8905)	loss 0.5719 (0.5171)	grad_norm 2.2030 (3.1471)	mem 20675MB
[2025-04-02 19:10:52 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][100/234]	eta 0:01:59 lr 0.001152	time 0.8781 (0.8902)	loss 0.4574 (0.5162)	grad_norm 3.5622 (3.1507)	mem 20675MB
[2025-04-02 19:10:54 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][102/234]	eta 0:01:57 lr 0.001151	time 0.8786 (0.8900)	loss 0.5066 (0.5155)	grad_norm 2.8850 (3.1434)	mem 20675MB
[2025-04-02 19:10:56 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][104/234]	eta 0:01:55 lr 0.001151	time 0.8784 (0.8898)	loss 0.5141 (0.5160)	grad_norm 2.6409 (3.1372)	mem 20675MB
[2025-04-02 19:10:58 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][106/234]	eta 0:01:53 lr 0.001151	time 0.8789 (0.8896)	loss 0.5525 (0.5168)	grad_norm 3.3343 (3.1340)	mem 20675MB
[2025-04-02 19:10:59 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][108/234]	eta 0:01:52 lr 0.001151	time 0.8784 (0.8894)	loss 0.4394 (0.5178)	grad_norm 2.1582 (3.1321)	mem 20675MB
[2025-04-02 19:11:01 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][110/234]	eta 0:01:50 lr 0.001150	time 0.8784 (0.8892)	loss 0.5901 (0.5186)	grad_norm 2.2419 (3.1173)	mem 20675MB
[2025-04-02 19:11:03 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][112/234]	eta 0:01:48 lr 0.001150	time 0.8782 (0.8891)	loss 0.4998 (0.5182)	grad_norm 1.8997 (3.0949)	mem 20675MB
[2025-04-02 19:11:05 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][114/234]	eta 0:01:46 lr 0.001150	time 0.8779 (0.8889)	loss 0.5876 (0.5185)	grad_norm 3.0081 (3.0832)	mem 20675MB
[2025-04-02 19:11:07 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][116/234]	eta 0:01:44 lr 0.001149	time 0.8781 (0.8887)	loss 0.5102 (0.5188)	grad_norm 1.5478 (3.0549)	mem 20675MB
[2025-04-02 19:11:08 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][118/234]	eta 0:01:43 lr 0.001149	time 0.8774 (0.8885)	loss 0.4688 (0.5185)	grad_norm 3.1084 (3.0450)	mem 20675MB
[2025-04-02 19:11:10 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][120/234]	eta 0:01:41 lr 0.001149	time 0.8785 (0.8884)	loss 0.5540 (0.5190)	grad_norm 2.0791 (3.0326)	mem 20675MB
[2025-04-02 19:11:12 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][122/234]	eta 0:01:39 lr 0.001148	time 0.8778 (0.8882)	loss 0.5781 (0.5188)	grad_norm 3.8978 (3.0379)	mem 20675MB
[2025-04-02 19:11:14 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][124/234]	eta 0:01:37 lr 0.001148	time 0.8787 (0.8881)	loss 0.5352 (0.5187)	grad_norm 4.6516 (3.0514)	mem 20675MB
[2025-04-02 19:11:15 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][126/234]	eta 0:01:35 lr 0.001148	time 0.8788 (0.8879)	loss 0.5086 (0.5185)	grad_norm 2.6634 (3.0618)	mem 20675MB
[2025-04-02 19:11:17 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][128/234]	eta 0:01:34 lr 0.001148	time 0.8785 (0.8878)	loss 0.5294 (0.5177)	grad_norm 5.6541 (3.0851)	mem 20675MB
[2025-04-02 19:11:19 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][130/234]	eta 0:01:32 lr 0.001147	time 0.8790 (0.8877)	loss 0.5932 (0.5185)	grad_norm 5.8907 (3.1090)	mem 20675MB
[2025-04-02 19:11:21 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][132/234]	eta 0:01:30 lr 0.001147	time 0.8785 (0.8876)	loss 0.5557 (0.5183)	grad_norm 6.2912 (3.1288)	mem 20675MB
[2025-04-02 19:11:22 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][134/234]	eta 0:01:28 lr 0.001147	time 0.8786 (0.8874)	loss 0.4046 (0.5180)	grad_norm 4.5685 (3.1514)	mem 20675MB
[2025-04-02 19:11:24 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][136/234]	eta 0:01:26 lr 0.001146	time 0.8790 (0.8873)	loss 0.4792 (0.5173)	grad_norm 2.5162 (3.1458)	mem 20675MB
[2025-04-02 19:11:26 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][138/234]	eta 0:01:25 lr 0.001146	time 0.8790 (0.8872)	loss 0.4607 (0.5167)	grad_norm 3.2026 (3.1374)	mem 20675MB
[2025-04-02 19:11:28 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][140/234]	eta 0:01:23 lr 0.001146	time 0.8782 (0.8871)	loss 0.3383 (0.5165)	grad_norm 3.4967 (3.1641)	mem 20675MB
[2025-04-02 19:11:29 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][142/234]	eta 0:01:21 lr 0.001145	time 0.8781 (0.8870)	loss 0.4093 (0.5155)	grad_norm 3.1704 (3.1545)	mem 20675MB
[2025-04-02 19:11:31 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][144/234]	eta 0:01:19 lr 0.001145	time 0.8783 (0.8869)	loss 0.3640 (0.5150)	grad_norm 3.0490 (3.1549)	mem 20675MB
[2025-04-02 19:11:33 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][146/234]	eta 0:01:18 lr 0.001145	time 0.8792 (0.8868)	loss 0.5112 (0.5150)	grad_norm 3.4968 (3.1620)	mem 20675MB
[2025-04-02 19:11:35 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][148/234]	eta 0:01:16 lr 0.001144	time 0.8795 (0.8867)	loss 0.4838 (0.5141)	grad_norm 3.4766 (3.1572)	mem 20675MB
[2025-04-02 19:11:36 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][150/234]	eta 0:01:14 lr 0.001144	time 0.8786 (0.8866)	loss 0.3886 (0.5136)	grad_norm 2.9799 (3.1667)	mem 20675MB
[2025-04-02 19:11:38 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][152/234]	eta 0:01:12 lr 0.001144	time 0.8797 (0.8865)	loss 0.5592 (0.5133)	grad_norm 2.4172 (3.1642)	mem 20675MB
[2025-04-02 19:11:40 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][154/234]	eta 0:01:10 lr 0.001143	time 0.8790 (0.8864)	loss 0.5517 (0.5129)	grad_norm 2.4594 (3.1662)	mem 20675MB
[2025-04-02 19:11:42 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][156/234]	eta 0:01:09 lr 0.001143	time 0.8798 (0.8864)	loss 0.4234 (0.5125)	grad_norm 3.2138 (3.1571)	mem 20675MB
[2025-04-02 19:11:43 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][158/234]	eta 0:01:07 lr 0.001143	time 0.8789 (0.8863)	loss 0.4763 (0.5129)	grad_norm 2.2162 (3.1554)	mem 20675MB
[2025-04-02 19:11:45 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][160/234]	eta 0:01:05 lr 0.001143	time 0.8786 (0.8862)	loss 0.5770 (0.5136)	grad_norm 2.2277 (3.1454)	mem 20675MB
[2025-04-02 19:11:47 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][162/234]	eta 0:01:03 lr 0.001142	time 0.8789 (0.8861)	loss 0.4895 (0.5131)	grad_norm 3.2868 (3.1518)	mem 20675MB
[2025-04-02 19:11:49 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][164/234]	eta 0:01:02 lr 0.001142	time 0.8787 (0.8860)	loss 0.3763 (0.5124)	grad_norm 3.1347 (3.1438)	mem 20675MB
[2025-04-02 19:11:51 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][166/234]	eta 0:01:00 lr 0.001142	time 0.8801 (0.8860)	loss 0.5825 (0.5121)	grad_norm 1.9632 (3.1367)	mem 20675MB
[2025-04-02 19:11:52 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][168/234]	eta 0:00:58 lr 0.001141	time 0.8800 (0.8859)	loss 0.6317 (0.5124)	grad_norm 4.2072 (3.1392)	mem 20675MB
[2025-04-02 19:11:54 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][170/234]	eta 0:00:56 lr 0.001141	time 0.8806 (0.8858)	loss 0.5448 (0.5128)	grad_norm 3.4256 (3.1374)	mem 20675MB
[2025-04-02 19:11:56 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][172/234]	eta 0:00:54 lr 0.001141	time 0.8794 (0.8858)	loss 0.5004 (0.5119)	grad_norm 3.6551 (3.1420)	mem 20675MB
[2025-04-02 19:11:58 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][174/234]	eta 0:00:53 lr 0.001140	time 0.8790 (0.8857)	loss 0.5040 (0.5119)	grad_norm 3.9529 (3.1432)	mem 20675MB
[2025-04-02 19:11:59 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][176/234]	eta 0:00:51 lr 0.001140	time 0.8803 (0.8857)	loss 0.4690 (0.5118)	grad_norm 2.5918 (3.1352)	mem 20675MB
[2025-04-02 19:12:01 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][178/234]	eta 0:00:49 lr 0.001140	time 0.8786 (0.8856)	loss 0.5267 (0.5121)	grad_norm 2.0842 (3.1256)	mem 20675MB
[2025-04-02 19:12:03 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][180/234]	eta 0:00:47 lr 0.001139	time 0.8785 (0.8855)	loss 0.5162 (0.5126)	grad_norm 2.3733 (3.1237)	mem 20675MB
[2025-04-02 19:12:05 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][182/234]	eta 0:00:46 lr 0.001139	time 0.8788 (0.8855)	loss 0.5246 (0.5127)	grad_norm 3.5474 (3.1204)	mem 20675MB
[2025-04-02 19:12:06 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][184/234]	eta 0:00:44 lr 0.001139	time 0.8784 (0.8854)	loss 0.4299 (0.5123)	grad_norm 2.5533 (3.1108)	mem 20675MB
[2025-04-02 19:12:08 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][186/234]	eta 0:00:42 lr 0.001138	time 0.8771 (0.8853)	loss 0.5196 (0.5130)	grad_norm 2.1479 (3.1010)	mem 20675MB
[2025-04-02 19:12:10 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][188/234]	eta 0:00:40 lr 0.001138	time 0.8779 (0.8852)	loss 0.5012 (0.5134)	grad_norm 3.7399 (3.1083)	mem 20675MB
[2025-04-02 19:12:12 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][190/234]	eta 0:00:38 lr 0.001138	time 0.8773 (0.8852)	loss 0.5433 (0.5136)	grad_norm 1.9682 (3.0969)	mem 20675MB
[2025-04-02 19:12:13 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][192/234]	eta 0:00:37 lr 0.001137	time 0.8776 (0.8851)	loss 0.5599 (0.5141)	grad_norm 4.6502 (3.1126)	mem 20675MB
[2025-04-02 19:12:15 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][194/234]	eta 0:00:35 lr 0.001137	time 0.8769 (0.8850)	loss 0.4893 (0.5136)	grad_norm 1.1730 (3.1024)	mem 20675MB
[2025-04-02 19:12:17 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][196/234]	eta 0:00:33 lr 0.001137	time 0.8773 (0.8850)	loss 0.5055 (0.5136)	grad_norm 1.8037 (3.0921)	mem 20675MB
[2025-04-02 19:12:19 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][198/234]	eta 0:00:31 lr 0.001137	time 0.8776 (0.8849)	loss 0.4511 (0.5134)	grad_norm 3.0283 (3.1069)	mem 20675MB
[2025-04-02 19:12:20 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][200/234]	eta 0:00:30 lr 0.001136	time 0.8772 (0.8848)	loss 0.5668 (0.5137)	grad_norm 2.5159 (3.0974)	mem 20675MB
[2025-04-02 19:12:22 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][202/234]	eta 0:00:28 lr 0.001136	time 0.8776 (0.8848)	loss 0.5757 (0.5142)	grad_norm 3.1577 (3.0974)	mem 20675MB
[2025-04-02 19:12:24 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][204/234]	eta 0:00:26 lr 0.001136	time 0.8782 (0.8847)	loss 0.5759 (0.5147)	grad_norm 2.7584 (3.0964)	mem 20675MB
[2025-04-02 19:12:26 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][206/234]	eta 0:00:24 lr 0.001135	time 0.8788 (0.8847)	loss 0.4028 (0.5141)	grad_norm 2.4686 (3.0944)	mem 20675MB
[2025-04-02 19:12:27 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][208/234]	eta 0:00:22 lr 0.001135	time 0.8781 (0.8846)	loss 0.5628 (0.5144)	grad_norm 2.1138 (3.0864)	mem 20675MB
[2025-04-02 19:12:29 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][210/234]	eta 0:00:21 lr 0.001135	time 0.8785 (0.8845)	loss 0.6081 (0.5149)	grad_norm 2.8641 (3.0880)	mem 20675MB
[2025-04-02 19:12:31 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][212/234]	eta 0:00:19 lr 0.001134	time 0.8779 (0.8845)	loss 0.6224 (0.5153)	grad_norm 2.4260 (3.0835)	mem 20675MB
[2025-04-02 19:12:33 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][214/234]	eta 0:00:17 lr 0.001134	time 0.8790 (0.8844)	loss 0.6149 (0.5153)	grad_norm 3.0862 (3.0883)	mem 20675MB
[2025-04-02 19:12:34 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][216/234]	eta 0:00:15 lr 0.001134	time 0.8780 (0.8844)	loss 0.5554 (0.5155)	grad_norm 2.3186 (3.0854)	mem 20675MB
[2025-04-02 19:12:36 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][218/234]	eta 0:00:14 lr 0.001133	time 0.8792 (0.8843)	loss 0.5233 (0.5149)	grad_norm 2.2921 (3.0797)	mem 20675MB
[2025-04-02 19:12:38 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][220/234]	eta 0:00:12 lr 0.001133	time 0.8777 (0.8843)	loss 0.4791 (0.5148)	grad_norm 1.6351 (3.0711)	mem 20675MB
[2025-04-02 19:12:40 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][222/234]	eta 0:00:10 lr 0.001133	time 0.8784 (0.8843)	loss 0.4831 (0.5139)	grad_norm 1.9914 (3.0646)	mem 20675MB
[2025-04-02 19:12:41 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][224/234]	eta 0:00:08 lr 0.001132	time 0.8782 (0.8842)	loss 0.5554 (0.5144)	grad_norm 4.0917 (3.0732)	mem 20675MB
[2025-04-02 19:12:43 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][226/234]	eta 0:00:07 lr 0.001132	time 0.8769 (0.8842)	loss 0.5313 (0.5144)	grad_norm 2.2970 (3.0793)	mem 20675MB
[2025-04-02 19:12:45 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][228/234]	eta 0:00:05 lr 0.001132	time 0.8785 (0.8841)	loss 0.4678 (0.5142)	grad_norm 3.3013 (3.0751)	mem 20675MB
[2025-04-02 19:12:47 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][230/234]	eta 0:00:03 lr 0.001131	time 0.8789 (0.8841)	loss 0.5308 (0.5144)	grad_norm 2.6455 (3.0719)	mem 20675MB
[2025-04-02 19:12:49 simmim_finetune] (main_finetune.py 252): INFO Train: [5/30][232/234]	eta 0:00:01 lr 0.001131	time 0.8781 (0.8840)	loss 0.5687 (0.5152)	grad_norm 5.3551 (3.0811)	mem 20675MB
[2025-04-02 19:12:49 simmim_finetune] (main_finetune.py 260): INFO EPOCH 5 training takes 0:03:26
[2025-04-02 19:12:50 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt5.pth saving......
[2025-04-02 19:12:53 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt5.pth saved !!!
[2025-04-02 19:12:54 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.115 (1.115)	Loss 0.9574 (0.9574)	Acc@1 43.750 (43.750)	Mem 20675MB
[2025-04-02 19:12:54 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 59.669
[2025-04-02 19:12:54 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 59.7%
[2025-04-02 19:12:54 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 59.67%
[2025-04-02 19:12:54 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [4.204640332381401e-06, 4.204640332381401e-06, 6.45584050860973e-06, 6.45584050860973e-06, 9.91922539511485e-06, 9.91922539511485e-06, 1.5247509835891962e-05, 1.5247509835891962e-05, 2.344487051401059e-05, 2.344487051401059e-05, 3.60561946341931e-05, 3.60561946341931e-05, 5.5458231742166184e-05, 5.5458231742166184e-05, 8.530751960058631e-05, 8.530751960058631e-05, 0.00013122950092123268, 0.00013122950092123268, 0.00020187870295299633, 0.00020187870295299633, 0.0003105697830018634, 0.0003105697830018634, 0.0004777868292308897, 0.0004777868292308897, 0.0007350438234293918, 0.0007350438234293918, 0.0011308238145040102, 0.0011308238145040102]
[2025-04-02 19:12:56 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][0/234]	eta 0:07:59 lr 0.001131	time 2.0500 (2.0500)	loss 0.5969 (0.5969)	grad_norm 1.5185 (1.5185)	mem 20675MB
[2025-04-02 19:12:58 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][2/234]	eta 0:04:54 lr 0.001130	time 0.8770 (1.2688)	loss 0.5127 (0.5403)	grad_norm 2.0851 (1.7603)	mem 20675MB
[2025-04-02 19:12:59 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][4/234]	eta 0:04:16 lr 0.001130	time 0.8798 (1.1132)	loss 0.5237 (0.5283)	grad_norm 3.2237 (2.4070)	mem 20675MB
[2025-04-02 19:13:01 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][6/234]	eta 0:03:58 lr 0.001130	time 0.8801 (1.0468)	loss 0.5482 (0.5393)	grad_norm 2.8063 (2.3444)	mem 20675MB
[2025-04-02 19:13:03 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][8/234]	eta 0:03:48 lr 0.001129	time 0.8799 (1.0099)	loss 0.5759 (0.5409)	grad_norm 2.6982 (2.5906)	mem 20675MB
[2025-04-02 19:13:05 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][10/234]	eta 0:03:40 lr 0.001129	time 0.8802 (0.9864)	loss 0.4788 (0.5409)	grad_norm 3.3642 (2.8545)	mem 20675MB
[2025-04-02 19:13:07 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][12/234]	eta 0:03:35 lr 0.001129	time 0.8786 (0.9700)	loss 0.4116 (0.5169)	grad_norm 5.3537 (2.9899)	mem 20675MB
[2025-04-02 19:13:08 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][14/234]	eta 0:03:30 lr 0.001128	time 0.8780 (0.9579)	loss 0.5694 (0.5192)	grad_norm 5.1599 (3.1805)	mem 20675MB
[2025-04-02 19:13:10 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][16/234]	eta 0:03:26 lr 0.001128	time 0.8788 (0.9487)	loss 0.4929 (0.5166)	grad_norm 2.0430 (3.0925)	mem 20675MB
[2025-04-02 19:13:12 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][18/234]	eta 0:03:23 lr 0.001128	time 0.8792 (0.9416)	loss 0.5669 (0.5200)	grad_norm 8.3102 (3.4970)	mem 20675MB
[2025-04-02 19:13:14 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][20/234]	eta 0:03:20 lr 0.001127	time 0.8780 (0.9357)	loss 0.4680 (0.5192)	grad_norm 3.4564 (3.4495)	mem 20675MB
[2025-04-02 19:13:15 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][22/234]	eta 0:03:17 lr 0.001127	time 0.8780 (0.9308)	loss 0.5186 (0.5149)	grad_norm 4.7970 (3.5152)	mem 20675MB
[2025-04-02 19:13:17 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][24/234]	eta 0:03:14 lr 0.001127	time 0.8788 (0.9268)	loss 0.5139 (0.5101)	grad_norm 2.2153 (3.4348)	mem 20675MB
[2025-04-02 19:13:19 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][26/234]	eta 0:03:12 lr 0.001126	time 0.8778 (0.9233)	loss 0.6341 (0.5175)	grad_norm 3.1651 (3.4509)	mem 20675MB
[2025-04-02 19:13:21 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][28/234]	eta 0:03:09 lr 0.001126	time 0.8787 (0.9202)	loss 0.5272 (0.5193)	grad_norm 1.7377 (3.3792)	mem 20675MB
[2025-04-02 19:13:22 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][30/234]	eta 0:03:07 lr 0.001126	time 0.8781 (0.9176)	loss 0.5063 (0.5149)	grad_norm 2.1608 (3.3368)	mem 20675MB
[2025-04-02 19:13:24 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][32/234]	eta 0:03:04 lr 0.001125	time 0.8775 (0.9152)	loss 0.5331 (0.5184)	grad_norm 4.7549 (3.3565)	mem 20675MB
[2025-04-02 19:13:26 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][34/234]	eta 0:03:02 lr 0.001125	time 0.8777 (0.9131)	loss 0.4515 (0.5151)	grad_norm 3.1721 (3.3533)	mem 20675MB
[2025-04-02 19:13:28 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][36/234]	eta 0:03:00 lr 0.001125	time 0.8787 (0.9112)	loss 0.4479 (0.5143)	grad_norm 3.7824 (3.3359)	mem 20675MB
[2025-04-02 19:13:29 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][38/234]	eta 0:02:58 lr 0.001124	time 0.8789 (0.9096)	loss 0.6194 (0.5191)	grad_norm 3.9321 (3.3359)	mem 20675MB
[2025-04-02 19:13:31 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][40/234]	eta 0:02:56 lr 0.001124	time 0.8798 (0.9082)	loss 0.4355 (0.5192)	grad_norm 2.1928 (3.3232)	mem 20675MB
[2025-04-02 19:13:33 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][42/234]	eta 0:02:54 lr 0.001124	time 0.8784 (0.9069)	loss 0.6464 (0.5227)	grad_norm 4.3527 (3.3120)	mem 20675MB
[2025-04-02 19:13:35 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][44/234]	eta 0:02:52 lr 0.001123	time 0.8791 (0.9057)	loss 0.4611 (0.5187)	grad_norm 3.1141 (3.3035)	mem 20675MB
[2025-04-02 19:13:36 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][46/234]	eta 0:02:50 lr 0.001123	time 0.8793 (0.9046)	loss 0.6175 (0.5196)	grad_norm 2.3863 (3.2724)	mem 20675MB
[2025-04-02 19:13:38 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][48/234]	eta 0:02:48 lr 0.001123	time 0.8783 (0.9035)	loss 0.4293 (0.5187)	grad_norm 1.9461 (3.2139)	mem 20675MB
[2025-04-02 19:13:40 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][50/234]	eta 0:02:46 lr 0.001122	time 0.8784 (0.9026)	loss 0.4440 (0.5177)	grad_norm 2.8459 (3.1855)	mem 20675MB
[2025-04-02 19:13:42 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][52/234]	eta 0:02:44 lr 0.001122	time 0.8795 (0.9017)	loss 0.5536 (0.5198)	grad_norm 1.8567 (3.1454)	mem 20675MB
[2025-04-02 19:13:43 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][54/234]	eta 0:02:42 lr 0.001122	time 0.8788 (0.9009)	loss 0.3811 (0.5186)	grad_norm 3.1194 (3.1470)	mem 20675MB
[2025-04-02 19:13:45 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][56/234]	eta 0:02:40 lr 0.001121	time 0.8768 (0.9001)	loss 0.5252 (0.5203)	grad_norm 4.8235 (3.1664)	mem 20675MB
[2025-04-02 19:13:47 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][58/234]	eta 0:02:38 lr 0.001121	time 0.8788 (0.8994)	loss 0.4246 (0.5193)	grad_norm 2.1217 (3.1374)	mem 20675MB
[2025-04-02 19:13:49 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][60/234]	eta 0:02:36 lr 0.001121	time 0.8776 (0.8987)	loss 0.5823 (0.5206)	grad_norm 1.9746 (3.0955)	mem 20675MB
[2025-04-02 19:13:51 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][62/234]	eta 0:02:34 lr 0.001120	time 0.8779 (0.8981)	loss 0.4443 (0.5167)	grad_norm 2.2631 (3.0828)	mem 20675MB
[2025-04-02 19:13:52 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][64/234]	eta 0:02:32 lr 0.001120	time 0.8778 (0.8975)	loss 0.5473 (0.5168)	grad_norm 2.5609 (3.0608)	mem 20675MB
[2025-04-02 19:13:54 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][66/234]	eta 0:02:30 lr 0.001120	time 0.8774 (0.8969)	loss 0.3423 (0.5141)	grad_norm 4.3956 (3.0602)	mem 20675MB
[2025-04-02 19:13:56 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][68/234]	eta 0:02:28 lr 0.001119	time 0.8775 (0.8964)	loss 0.4895 (0.5138)	grad_norm 3.0070 (3.0566)	mem 20675MB
[2025-04-02 19:13:58 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][70/234]	eta 0:02:26 lr 0.001119	time 0.8784 (0.8958)	loss 0.3518 (0.5128)	grad_norm 4.5538 (3.0993)	mem 20675MB
[2025-04-02 19:13:59 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][72/234]	eta 0:02:25 lr 0.001119	time 0.8769 (0.8954)	loss 0.3398 (0.5095)	grad_norm 2.6036 (3.0978)	mem 20675MB
[2025-04-02 19:14:01 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][74/234]	eta 0:02:23 lr 0.001118	time 0.8774 (0.8949)	loss 0.4525 (0.5093)	grad_norm 2.7787 (3.0909)	mem 20675MB
[2025-04-02 19:14:03 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][76/234]	eta 0:02:21 lr 0.001118	time 0.8777 (0.8945)	loss 0.5841 (0.5086)	grad_norm 3.1066 (3.0878)	mem 20675MB
[2025-04-02 19:14:05 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][78/234]	eta 0:02:19 lr 0.001118	time 0.8777 (0.8941)	loss 0.3555 (0.5054)	grad_norm 6.5198 (3.1535)	mem 20675MB
[2025-04-02 19:14:06 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][80/234]	eta 0:02:17 lr 0.001117	time 0.8788 (0.8937)	loss 0.5411 (0.5044)	grad_norm 5.0598 (3.1707)	mem 20675MB
[2025-04-02 19:14:08 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][82/234]	eta 0:02:15 lr 0.001117	time 0.8789 (0.8934)	loss 0.3644 (0.5028)	grad_norm 4.6132 (3.1895)	mem 20675MB
[2025-04-02 19:14:10 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][84/234]	eta 0:02:13 lr 0.001116	time 0.8785 (0.8930)	loss 0.6032 (0.5033)	grad_norm 3.7752 (3.2002)	mem 20675MB
[2025-04-02 19:14:12 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][86/234]	eta 0:02:12 lr 0.001116	time 0.8780 (0.8927)	loss 0.5098 (0.5018)	grad_norm 5.3132 (3.3128)	mem 20675MB
[2025-04-02 19:14:13 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][88/234]	eta 0:02:10 lr 0.001116	time 0.8778 (0.8924)	loss 0.3994 (0.5015)	grad_norm 2.4918 (3.2973)	mem 20675MB
[2025-04-02 19:14:15 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][90/234]	eta 0:02:08 lr 0.001115	time 0.8783 (0.8921)	loss 0.4224 (0.5010)	grad_norm 6.0842 (3.3528)	mem 20675MB
[2025-04-02 19:14:17 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][92/234]	eta 0:02:06 lr 0.001115	time 0.8785 (0.8918)	loss 0.4979 (0.5009)	grad_norm 4.1952 (3.3514)	mem 20675MB
[2025-04-02 19:14:19 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][94/234]	eta 0:02:04 lr 0.001115	time 0.8791 (0.8916)	loss 0.4714 (0.5005)	grad_norm 2.8852 (3.3367)	mem 20675MB
[2025-04-02 19:14:20 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][96/234]	eta 0:02:03 lr 0.001114	time 0.8797 (0.8913)	loss 0.5035 (0.5011)	grad_norm 4.3034 (3.3688)	mem 20675MB
[2025-04-02 19:14:22 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][98/234]	eta 0:02:01 lr 0.001114	time 0.8781 (0.8911)	loss 0.5505 (0.5016)	grad_norm 2.4359 (3.3471)	mem 20675MB
[2025-04-02 19:14:24 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][100/234]	eta 0:01:59 lr 0.001114	time 0.8782 (0.8908)	loss 0.5334 (0.5024)	grad_norm 3.9245 (3.3391)	mem 20675MB
[2025-04-02 19:14:26 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][102/234]	eta 0:01:57 lr 0.001113	time 0.8782 (0.8906)	loss 0.6255 (0.5036)	grad_norm 3.5892 (3.3340)	mem 20675MB
[2025-04-02 19:14:27 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][104/234]	eta 0:01:55 lr 0.001113	time 0.8778 (0.8904)	loss 0.4628 (0.5037)	grad_norm 3.5915 (3.3259)	mem 20675MB
[2025-04-02 19:14:29 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][106/234]	eta 0:01:53 lr 0.001113	time 0.8790 (0.8902)	loss 0.4987 (0.5028)	grad_norm 2.7167 (3.3239)	mem 20675MB
[2025-04-02 19:14:31 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][108/234]	eta 0:01:52 lr 0.001112	time 0.8794 (0.8900)	loss 0.5385 (0.5033)	grad_norm 2.8834 (3.3175)	mem 20675MB
[2025-04-02 19:14:33 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][110/234]	eta 0:01:50 lr 0.001112	time 0.8796 (0.8898)	loss 0.6027 (0.5034)	grad_norm 2.6692 (3.3205)	mem 20675MB
[2025-04-02 19:14:34 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][112/234]	eta 0:01:48 lr 0.001112	time 0.8787 (0.8896)	loss 0.4986 (0.5032)	grad_norm 2.1998 (3.2995)	mem 20675MB
[2025-04-02 19:14:36 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][114/234]	eta 0:01:46 lr 0.001111	time 0.8782 (0.8894)	loss 0.4961 (0.5035)	grad_norm 2.5799 (3.2811)	mem 20675MB
[2025-04-02 19:14:38 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][116/234]	eta 0:01:44 lr 0.001111	time 0.8775 (0.8893)	loss 0.5348 (0.5038)	grad_norm 2.4257 (3.2697)	mem 20675MB
[2025-04-02 19:14:40 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][118/234]	eta 0:01:43 lr 0.001111	time 0.8799 (0.8891)	loss 0.4838 (0.5040)	grad_norm 2.3474 (3.2578)	mem 20675MB
[2025-04-02 19:14:41 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][120/234]	eta 0:01:41 lr 0.001110	time 0.8792 (0.8890)	loss 0.5660 (0.5050)	grad_norm 2.6703 (3.2414)	mem 20675MB
[2025-04-02 19:14:43 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][122/234]	eta 0:01:39 lr 0.001110	time 0.8772 (0.8888)	loss 0.4885 (0.5054)	grad_norm 1.9370 (3.2231)	mem 20675MB
[2025-04-02 19:14:45 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][124/234]	eta 0:01:37 lr 0.001110	time 0.8785 (0.8886)	loss 0.4966 (0.5042)	grad_norm 2.3390 (3.2145)	mem 20675MB
[2025-04-02 19:14:47 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][126/234]	eta 0:01:35 lr 0.001109	time 0.8778 (0.8885)	loss 0.4255 (0.5030)	grad_norm 3.3103 (3.2094)	mem 20675MB
[2025-04-02 19:14:49 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][128/234]	eta 0:01:34 lr 0.001109	time 0.8786 (0.8883)	loss 0.4794 (0.5032)	grad_norm 3.0720 (3.2083)	mem 20675MB
[2025-04-02 19:14:50 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][130/234]	eta 0:01:32 lr 0.001108	time 0.8787 (0.8882)	loss 0.5173 (0.5029)	grad_norm 2.9236 (3.1963)	mem 20675MB
[2025-04-02 19:14:52 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][132/234]	eta 0:01:30 lr 0.001108	time 0.8789 (0.8881)	loss 0.5372 (0.5037)	grad_norm 3.1829 (3.2038)	mem 20675MB
[2025-04-02 19:14:54 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][134/234]	eta 0:01:28 lr 0.001108	time 0.8782 (0.8879)	loss 0.4594 (0.5034)	grad_norm 2.4381 (3.1923)	mem 20675MB
[2025-04-02 19:14:56 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][136/234]	eta 0:01:27 lr 0.001107	time 0.8787 (0.8878)	loss 0.3795 (0.5032)	grad_norm 2.2655 (3.1880)	mem 20675MB
[2025-04-02 19:14:57 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][138/234]	eta 0:01:25 lr 0.001107	time 0.8778 (0.8877)	loss 0.5934 (0.5042)	grad_norm 2.8789 (3.1835)	mem 20675MB
[2025-04-02 19:14:59 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][140/234]	eta 0:01:23 lr 0.001107	time 0.8776 (0.8875)	loss 0.4390 (0.5032)	grad_norm 2.3501 (3.1863)	mem 20675MB
[2025-04-02 19:15:01 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][142/234]	eta 0:01:21 lr 0.001106	time 0.8777 (0.8874)	loss 0.5038 (0.5032)	grad_norm 2.0434 (3.1709)	mem 20675MB
[2025-04-02 19:15:03 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][144/234]	eta 0:01:19 lr 0.001106	time 0.8782 (0.8873)	loss 0.3478 (0.5023)	grad_norm 2.9684 (3.1809)	mem 20675MB
[2025-04-02 19:15:04 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][146/234]	eta 0:01:18 lr 0.001106	time 0.8765 (0.8872)	loss 0.4718 (0.5019)	grad_norm 2.8047 (3.1692)	mem 20675MB
[2025-04-02 19:15:06 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][148/234]	eta 0:01:16 lr 0.001105	time 0.8766 (0.8870)	loss 0.5111 (0.5012)	grad_norm 2.7137 (3.1784)	mem 20675MB
[2025-04-02 19:15:08 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][150/234]	eta 0:01:14 lr 0.001105	time 0.8777 (0.8869)	loss 0.6187 (0.5025)	grad_norm 3.2636 (3.1787)	mem 20675MB
[2025-04-02 19:15:10 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][152/234]	eta 0:01:12 lr 0.001105	time 0.8765 (0.8868)	loss 0.5192 (0.5028)	grad_norm 3.7867 (3.1873)	mem 20675MB
[2025-04-02 19:15:11 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][154/234]	eta 0:01:10 lr 0.001104	time 0.8896 (0.8868)	loss 0.4897 (0.5022)	grad_norm 2.7423 (3.1832)	mem 20675MB
[2025-04-02 19:15:13 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][156/234]	eta 0:01:09 lr 0.001104	time 0.8776 (0.8866)	loss 0.3663 (0.5008)	grad_norm 2.5098 (3.1773)	mem 20675MB
[2025-04-02 19:15:15 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][158/234]	eta 0:01:07 lr 0.001103	time 0.8776 (0.8865)	loss 0.4643 (0.5000)	grad_norm 4.6336 (3.1855)	mem 20675MB
[2025-04-02 19:15:17 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][160/234]	eta 0:01:05 lr 0.001103	time 0.8791 (0.8864)	loss 0.4807 (0.4998)	grad_norm 3.1253 (3.1761)	mem 20675MB
[2025-04-02 19:15:18 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][162/234]	eta 0:01:03 lr 0.001103	time 0.8777 (0.8863)	loss 0.5577 (0.4998)	grad_norm 2.3495 (3.1733)	mem 20675MB
[2025-04-02 19:15:20 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][164/234]	eta 0:01:02 lr 0.001102	time 0.8762 (0.8862)	loss 0.5271 (0.4992)	grad_norm 2.2945 (3.1697)	mem 20675MB
[2025-04-02 19:15:22 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][166/234]	eta 0:01:00 lr 0.001102	time 0.8774 (0.8862)	loss 0.4508 (0.4990)	grad_norm 3.2010 (3.1683)	mem 20675MB
[2025-04-02 19:15:24 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][168/234]	eta 0:00:58 lr 0.001102	time 0.8790 (0.8861)	loss 0.5039 (0.4993)	grad_norm 2.4318 (3.1590)	mem 20675MB
[2025-04-02 19:15:25 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][170/234]	eta 0:00:56 lr 0.001101	time 0.8783 (0.8860)	loss 0.5494 (0.4999)	grad_norm 3.0389 (3.1664)	mem 20675MB
[2025-04-02 19:15:27 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][172/234]	eta 0:00:54 lr 0.001101	time 0.8779 (0.8859)	loss 0.3938 (0.4988)	grad_norm 2.8409 (3.1586)	mem 20675MB
[2025-04-02 19:15:29 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][174/234]	eta 0:00:53 lr 0.001101	time 0.8765 (0.8858)	loss 0.4930 (0.4993)	grad_norm 1.9588 (3.1454)	mem 20675MB
[2025-04-02 19:15:31 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][176/234]	eta 0:00:51 lr 0.001100	time 0.8778 (0.8857)	loss 0.3380 (0.4980)	grad_norm 3.6569 (3.1462)	mem 20675MB
[2025-04-02 19:15:32 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][178/234]	eta 0:00:49 lr 0.001100	time 0.8780 (0.8857)	loss 0.4459 (0.4975)	grad_norm 4.2304 (3.1468)	mem 20675MB
[2025-04-02 19:15:34 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][180/234]	eta 0:00:47 lr 0.001099	time 0.8778 (0.8856)	loss 0.4957 (0.4981)	grad_norm 3.5161 (3.1655)	mem 20675MB
[2025-04-02 19:15:36 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][182/234]	eta 0:00:46 lr 0.001099	time 0.8777 (0.8855)	loss 0.5096 (0.4986)	grad_norm 6.8483 (3.1872)	mem 20675MB
[2025-04-02 19:15:38 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][184/234]	eta 0:00:44 lr 0.001099	time 0.8773 (0.8854)	loss 0.6166 (0.4994)	grad_norm 3.2747 (3.1948)	mem 20675MB
[2025-04-02 19:15:39 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][186/234]	eta 0:00:42 lr 0.001098	time 0.8770 (0.8853)	loss 0.4476 (0.4995)	grad_norm 2.2091 (3.1880)	mem 20675MB
[2025-04-02 19:15:41 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][188/234]	eta 0:00:40 lr 0.001098	time 0.8764 (0.8853)	loss 0.5716 (0.5002)	grad_norm 1.7991 (3.1744)	mem 20675MB
[2025-04-02 19:15:43 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][190/234]	eta 0:00:38 lr 0.001098	time 0.8764 (0.8852)	loss 0.4788 (0.5004)	grad_norm 2.1960 (3.1645)	mem 20675MB
[2025-04-02 19:15:45 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][192/234]	eta 0:00:37 lr 0.001097	time 0.8772 (0.8851)	loss 0.5428 (0.5006)	grad_norm 1.8631 (3.1572)	mem 20675MB
[2025-04-02 19:15:47 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][194/234]	eta 0:00:35 lr 0.001097	time 0.8774 (0.8850)	loss 0.5199 (0.5007)	grad_norm 1.7905 (3.1449)	mem 20675MB
[2025-04-02 19:15:48 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][196/234]	eta 0:00:33 lr 0.001097	time 0.8775 (0.8850)	loss 0.6141 (0.5019)	grad_norm 2.8322 (3.1423)	mem 20675MB
[2025-04-02 19:15:50 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][198/234]	eta 0:00:31 lr 0.001096	time 0.8773 (0.8849)	loss 0.5713 (0.5020)	grad_norm 3.4718 (3.1419)	mem 20675MB
[2025-04-02 19:15:52 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][200/234]	eta 0:00:30 lr 0.001096	time 0.8767 (0.8848)	loss 0.5812 (0.5027)	grad_norm 2.6585 (3.1381)	mem 20675MB
[2025-04-02 19:15:54 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][202/234]	eta 0:00:28 lr 0.001095	time 0.8784 (0.8848)	loss 0.6081 (0.5032)	grad_norm 2.6291 (3.1306)	mem 20675MB
[2025-04-02 19:15:55 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][204/234]	eta 0:00:26 lr 0.001095	time 0.8779 (0.8847)	loss 0.5441 (0.5031)	grad_norm 3.3481 (3.1384)	mem 20675MB
[2025-04-02 19:15:57 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][206/234]	eta 0:00:24 lr 0.001095	time 0.8796 (0.8847)	loss 0.4929 (0.5029)	grad_norm 2.3695 (3.1320)	mem 20675MB
[2025-04-02 19:15:59 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][208/234]	eta 0:00:22 lr 0.001094	time 0.8775 (0.8846)	loss 0.4750 (0.5031)	grad_norm 4.7023 (3.1355)	mem 20675MB
[2025-04-02 19:16:01 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][210/234]	eta 0:00:21 lr 0.001094	time 0.8785 (0.8846)	loss 0.4762 (0.5031)	grad_norm 2.0535 (3.1317)	mem 20675MB
[2025-04-02 19:16:02 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][212/234]	eta 0:00:19 lr 0.001094	time 0.8787 (0.8845)	loss 0.5225 (0.5034)	grad_norm 2.7942 (3.1324)	mem 20675MB
[2025-04-02 19:16:04 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][214/234]	eta 0:00:17 lr 0.001093	time 0.8778 (0.8844)	loss 0.3791 (0.5031)	grad_norm 4.2262 (3.1328)	mem 20675MB
[2025-04-02 19:16:06 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][216/234]	eta 0:00:15 lr 0.001093	time 0.8767 (0.8844)	loss 0.4236 (0.5023)	grad_norm 2.2282 (3.1324)	mem 20675MB
[2025-04-02 19:16:08 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][218/234]	eta 0:00:14 lr 0.001092	time 0.8786 (0.8843)	loss 0.5479 (0.5027)	grad_norm 4.9647 (3.1418)	mem 20675MB
[2025-04-02 19:16:09 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][220/234]	eta 0:00:12 lr 0.001092	time 0.8773 (0.8843)	loss 0.4346 (0.5031)	grad_norm 3.7610 (3.1483)	mem 20675MB
[2025-04-02 19:16:11 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][222/234]	eta 0:00:10 lr 0.001092	time 0.8775 (0.8842)	loss 0.5463 (0.5034)	grad_norm 2.8877 (3.1526)	mem 20675MB
[2025-04-02 19:16:13 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][224/234]	eta 0:00:08 lr 0.001091	time 0.8771 (0.8842)	loss 0.5739 (0.5034)	grad_norm 2.0065 (3.1466)	mem 20675MB
[2025-04-02 19:16:15 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][226/234]	eta 0:00:07 lr 0.001091	time 0.8783 (0.8841)	loss 0.6073 (0.5041)	grad_norm 4.2792 (3.1550)	mem 20675MB
[2025-04-02 19:16:16 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][228/234]	eta 0:00:05 lr 0.001091	time 0.8783 (0.8841)	loss 0.5574 (0.5046)	grad_norm 2.5903 (3.1465)	mem 20675MB
[2025-04-02 19:16:18 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][230/234]	eta 0:00:03 lr 0.001090	time 0.8778 (0.8840)	loss 0.5283 (0.5047)	grad_norm 5.3259 (3.1583)	mem 20675MB
[2025-04-02 19:16:20 simmim_finetune] (main_finetune.py 252): INFO Train: [6/30][232/234]	eta 0:00:01 lr 0.001090	time 0.8780 (0.8840)	loss 0.4778 (0.5049)	grad_norm 2.3254 (3.1501)	mem 20675MB
[2025-04-02 19:16:21 simmim_finetune] (main_finetune.py 260): INFO EPOCH 6 training takes 0:03:26
[2025-04-02 19:16:22 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.228 (1.228)	Loss 1.1352 (1.1352)	Acc@1 30.469 (30.469)	Mem 20675MB
[2025-04-02 19:16:22 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 50.829
[2025-04-02 19:16:22 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 50.8%
[2025-04-02 19:16:22 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 59.67%
[2025-04-02 19:16:22 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [4.060739580613894e-06, 4.060739580613894e-06, 6.230023483676789e-06, 6.230023483676789e-06, 9.56738333454278e-06, 9.56738333454278e-06, 1.4701783105105846e-05, 1.4701783105105846e-05, 2.2600859675202865e-05, 2.2600859675202865e-05, 3.475328516765982e-05, 3.475328516765982e-05, 5.3449324386824364e-05, 5.3449324386824364e-05, 8.22124616470775e-05, 8.22124616470775e-05, 0.00012646344204746693, 0.00012646344204746693, 0.00019454187343268152, 0.00019454187343268152, 0.0002992779217176269, 0.0002992779217176269, 0.000460410303694466, 0.000460410303694466, 0.0007083062759665262, 0.0007083062759665262, 0.0010896846948466189, 0.0010896846948466189]
[2025-04-02 19:16:24 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][0/234]	eta 0:07:57 lr 0.001089	time 2.0423 (2.0423)	loss 0.4238 (0.4238)	grad_norm 2.7064 (2.7064)	mem 20675MB
[2025-04-02 19:16:26 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][2/234]	eta 0:04:54 lr 0.001089	time 0.8801 (1.2676)	loss 0.5538 (0.4466)	grad_norm 2.6329 (2.5135)	mem 20675MB
[2025-04-02 19:16:28 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][4/234]	eta 0:04:15 lr 0.001089	time 0.8796 (1.1127)	loss 0.3351 (0.4368)	grad_norm 2.8961 (2.5782)	mem 20675MB
[2025-04-02 19:16:30 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][6/234]	eta 0:03:58 lr 0.001088	time 0.8796 (1.0461)	loss 0.5132 (0.4439)	grad_norm 3.2235 (2.6816)	mem 20675MB
[2025-04-02 19:16:31 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][8/234]	eta 0:03:48 lr 0.001088	time 0.8789 (1.0093)	loss 0.3601 (0.4282)	grad_norm 2.7078 (2.7606)	mem 20675MB
[2025-04-02 19:16:33 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][10/234]	eta 0:03:40 lr 0.001088	time 0.8789 (0.9857)	loss 0.6019 (0.4417)	grad_norm 4.2744 (2.8678)	mem 20675MB
[2025-04-02 19:16:35 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][12/234]	eta 0:03:35 lr 0.001087	time 0.8768 (0.9692)	loss 0.4435 (0.4465)	grad_norm 3.6525 (2.9818)	mem 20675MB
[2025-04-02 19:16:37 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][14/234]	eta 0:03:30 lr 0.001087	time 0.8768 (0.9570)	loss 0.4729 (0.4439)	grad_norm 2.7434 (3.0278)	mem 20675MB
[2025-04-02 19:16:38 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][16/234]	eta 0:03:26 lr 0.001086	time 0.8789 (0.9478)	loss 0.3790 (0.4387)	grad_norm 3.2310 (3.0123)	mem 20675MB
[2025-04-02 19:16:40 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][18/234]	eta 0:03:23 lr 0.001086	time 0.8780 (0.9405)	loss 0.5555 (0.4482)	grad_norm 3.5982 (3.0973)	mem 20675MB
[2025-04-02 19:16:42 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][20/234]	eta 0:03:20 lr 0.001086	time 0.8792 (0.9348)	loss 0.4371 (0.4426)	grad_norm 2.3355 (3.0482)	mem 20675MB
[2025-04-02 19:16:44 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][22/234]	eta 0:03:17 lr 0.001085	time 0.8792 (0.9300)	loss 0.4329 (0.4487)	grad_norm 3.1472 (3.1677)	mem 20675MB
[2025-04-02 19:16:45 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][24/234]	eta 0:03:14 lr 0.001085	time 0.8786 (0.9259)	loss 0.5792 (0.4549)	grad_norm 3.1065 (3.1409)	mem 20675MB
[2025-04-02 19:16:47 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][26/234]	eta 0:03:11 lr 0.001085	time 0.8786 (0.9225)	loss 0.5209 (0.4543)	grad_norm 3.7434 (3.1551)	mem 20675MB
[2025-04-02 19:16:49 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][28/234]	eta 0:03:09 lr 0.001084	time 0.8795 (0.9195)	loss 0.5571 (0.4611)	grad_norm 2.3183 (3.1109)	mem 20675MB
[2025-04-02 19:16:51 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][30/234]	eta 0:03:07 lr 0.001084	time 0.8776 (0.9169)	loss 0.5673 (0.4636)	grad_norm 3.0241 (3.1091)	mem 20675MB
[2025-04-02 19:16:53 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][32/234]	eta 0:03:04 lr 0.001083	time 0.8767 (0.9145)	loss 0.4914 (0.4652)	grad_norm 2.1311 (3.0620)	mem 20675MB
[2025-04-02 19:16:54 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][34/234]	eta 0:03:02 lr 0.001083	time 0.8765 (0.9124)	loss 0.6700 (0.4678)	grad_norm 4.2661 (3.0991)	mem 20675MB
[2025-04-02 19:16:56 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][36/234]	eta 0:03:00 lr 0.001083	time 0.8782 (0.9106)	loss 0.5559 (0.4683)	grad_norm 3.5062 (3.0932)	mem 20675MB
[2025-04-02 19:16:58 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][38/234]	eta 0:02:58 lr 0.001082	time 0.8777 (0.9089)	loss 0.3565 (0.4674)	grad_norm 3.0588 (3.0838)	mem 20675MB
[2025-04-02 19:17:00 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][40/234]	eta 0:02:56 lr 0.001082	time 0.8793 (0.9075)	loss 0.4147 (0.4668)	grad_norm 3.9153 (3.0876)	mem 20675MB
[2025-04-02 19:17:01 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][42/234]	eta 0:02:53 lr 0.001082	time 0.8779 (0.9062)	loss 0.5283 (0.4682)	grad_norm 1.9253 (3.0427)	mem 20675MB
[2025-04-02 19:17:03 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][44/234]	eta 0:02:51 lr 0.001081	time 0.8778 (0.9050)	loss 0.4305 (0.4649)	grad_norm 2.7014 (3.0309)	mem 20675MB
[2025-04-02 19:17:05 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][46/234]	eta 0:02:49 lr 0.001081	time 0.8778 (0.9038)	loss 0.5069 (0.4680)	grad_norm 3.2723 (3.0344)	mem 20675MB
[2025-04-02 19:17:07 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][48/234]	eta 0:02:47 lr 0.001080	time 0.8774 (0.9028)	loss 0.4826 (0.4696)	grad_norm 1.1944 (2.9849)	mem 20675MB
[2025-04-02 19:17:08 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][50/234]	eta 0:02:45 lr 0.001080	time 0.8786 (0.9019)	loss 0.5525 (0.4712)	grad_norm 2.1771 (2.9523)	mem 20675MB
[2025-04-02 19:17:10 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][52/234]	eta 0:02:43 lr 0.001080	time 0.8787 (0.9010)	loss 0.4594 (0.4707)	grad_norm 2.9391 (2.9643)	mem 20675MB
[2025-04-02 19:17:12 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][54/234]	eta 0:02:42 lr 0.001079	time 0.8779 (0.9002)	loss 0.4462 (0.4728)	grad_norm 2.6857 (2.9600)	mem 20675MB
[2025-04-02 19:17:14 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][56/234]	eta 0:02:40 lr 0.001079	time 0.8784 (0.8995)	loss 0.5149 (0.4738)	grad_norm 1.8112 (2.9193)	mem 20675MB
[2025-04-02 19:17:15 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][58/234]	eta 0:02:38 lr 0.001078	time 0.8773 (0.8988)	loss 0.5173 (0.4762)	grad_norm 2.5431 (2.8893)	mem 20675MB
[2025-04-02 19:17:17 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][60/234]	eta 0:02:36 lr 0.001078	time 0.8779 (0.8981)	loss 0.5623 (0.4785)	grad_norm 2.1235 (2.8839)	mem 20675MB
[2025-04-02 19:17:19 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][62/234]	eta 0:02:34 lr 0.001078	time 0.8784 (0.8975)	loss 0.5661 (0.4807)	grad_norm 2.1286 (2.8560)	mem 20675MB
[2025-04-02 19:17:21 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][64/234]	eta 0:02:32 lr 0.001077	time 0.8791 (0.8970)	loss 0.5009 (0.4830)	grad_norm 3.6963 (2.8737)	mem 20675MB
[2025-04-02 19:17:22 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][66/234]	eta 0:02:30 lr 0.001077	time 0.8787 (0.8964)	loss 0.5472 (0.4837)	grad_norm 5.1648 (2.9071)	mem 20675MB
[2025-04-02 19:17:24 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][68/234]	eta 0:02:28 lr 0.001077	time 0.8783 (0.8959)	loss 0.4112 (0.4836)	grad_norm 2.2308 (2.9090)	mem 20675MB
[2025-04-02 19:17:26 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][70/234]	eta 0:02:26 lr 0.001076	time 0.8775 (0.8954)	loss 0.4491 (0.4830)	grad_norm 3.4586 (2.9140)	mem 20675MB
[2025-04-02 19:17:28 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][72/234]	eta 0:02:24 lr 0.001076	time 0.8788 (0.8950)	loss 0.4696 (0.4825)	grad_norm 3.3648 (2.9419)	mem 20675MB
[2025-04-02 19:17:29 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][74/234]	eta 0:02:23 lr 0.001075	time 0.8780 (0.8946)	loss 0.5774 (0.4845)	grad_norm 2.7610 (2.9271)	mem 20675MB
[2025-04-02 19:17:31 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][76/234]	eta 0:02:21 lr 0.001075	time 0.8783 (0.8942)	loss 0.3697 (0.4827)	grad_norm 3.9255 (2.9459)	mem 20675MB
[2025-04-02 19:17:33 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][78/234]	eta 0:02:19 lr 0.001075	time 0.8795 (0.8938)	loss 0.5587 (0.4845)	grad_norm 2.4699 (2.9425)	mem 20675MB
[2025-04-02 19:17:35 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][80/234]	eta 0:02:17 lr 0.001074	time 0.8776 (0.8935)	loss 0.4766 (0.4825)	grad_norm 1.9804 (2.9447)	mem 20675MB
[2025-04-02 19:17:36 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][82/234]	eta 0:02:15 lr 0.001074	time 0.8776 (0.8931)	loss 0.4409 (0.4817)	grad_norm 3.2456 (2.9489)	mem 20675MB
[2025-04-02 19:17:38 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][84/234]	eta 0:02:13 lr 0.001073	time 0.8798 (0.8928)	loss 0.5106 (0.4828)	grad_norm 3.6614 (2.9475)	mem 20675MB
[2025-04-02 19:17:40 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][86/234]	eta 0:02:12 lr 0.001073	time 0.8797 (0.8925)	loss 0.6062 (0.4838)	grad_norm 2.8560 (2.9473)	mem 20675MB
[2025-04-02 19:17:42 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][88/234]	eta 0:02:10 lr 0.001073	time 0.8779 (0.8922)	loss 0.4045 (0.4836)	grad_norm 2.3947 (2.9411)	mem 20675MB
[2025-04-02 19:17:44 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][90/234]	eta 0:02:08 lr 0.001072	time 0.8768 (0.8919)	loss 0.5288 (0.4843)	grad_norm 1.9388 (2.9175)	mem 20675MB
[2025-04-02 19:17:45 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][92/234]	eta 0:02:06 lr 0.001072	time 0.8764 (0.8916)	loss 0.5514 (0.4859)	grad_norm 2.1643 (2.9095)	mem 20675MB
[2025-04-02 19:17:47 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][94/234]	eta 0:02:04 lr 0.001072	time 0.8765 (0.8913)	loss 0.4433 (0.4862)	grad_norm 3.3271 (2.9050)	mem 20675MB
[2025-04-02 19:17:49 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][96/234]	eta 0:02:02 lr 0.001071	time 0.8782 (0.8910)	loss 0.3669 (0.4846)	grad_norm 3.4410 (2.9141)	mem 20675MB
[2025-04-02 19:17:51 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][98/234]	eta 0:02:01 lr 0.001071	time 0.8781 (0.8908)	loss 0.4894 (0.4850)	grad_norm 2.6986 (2.9011)	mem 20675MB
[2025-04-02 19:17:52 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][100/234]	eta 0:01:59 lr 0.001070	time 0.8779 (0.8905)	loss 0.4895 (0.4861)	grad_norm 4.2499 (2.9200)	mem 20675MB
[2025-04-02 19:17:54 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][102/234]	eta 0:01:57 lr 0.001070	time 0.8774 (0.8903)	loss 0.3908 (0.4854)	grad_norm 4.0313 (2.9253)	mem 20675MB
[2025-04-02 19:17:56 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][104/234]	eta 0:01:55 lr 0.001070	time 0.8787 (0.8901)	loss 0.6823 (0.4875)	grad_norm 5.3973 (2.9557)	mem 20675MB
[2025-04-02 19:17:58 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][106/234]	eta 0:01:53 lr 0.001069	time 0.8777 (0.8899)	loss 0.5321 (0.4865)	grad_norm 2.3948 (2.9529)	mem 20675MB
[2025-04-02 19:17:59 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][108/234]	eta 0:01:52 lr 0.001069	time 0.8798 (0.8897)	loss 0.4448 (0.4854)	grad_norm 4.8421 (2.9792)	mem 20675MB
[2025-04-02 19:18:01 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][110/234]	eta 0:01:50 lr 0.001068	time 0.8780 (0.8895)	loss 0.4157 (0.4863)	grad_norm 2.9351 (2.9934)	mem 20675MB
[2025-04-02 19:18:03 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][112/234]	eta 0:01:48 lr 0.001068	time 0.8781 (0.8893)	loss 0.4245 (0.4849)	grad_norm 2.7173 (2.9840)	mem 20675MB
[2025-04-02 19:18:05 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][114/234]	eta 0:01:46 lr 0.001068	time 0.8770 (0.8891)	loss 0.4315 (0.4845)	grad_norm 5.7422 (3.0100)	mem 20675MB
[2025-04-02 19:18:06 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][116/234]	eta 0:01:44 lr 0.001067	time 0.8774 (0.8889)	loss 0.3983 (0.4843)	grad_norm 4.1232 (3.0384)	mem 20675MB
[2025-04-02 19:18:08 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][118/234]	eta 0:01:43 lr 0.001067	time 0.8767 (0.8887)	loss 0.5642 (0.4849)	grad_norm 7.4005 (3.0846)	mem 20675MB
[2025-04-02 19:18:10 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][120/234]	eta 0:01:41 lr 0.001066	time 0.8776 (0.8886)	loss 0.5922 (0.4854)	grad_norm 7.5511 (3.1535)	mem 20675MB
[2025-04-02 19:18:12 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][122/234]	eta 0:01:39 lr 0.001066	time 0.8766 (0.8884)	loss 0.6349 (0.4878)	grad_norm 3.7668 (3.1788)	mem 20675MB
[2025-04-02 19:18:13 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][124/234]	eta 0:01:37 lr 0.001066	time 0.8767 (0.8882)	loss 0.5670 (0.4888)	grad_norm 2.9991 (3.1721)	mem 20675MB
[2025-04-02 19:18:15 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][126/234]	eta 0:01:35 lr 0.001065	time 0.8777 (0.8881)	loss 0.4354 (0.4892)	grad_norm 2.7973 (3.1769)	mem 20675MB
[2025-04-02 19:18:17 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][128/234]	eta 0:01:34 lr 0.001065	time 0.8776 (0.8879)	loss 0.4161 (0.4889)	grad_norm 4.2190 (3.1814)	mem 20675MB
[2025-04-02 19:18:19 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][130/234]	eta 0:01:32 lr 0.001064	time 0.8782 (0.8878)	loss 0.5268 (0.4892)	grad_norm 2.8022 (3.1729)	mem 20675MB
[2025-04-02 19:18:20 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][132/234]	eta 0:01:30 lr 0.001064	time 0.8778 (0.8876)	loss 0.5381 (0.4899)	grad_norm 3.2041 (3.1739)	mem 20675MB
[2025-04-02 19:18:22 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][134/234]	eta 0:01:28 lr 0.001064	time 0.8771 (0.8875)	loss 0.5564 (0.4895)	grad_norm 2.4156 (3.1691)	mem 20675MB
[2025-04-02 19:18:24 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][136/234]	eta 0:01:26 lr 0.001063	time 0.8766 (0.8873)	loss 0.5506 (0.4909)	grad_norm 4.1689 (3.1873)	mem 20675MB
[2025-04-02 19:18:26 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][138/234]	eta 0:01:25 lr 0.001063	time 0.8772 (0.8872)	loss 0.5308 (0.4919)	grad_norm 2.9966 (3.1874)	mem 20675MB
[2025-04-02 19:18:27 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][140/234]	eta 0:01:23 lr 0.001062	time 0.8771 (0.8871)	loss 0.3611 (0.4905)	grad_norm 3.7266 (3.1907)	mem 20675MB
[2025-04-02 19:18:29 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][142/234]	eta 0:01:21 lr 0.001062	time 0.8774 (0.8870)	loss 0.5443 (0.4914)	grad_norm 1.8491 (3.1918)	mem 20675MB
[2025-04-02 19:18:31 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][144/234]	eta 0:01:19 lr 0.001062	time 0.8765 (0.8868)	loss 0.4578 (0.4913)	grad_norm 3.0300 (3.1836)	mem 20675MB
[2025-04-02 19:18:33 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][146/234]	eta 0:01:18 lr 0.001061	time 0.8772 (0.8867)	loss 0.4310 (0.4903)	grad_norm 3.6523 (3.1895)	mem 20675MB
[2025-04-02 19:18:34 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][148/234]	eta 0:01:16 lr 0.001061	time 0.8775 (0.8866)	loss 0.4810 (0.4905)	grad_norm 5.6051 (3.2119)	mem 20675MB
[2025-04-02 19:18:36 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][150/234]	eta 0:01:14 lr 0.001060	time 0.8771 (0.8865)	loss 0.4464 (0.4901)	grad_norm 3.7652 (3.2068)	mem 20675MB
[2025-04-02 19:18:38 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][152/234]	eta 0:01:12 lr 0.001060	time 0.8765 (0.8864)	loss 0.5327 (0.4907)	grad_norm 5.6272 (3.2166)	mem 20675MB
[2025-04-02 19:18:40 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][154/234]	eta 0:01:10 lr 0.001060	time 0.8764 (0.8862)	loss 0.5742 (0.4916)	grad_norm 4.2800 (3.2297)	mem 20675MB
[2025-04-02 19:18:41 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][156/234]	eta 0:01:09 lr 0.001059	time 0.8772 (0.8861)	loss 0.5714 (0.4921)	grad_norm 2.7049 (3.2176)	mem 20675MB
[2025-04-02 19:18:43 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][158/234]	eta 0:01:07 lr 0.001059	time 0.8771 (0.8860)	loss 0.6159 (0.4933)	grad_norm 7.0400 (3.2424)	mem 20675MB
[2025-04-02 19:18:45 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][160/234]	eta 0:01:05 lr 0.001058	time 0.8767 (0.8859)	loss 0.3739 (0.4934)	grad_norm 2.9376 (3.2505)	mem 20675MB
[2025-04-02 19:18:47 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][162/234]	eta 0:01:03 lr 0.001058	time 0.8776 (0.8859)	loss 0.4867 (0.4937)	grad_norm 2.1872 (3.2340)	mem 20675MB
[2025-04-02 19:18:49 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][164/234]	eta 0:01:02 lr 0.001058	time 0.8791 (0.8858)	loss 0.5620 (0.4940)	grad_norm 8.2187 (3.2569)	mem 20675MB
[2025-04-02 19:18:50 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][166/234]	eta 0:01:00 lr 0.001057	time 0.8776 (0.8857)	loss 0.4523 (0.4938)	grad_norm 5.7129 (3.2809)	mem 20675MB
[2025-04-02 19:18:52 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][168/234]	eta 0:00:58 lr 0.001057	time 0.8773 (0.8856)	loss 0.5800 (0.4943)	grad_norm 3.1762 (3.2751)	mem 20675MB
[2025-04-02 19:18:54 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][170/234]	eta 0:00:56 lr 0.001056	time 0.8767 (0.8855)	loss 0.5561 (0.4948)	grad_norm 7.1448 (3.2915)	mem 20675MB
[2025-04-02 19:18:56 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][172/234]	eta 0:00:54 lr 0.001056	time 0.8770 (0.8854)	loss 0.3939 (0.4941)	grad_norm 3.5574 (3.2989)	mem 20675MB
[2025-04-02 19:18:57 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][174/234]	eta 0:00:53 lr 0.001056	time 0.8775 (0.8853)	loss 0.5070 (0.4947)	grad_norm 2.3567 (3.2912)	mem 20675MB
[2025-04-02 19:18:59 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][176/234]	eta 0:00:51 lr 0.001055	time 0.8766 (0.8852)	loss 0.4737 (0.4942)	grad_norm 2.0535 (3.2827)	mem 20675MB
[2025-04-02 19:19:01 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][178/234]	eta 0:00:49 lr 0.001055	time 0.8770 (0.8852)	loss 0.5304 (0.4942)	grad_norm 2.5355 (3.2805)	mem 20675MB
[2025-04-02 19:19:03 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][180/234]	eta 0:00:47 lr 0.001054	time 0.8779 (0.8851)	loss 0.4639 (0.4939)	grad_norm 2.4434 (3.2713)	mem 20675MB
[2025-04-02 19:19:04 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][182/234]	eta 0:00:46 lr 0.001054	time 0.8780 (0.8850)	loss 0.5577 (0.4946)	grad_norm 1.9821 (3.2545)	mem 20675MB
[2025-04-02 19:19:06 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][184/234]	eta 0:00:44 lr 0.001054	time 0.8768 (0.8849)	loss 0.5449 (0.4944)	grad_norm 2.2541 (3.2469)	mem 20675MB
[2025-04-02 19:19:08 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][186/234]	eta 0:00:42 lr 0.001053	time 0.8771 (0.8849)	loss 0.5514 (0.4950)	grad_norm 2.2987 (3.2467)	mem 20675MB
[2025-04-02 19:19:10 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][188/234]	eta 0:00:40 lr 0.001053	time 0.8767 (0.8848)	loss 0.4288 (0.4949)	grad_norm 2.4137 (3.2404)	mem 20675MB
[2025-04-02 19:19:11 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][190/234]	eta 0:00:38 lr 0.001052	time 0.8763 (0.8847)	loss 0.5138 (0.4951)	grad_norm 2.9305 (3.2358)	mem 20675MB
[2025-04-02 19:19:13 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][192/234]	eta 0:00:37 lr 0.001052	time 0.8767 (0.8846)	loss 0.5174 (0.4955)	grad_norm 2.3494 (3.2277)	mem 20675MB
[2025-04-02 19:19:15 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][194/234]	eta 0:00:35 lr 0.001051	time 0.8767 (0.8846)	loss 0.4787 (0.4956)	grad_norm 1.8462 (3.2173)	mem 20675MB
[2025-04-02 19:19:17 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][196/234]	eta 0:00:33 lr 0.001051	time 0.8763 (0.8845)	loss 0.5135 (0.4954)	grad_norm 1.7326 (3.2135)	mem 20675MB
[2025-04-02 19:19:18 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][198/234]	eta 0:00:31 lr 0.001051	time 0.8769 (0.8844)	loss 0.4828 (0.4950)	grad_norm 2.2485 (3.2177)	mem 20675MB
[2025-04-02 19:19:20 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][200/234]	eta 0:00:30 lr 0.001050	time 0.8767 (0.8844)	loss 0.4890 (0.4946)	grad_norm 2.2594 (3.2108)	mem 20675MB
[2025-04-02 19:19:22 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][202/234]	eta 0:00:28 lr 0.001050	time 0.8770 (0.8843)	loss 0.4131 (0.4949)	grad_norm 3.8954 (3.2235)	mem 20675MB
[2025-04-02 19:19:24 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][204/234]	eta 0:00:26 lr 0.001049	time 0.8768 (0.8842)	loss 0.4809 (0.4944)	grad_norm 2.9290 (3.2263)	mem 20675MB
[2025-04-02 19:19:25 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][206/234]	eta 0:00:24 lr 0.001049	time 0.8774 (0.8842)	loss 0.4205 (0.4943)	grad_norm 2.6444 (3.2239)	mem 20675MB
[2025-04-02 19:19:27 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][208/234]	eta 0:00:22 lr 0.001049	time 0.8764 (0.8841)	loss 0.3826 (0.4937)	grad_norm 2.8280 (3.2191)	mem 20675MB
[2025-04-02 19:19:29 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][210/234]	eta 0:00:21 lr 0.001048	time 0.8780 (0.8841)	loss 0.6035 (0.4941)	grad_norm 2.6806 (3.2110)	mem 20675MB
[2025-04-02 19:19:31 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][212/234]	eta 0:00:19 lr 0.001048	time 0.8778 (0.8840)	loss 0.3677 (0.4933)	grad_norm 3.1169 (3.2083)	mem 20675MB
[2025-04-02 19:19:32 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][214/234]	eta 0:00:17 lr 0.001047	time 0.8775 (0.8839)	loss 0.5636 (0.4932)	grad_norm 2.8764 (3.2020)	mem 20675MB
[2025-04-02 19:19:34 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][216/234]	eta 0:00:15 lr 0.001047	time 0.8787 (0.8839)	loss 0.3929 (0.4921)	grad_norm 4.0602 (3.2191)	mem 20675MB
[2025-04-02 19:19:36 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][218/234]	eta 0:00:14 lr 0.001047	time 0.8788 (0.8839)	loss 0.4412 (0.4922)	grad_norm 2.9878 (3.2182)	mem 20675MB
[2025-04-02 19:19:38 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][220/234]	eta 0:00:12 lr 0.001046	time 0.8781 (0.8838)	loss 0.4874 (0.4924)	grad_norm 2.6770 (3.2143)	mem 20675MB
[2025-04-02 19:19:39 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][222/234]	eta 0:00:10 lr 0.001046	time 0.8777 (0.8838)	loss 0.5680 (0.4926)	grad_norm 3.9949 (3.2134)	mem 20675MB
[2025-04-02 19:19:41 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][224/234]	eta 0:00:08 lr 0.001045	time 0.8767 (0.8837)	loss 0.4363 (0.4926)	grad_norm 2.9622 (3.2089)	mem 20675MB
[2025-04-02 19:19:43 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][226/234]	eta 0:00:07 lr 0.001045	time 0.8771 (0.8837)	loss 0.5878 (0.4925)	grad_norm 3.7676 (3.2147)	mem 20675MB
[2025-04-02 19:19:45 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][228/234]	eta 0:00:05 lr 0.001044	time 0.8764 (0.8836)	loss 0.3753 (0.4916)	grad_norm 2.4476 (3.2098)	mem 20675MB
[2025-04-02 19:19:46 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][230/234]	eta 0:00:03 lr 0.001044	time 0.8762 (0.8835)	loss 0.3647 (0.4912)	grad_norm 5.2320 (3.2163)	mem 20675MB
[2025-04-02 19:19:48 simmim_finetune] (main_finetune.py 252): INFO Train: [7/30][232/234]	eta 0:00:01 lr 0.001044	time 0.8772 (0.8835)	loss 0.5383 (0.4919)	grad_norm 1.8184 (3.2103)	mem 20675MB
[2025-04-02 19:19:49 simmim_finetune] (main_finetune.py 260): INFO EPOCH 7 training takes 0:03:26
[2025-04-02 19:19:50 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.075 (1.075)	Loss 0.8786 (0.8786)	Acc@1 53.125 (53.125)	Mem 20675MB
[2025-04-02 19:19:50 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 63.536
[2025-04-02 19:19:50 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 63.5%
[2025-04-02 19:19:50 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 63.54%
[2025-04-02 19:19:50 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.899035143289772e-06, 3.899035143289772e-06, 5.976267929890767e-06, 5.976267929890767e-06, 9.17201067850768e-06, 9.17201067850768e-06, 1.4088537984072165e-05, 1.4088537984072165e-05, 2.165242614647906e-05, 2.165242614647906e-05, 3.32891771655666e-05, 3.32891771655666e-05, 5.119187104108588e-05, 5.119187104108588e-05, 7.873447700342323e-05, 7.873447700342323e-05, 0.0001211077169454807, 0.0001211077169454807, 0.00018629731685633838, 0.00018629731685633838, 0.0002865890090268885, 0.0002865890090268885, 0.00044088392005850425, 0.00044088392005850425, 0.0006782607062609901, 0.0006782607062609901, 0.001043455761957122, 0.001043455761957122]
[2025-04-02 19:19:53 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][0/234]	eta 0:08:12 lr 0.001043	time 2.1029 (2.1029)	loss 0.5789 (0.5789)	grad_norm 3.3950 (3.3950)	mem 20675MB
[2025-04-02 19:19:54 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][2/234]	eta 0:04:58 lr 0.001043	time 0.8782 (1.2871)	loss 0.5570 (0.5748)	grad_norm 1.9323 (3.6244)	mem 20675MB
[2025-04-02 19:19:56 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][4/234]	eta 0:04:18 lr 0.001042	time 0.8776 (1.1237)	loss 0.5483 (0.5636)	grad_norm 1.9458 (2.9384)	mem 20675MB
[2025-04-02 19:19:58 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][6/234]	eta 0:04:00 lr 0.001042	time 0.8775 (1.0536)	loss 0.5490 (0.5447)	grad_norm 2.2016 (2.7172)	mem 20675MB
[2025-04-02 19:20:00 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][8/234]	eta 0:03:49 lr 0.001042	time 0.8777 (1.0146)	loss 0.4867 (0.5425)	grad_norm 2.4030 (2.6569)	mem 20675MB
[2025-04-02 19:20:01 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][10/234]	eta 0:03:41 lr 0.001041	time 0.8780 (0.9899)	loss 0.3860 (0.5263)	grad_norm 3.2884 (2.6790)	mem 20675MB
[2025-04-02 19:20:03 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][12/234]	eta 0:03:35 lr 0.001041	time 0.8789 (0.9729)	loss 0.5691 (0.5280)	grad_norm 1.8898 (2.6161)	mem 20675MB
[2025-04-02 19:20:05 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][14/234]	eta 0:03:31 lr 0.001040	time 0.8784 (0.9604)	loss 0.4855 (0.5214)	grad_norm 2.4488 (2.5955)	mem 20675MB
[2025-04-02 19:20:07 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][16/234]	eta 0:03:27 lr 0.001040	time 0.8775 (0.9508)	loss 0.3892 (0.5114)	grad_norm 5.3562 (2.7202)	mem 20675MB
[2025-04-02 19:20:08 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][18/234]	eta 0:03:23 lr 0.001039	time 0.8776 (0.9431)	loss 0.3925 (0.4988)	grad_norm 5.2946 (2.8554)	mem 20675MB
[2025-04-02 19:20:10 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][20/234]	eta 0:03:20 lr 0.001039	time 0.8778 (0.9369)	loss 0.5578 (0.4954)	grad_norm 3.7701 (2.9450)	mem 20675MB
[2025-04-02 19:20:12 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][22/234]	eta 0:03:17 lr 0.001039	time 0.8778 (0.9319)	loss 0.4807 (0.4904)	grad_norm 2.9842 (2.9627)	mem 20675MB
[2025-04-02 19:20:14 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][24/234]	eta 0:03:14 lr 0.001038	time 0.8785 (0.9277)	loss 0.3708 (0.4877)	grad_norm 2.8626 (2.9275)	mem 20675MB
[2025-04-02 19:20:15 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][26/234]	eta 0:03:12 lr 0.001038	time 0.8779 (0.9240)	loss 0.4243 (0.4804)	grad_norm 2.4767 (2.9121)	mem 20675MB
[2025-04-02 19:20:17 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][28/234]	eta 0:03:09 lr 0.001037	time 0.8776 (0.9209)	loss 0.5605 (0.4826)	grad_norm 2.2084 (2.8742)	mem 20675MB
[2025-04-02 19:20:19 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][30/234]	eta 0:03:07 lr 0.001037	time 0.8776 (0.9181)	loss 0.4984 (0.4838)	grad_norm 2.5287 (2.8509)	mem 20675MB
[2025-04-02 19:20:21 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][32/234]	eta 0:03:04 lr 0.001037	time 0.8769 (0.9157)	loss 0.5608 (0.4870)	grad_norm 3.8090 (2.8662)	mem 20675MB
[2025-04-02 19:20:22 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][34/234]	eta 0:03:02 lr 0.001036	time 0.8769 (0.9135)	loss 0.3993 (0.4847)	grad_norm 3.0725 (2.8460)	mem 20675MB
[2025-04-02 19:20:24 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][36/234]	eta 0:03:00 lr 0.001036	time 0.8764 (0.9116)	loss 0.4533 (0.4818)	grad_norm 2.9879 (2.8459)	mem 20675MB
[2025-04-02 19:20:26 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][38/234]	eta 0:02:58 lr 0.001035	time 0.8777 (0.9098)	loss 0.5239 (0.4827)	grad_norm 2.9629 (2.8662)	mem 20675MB
[2025-04-02 19:20:28 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][40/234]	eta 0:02:56 lr 0.001035	time 0.8778 (0.9083)	loss 0.4349 (0.4826)	grad_norm 3.2562 (2.8919)	mem 20675MB
[2025-04-02 19:20:29 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][42/234]	eta 0:02:54 lr 0.001034	time 0.8778 (0.9069)	loss 0.3574 (0.4780)	grad_norm 4.4535 (2.9846)	mem 20675MB
[2025-04-02 19:20:31 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][44/234]	eta 0:02:52 lr 0.001034	time 0.8771 (0.9056)	loss 0.4184 (0.4769)	grad_norm 3.2712 (3.0149)	mem 20675MB
[2025-04-02 19:20:33 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][46/234]	eta 0:02:50 lr 0.001034	time 0.8770 (0.9045)	loss 0.5643 (0.4750)	grad_norm 4.6500 (3.0400)	mem 20675MB
[2025-04-02 19:20:35 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][48/234]	eta 0:02:48 lr 0.001033	time 0.8767 (0.9034)	loss 0.4451 (0.4744)	grad_norm 2.7441 (3.0488)	mem 20675MB
[2025-04-02 19:20:37 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][50/234]	eta 0:02:46 lr 0.001033	time 0.8778 (0.9024)	loss 0.4589 (0.4742)	grad_norm 4.8629 (3.1011)	mem 20675MB
[2025-04-02 19:20:38 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][52/234]	eta 0:02:44 lr 0.001032	time 0.8776 (0.9015)	loss 0.5684 (0.4753)	grad_norm 2.4694 (3.0736)	mem 20675MB
[2025-04-02 19:20:40 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][54/234]	eta 0:02:42 lr 0.001032	time 0.8778 (0.9006)	loss 0.4107 (0.4715)	grad_norm 2.5783 (3.0579)	mem 20675MB
[2025-04-02 19:20:42 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][56/234]	eta 0:02:40 lr 0.001031	time 0.8773 (0.8999)	loss 0.5246 (0.4734)	grad_norm 1.8443 (3.0238)	mem 20675MB
[2025-04-02 19:20:44 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][58/234]	eta 0:02:38 lr 0.001031	time 0.8772 (0.8991)	loss 0.4470 (0.4734)	grad_norm 2.7118 (3.0063)	mem 20675MB
[2025-04-02 19:20:45 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][60/234]	eta 0:02:36 lr 0.001031	time 0.8772 (0.8984)	loss 0.4646 (0.4718)	grad_norm 2.8554 (3.0211)	mem 20675MB
[2025-04-02 19:20:47 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][62/234]	eta 0:02:34 lr 0.001030	time 0.8771 (0.8978)	loss 0.5426 (0.4737)	grad_norm 3.5081 (3.0211)	mem 20675MB
[2025-04-02 19:20:49 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][64/234]	eta 0:02:32 lr 0.001030	time 0.8775 (0.8972)	loss 0.5725 (0.4756)	grad_norm 2.8615 (3.0247)	mem 20675MB
[2025-04-02 19:20:51 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][66/234]	eta 0:02:30 lr 0.001029	time 0.8772 (0.8966)	loss 0.4406 (0.4748)	grad_norm 3.0158 (3.0289)	mem 20675MB
[2025-04-02 19:20:52 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][68/234]	eta 0:02:28 lr 0.001029	time 0.8773 (0.8960)	loss 0.5310 (0.4751)	grad_norm 2.8520 (3.0369)	mem 20675MB
[2025-04-02 19:20:54 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][70/234]	eta 0:02:26 lr 0.001028	time 0.8764 (0.8955)	loss 0.4791 (0.4763)	grad_norm 2.0912 (3.0262)	mem 20675MB
[2025-04-02 19:20:56 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][72/234]	eta 0:02:24 lr 0.001028	time 0.8770 (0.8951)	loss 0.5529 (0.4774)	grad_norm 2.3723 (3.0138)	mem 20675MB
[2025-04-02 19:20:58 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][74/234]	eta 0:02:23 lr 0.001028	time 0.8771 (0.8946)	loss 0.3773 (0.4756)	grad_norm 3.4152 (3.0057)	mem 20675MB
[2025-04-02 19:20:59 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][76/234]	eta 0:02:21 lr 0.001027	time 0.8771 (0.8942)	loss 0.4924 (0.4744)	grad_norm 2.4308 (3.0152)	mem 20675MB
[2025-04-02 19:21:01 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][78/234]	eta 0:02:19 lr 0.001027	time 0.8770 (0.8938)	loss 0.4583 (0.4756)	grad_norm 2.0987 (3.0269)	mem 20675MB
[2025-04-02 19:21:03 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][80/234]	eta 0:02:17 lr 0.001026	time 0.8772 (0.8934)	loss 0.3146 (0.4739)	grad_norm 3.6299 (3.0471)	mem 20675MB
[2025-04-02 19:21:05 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][82/234]	eta 0:02:15 lr 0.001026	time 0.8785 (0.8930)	loss 0.4994 (0.4738)	grad_norm 6.0182 (3.0818)	mem 20675MB
[2025-04-02 19:21:06 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][84/234]	eta 0:02:13 lr 0.001026	time 0.8782 (0.8927)	loss 0.5616 (0.4773)	grad_norm 3.3104 (3.1144)	mem 20675MB
[2025-04-02 19:21:08 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][86/234]	eta 0:02:12 lr 0.001025	time 0.8799 (0.8924)	loss 0.5123 (0.4782)	grad_norm 2.9002 (3.0976)	mem 20675MB
[2025-04-02 19:21:10 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][88/234]	eta 0:02:10 lr 0.001025	time 0.8780 (0.8921)	loss 0.5076 (0.4779)	grad_norm 3.5469 (3.1028)	mem 20675MB
[2025-04-02 19:21:12 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][90/234]	eta 0:02:08 lr 0.001024	time 0.8782 (0.8919)	loss 0.3614 (0.4778)	grad_norm 4.1392 (3.1065)	mem 20675MB
[2025-04-02 19:21:13 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][92/234]	eta 0:02:06 lr 0.001024	time 0.8781 (0.8916)	loss 0.5193 (0.4774)	grad_norm 1.7315 (3.0922)	mem 20675MB
[2025-04-02 19:21:15 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][94/234]	eta 0:02:04 lr 0.001023	time 0.8786 (0.8913)	loss 0.5618 (0.4775)	grad_norm 3.1122 (3.1060)	mem 20675MB
[2025-04-02 19:21:17 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][96/234]	eta 0:02:02 lr 0.001023	time 0.8781 (0.8911)	loss 0.4154 (0.4761)	grad_norm 2.6211 (3.1016)	mem 20675MB
[2025-04-02 19:21:19 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][98/234]	eta 0:02:01 lr 0.001022	time 0.8776 (0.8908)	loss 0.4986 (0.4768)	grad_norm 4.7670 (3.1279)	mem 20675MB
[2025-04-02 19:21:20 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][100/234]	eta 0:01:59 lr 0.001022	time 0.8765 (0.8905)	loss 0.4814 (0.4777)	grad_norm 4.8751 (3.1446)	mem 20675MB
[2025-04-02 19:21:22 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][102/234]	eta 0:01:57 lr 0.001022	time 0.8764 (0.8903)	loss 0.5148 (0.4791)	grad_norm 4.1667 (3.1577)	mem 20675MB
[2025-04-02 19:21:24 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][104/234]	eta 0:01:55 lr 0.001021	time 0.8765 (0.8900)	loss 0.5693 (0.4802)	grad_norm 3.0825 (3.1510)	mem 20675MB
[2025-04-02 19:21:26 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][106/234]	eta 0:01:53 lr 0.001021	time 0.8773 (0.8898)	loss 0.3320 (0.4801)	grad_norm 2.8670 (3.1409)	mem 20675MB
[2025-04-02 19:21:27 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][108/234]	eta 0:01:52 lr 0.001020	time 0.8779 (0.8896)	loss 0.3849 (0.4778)	grad_norm 3.2848 (3.1401)	mem 20675MB
[2025-04-02 19:21:29 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][110/234]	eta 0:01:50 lr 0.001020	time 0.8790 (0.8894)	loss 0.5561 (0.4774)	grad_norm 4.4414 (3.1569)	mem 20675MB
[2025-04-02 19:21:31 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][112/234]	eta 0:01:48 lr 0.001019	time 0.8779 (0.8893)	loss 0.5531 (0.4769)	grad_norm 2.1711 (3.1489)	mem 20675MB
[2025-04-02 19:21:33 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][114/234]	eta 0:01:46 lr 0.001019	time 0.8781 (0.8893)	loss 0.4596 (0.4762)	grad_norm 4.7559 (3.1756)	mem 20675MB
[2025-04-02 19:21:35 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][116/234]	eta 0:01:44 lr 0.001019	time 0.8774 (0.8892)	loss 0.5119 (0.4773)	grad_norm 2.9329 (3.1755)	mem 20675MB
[2025-04-02 19:21:36 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][118/234]	eta 0:01:43 lr 0.001018	time 0.8789 (0.8890)	loss 0.5915 (0.4786)	grad_norm 2.6756 (3.1746)	mem 20675MB
[2025-04-02 19:21:38 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][120/234]	eta 0:01:41 lr 0.001018	time 0.8832 (0.8889)	loss 0.4234 (0.4780)	grad_norm 2.3946 (3.1629)	mem 20675MB
[2025-04-02 19:21:40 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][122/234]	eta 0:01:39 lr 0.001017	time 0.8782 (0.8888)	loss 0.3981 (0.4774)	grad_norm 5.1623 (3.1748)	mem 20675MB
[2025-04-02 19:21:42 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][124/234]	eta 0:01:37 lr 0.001017	time 0.8776 (0.8887)	loss 0.5382 (0.4785)	grad_norm 1.5388 (3.1674)	mem 20675MB
[2025-04-02 19:21:43 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][126/234]	eta 0:01:35 lr 0.001016	time 0.8771 (0.8885)	loss 0.6334 (0.4786)	grad_norm 2.6177 (3.1622)	mem 20675MB
[2025-04-02 19:21:45 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][128/234]	eta 0:01:34 lr 0.001016	time 0.8775 (0.8884)	loss 0.5336 (0.4794)	grad_norm 2.0325 (3.1600)	mem 20675MB
[2025-04-02 19:21:47 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][130/234]	eta 0:01:32 lr 0.001016	time 0.8772 (0.8883)	loss 0.5337 (0.4799)	grad_norm 1.8195 (3.1558)	mem 20675MB
[2025-04-02 19:21:49 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][132/234]	eta 0:01:30 lr 0.001015	time 0.8788 (0.8882)	loss 0.5596 (0.4809)	grad_norm 3.9471 (3.1503)	mem 20675MB
[2025-04-02 19:21:50 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][134/234]	eta 0:01:28 lr 0.001015	time 0.8825 (0.8881)	loss 0.5225 (0.4820)	grad_norm 1.3014 (3.1336)	mem 20675MB
[2025-04-02 19:21:52 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][136/234]	eta 0:01:27 lr 0.001014	time 0.8785 (0.8880)	loss 0.5057 (0.4827)	grad_norm 1.5549 (3.1133)	mem 20675MB
[2025-04-02 19:21:54 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][138/234]	eta 0:01:25 lr 0.001014	time 0.8780 (0.8879)	loss 0.6049 (0.4835)	grad_norm 5.1266 (3.1169)	mem 20675MB
[2025-04-02 19:21:56 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][140/234]	eta 0:01:23 lr 0.001013	time 0.8785 (0.8878)	loss 0.4034 (0.4831)	grad_norm 2.2214 (3.1085)	mem 20675MB
[2025-04-02 19:21:57 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][142/234]	eta 0:01:21 lr 0.001013	time 0.8888 (0.8878)	loss 0.3849 (0.4825)	grad_norm 2.6577 (3.0973)	mem 20675MB
[2025-04-02 19:21:59 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][144/234]	eta 0:01:19 lr 0.001012	time 0.8785 (0.8878)	loss 0.4961 (0.4828)	grad_norm 2.2862 (3.1027)	mem 20675MB
[2025-04-02 19:22:01 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][146/234]	eta 0:01:18 lr 0.001012	time 0.8878 (0.8878)	loss 0.4515 (0.4831)	grad_norm 3.3571 (3.1028)	mem 20675MB
[2025-04-02 19:22:03 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][148/234]	eta 0:01:16 lr 0.001012	time 0.8784 (0.8877)	loss 0.4422 (0.4829)	grad_norm 3.5801 (3.1007)	mem 20675MB
[2025-04-02 19:22:05 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][150/234]	eta 0:01:14 lr 0.001011	time 0.8814 (0.8876)	loss 0.5320 (0.4835)	grad_norm 5.4407 (3.1252)	mem 20675MB
[2025-04-02 19:22:06 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][152/234]	eta 0:01:12 lr 0.001011	time 0.8793 (0.8875)	loss 0.4994 (0.4829)	grad_norm 1.9402 (3.1197)	mem 20675MB
[2025-04-02 19:22:08 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][154/234]	eta 0:01:10 lr 0.001010	time 0.8842 (0.8874)	loss 0.5578 (0.4836)	grad_norm 4.6764 (3.1226)	mem 20675MB
[2025-04-02 19:22:10 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][156/234]	eta 0:01:09 lr 0.001010	time 0.8782 (0.8874)	loss 0.5656 (0.4842)	grad_norm 3.7836 (3.1286)	mem 20675MB
[2025-04-02 19:22:12 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][158/234]	eta 0:01:07 lr 0.001009	time 0.8793 (0.8873)	loss 0.5281 (0.4847)	grad_norm 1.4578 (3.1163)	mem 20675MB
[2025-04-02 19:22:13 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][160/234]	eta 0:01:05 lr 0.001009	time 0.8784 (0.8872)	loss 0.5675 (0.4855)	grad_norm 2.0117 (3.1099)	mem 20675MB
[2025-04-02 19:22:15 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][162/234]	eta 0:01:03 lr 0.001009	time 0.8783 (0.8871)	loss 0.4040 (0.4847)	grad_norm 3.4777 (3.1146)	mem 20675MB
[2025-04-02 19:22:17 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][164/234]	eta 0:01:02 lr 0.001008	time 0.8794 (0.8870)	loss 0.5516 (0.4853)	grad_norm 3.6250 (3.1148)	mem 20675MB
[2025-04-02 19:22:19 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][166/234]	eta 0:01:00 lr 0.001008	time 0.8767 (0.8870)	loss 0.5736 (0.4859)	grad_norm 3.4032 (3.1122)	mem 20675MB
[2025-04-02 19:22:20 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][168/234]	eta 0:00:58 lr 0.001007	time 0.8766 (0.8869)	loss 0.5841 (0.4865)	grad_norm 4.2863 (3.1146)	mem 20675MB
[2025-04-02 19:22:22 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][170/234]	eta 0:00:56 lr 0.001007	time 0.8813 (0.8868)	loss 0.4714 (0.4863)	grad_norm 2.1618 (3.1052)	mem 20675MB
[2025-04-02 19:22:24 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][172/234]	eta 0:00:54 lr 0.001006	time 0.8821 (0.8868)	loss 0.5547 (0.4861)	grad_norm 2.5057 (3.0987)	mem 20675MB
[2025-04-02 19:22:26 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][174/234]	eta 0:00:53 lr 0.001006	time 0.8865 (0.8867)	loss 0.5402 (0.4861)	grad_norm 3.4815 (3.1052)	mem 20675MB
[2025-04-02 19:22:27 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][176/234]	eta 0:00:51 lr 0.001005	time 0.8787 (0.8866)	loss 0.4507 (0.4865)	grad_norm 3.1202 (3.1135)	mem 20675MB
[2025-04-02 19:22:29 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][178/234]	eta 0:00:49 lr 0.001005	time 0.8786 (0.8865)	loss 0.4277 (0.4864)	grad_norm 2.7002 (3.1103)	mem 20675MB
[2025-04-02 19:22:31 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][180/234]	eta 0:00:47 lr 0.001005	time 0.8822 (0.8865)	loss 0.6318 (0.4873)	grad_norm 6.5119 (3.1258)	mem 20675MB
[2025-04-02 19:22:33 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][182/234]	eta 0:00:46 lr 0.001004	time 0.8779 (0.8864)	loss 0.4268 (0.4874)	grad_norm 3.1270 (3.1231)	mem 20675MB
[2025-04-02 19:22:34 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][184/234]	eta 0:00:44 lr 0.001004	time 0.8777 (0.8864)	loss 0.4217 (0.4868)	grad_norm 3.0220 (3.1178)	mem 20675MB
[2025-04-02 19:22:36 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][186/234]	eta 0:00:42 lr 0.001003	time 0.8790 (0.8863)	loss 0.4810 (0.4865)	grad_norm 3.1675 (3.1103)	mem 20675MB
[2025-04-02 19:22:38 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][188/234]	eta 0:00:40 lr 0.001003	time 0.8787 (0.8862)	loss 0.4762 (0.4863)	grad_norm 2.5947 (3.1160)	mem 20675MB
[2025-04-02 19:22:40 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][190/234]	eta 0:00:38 lr 0.001002	time 0.8789 (0.8862)	loss 0.4797 (0.4856)	grad_norm 1.9060 (3.1068)	mem 20675MB
[2025-04-02 19:22:42 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][192/234]	eta 0:00:37 lr 0.001002	time 0.8810 (0.8861)	loss 0.6108 (0.4870)	grad_norm 4.2657 (3.1177)	mem 20675MB
[2025-04-02 19:22:43 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][194/234]	eta 0:00:35 lr 0.001001	time 0.8818 (0.8861)	loss 0.4035 (0.4866)	grad_norm 2.8698 (3.1141)	mem 20675MB
[2025-04-02 19:22:45 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][196/234]	eta 0:00:33 lr 0.001001	time 0.8793 (0.8860)	loss 0.5236 (0.4870)	grad_norm 2.3099 (3.1090)	mem 20675MB
[2025-04-02 19:22:47 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][198/234]	eta 0:00:31 lr 0.001001	time 0.8784 (0.8860)	loss 0.5580 (0.4875)	grad_norm 1.9742 (3.0996)	mem 20675MB
[2025-04-02 19:22:49 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][200/234]	eta 0:00:30 lr 0.001000	time 0.8916 (0.8860)	loss 0.4863 (0.4874)	grad_norm 2.3661 (3.0919)	mem 20675MB
[2025-04-02 19:22:50 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][202/234]	eta 0:00:28 lr 0.001000	time 0.8784 (0.8859)	loss 0.4755 (0.4873)	grad_norm 2.6325 (3.0851)	mem 20675MB
[2025-04-02 19:22:52 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][204/234]	eta 0:00:26 lr 0.000999	time 0.8794 (0.8858)	loss 0.4837 (0.4874)	grad_norm 2.3625 (3.0753)	mem 20675MB
[2025-04-02 19:22:54 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][206/234]	eta 0:00:24 lr 0.000999	time 0.8839 (0.8858)	loss 0.5535 (0.4877)	grad_norm 2.2639 (3.0684)	mem 20675MB
[2025-04-02 19:22:56 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][208/234]	eta 0:00:23 lr 0.000998	time 0.8831 (0.8858)	loss 0.3687 (0.4868)	grad_norm 2.7907 (3.0661)	mem 20675MB
[2025-04-02 19:22:57 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][210/234]	eta 0:00:21 lr 0.000998	time 0.8918 (0.8858)	loss 0.4894 (0.4862)	grad_norm 3.9113 (3.0710)	mem 20675MB
[2025-04-02 19:22:59 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][212/234]	eta 0:00:19 lr 0.000997	time 0.8773 (0.8857)	loss 0.5234 (0.4867)	grad_norm 2.5447 (3.0702)	mem 20675MB
[2025-04-02 19:23:01 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][214/234]	eta 0:00:17 lr 0.000997	time 0.8773 (0.8856)	loss 0.5862 (0.4877)	grad_norm 3.6192 (3.0729)	mem 20675MB
[2025-04-02 19:23:03 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][216/234]	eta 0:00:15 lr 0.000996	time 0.8778 (0.8856)	loss 0.5404 (0.4880)	grad_norm 3.5131 (3.0691)	mem 20675MB
[2025-04-02 19:23:04 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][218/234]	eta 0:00:14 lr 0.000996	time 0.8916 (0.8856)	loss 0.4823 (0.4877)	grad_norm 1.7402 (3.0593)	mem 20675MB
[2025-04-02 19:23:06 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][220/234]	eta 0:00:12 lr 0.000996	time 0.8799 (0.8855)	loss 0.5424 (0.4879)	grad_norm 2.0923 (3.0606)	mem 20675MB
[2025-04-02 19:23:08 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][222/234]	eta 0:00:10 lr 0.000995	time 0.8772 (0.8855)	loss 0.5671 (0.4878)	grad_norm 2.0115 (3.0580)	mem 20675MB
[2025-04-02 19:23:10 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][224/234]	eta 0:00:08 lr 0.000995	time 0.8765 (0.8854)	loss 0.4454 (0.4881)	grad_norm 2.9570 (3.0537)	mem 20675MB
[2025-04-02 19:23:11 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][226/234]	eta 0:00:07 lr 0.000994	time 0.8768 (0.8854)	loss 0.5141 (0.4880)	grad_norm 2.4201 (3.0489)	mem 20675MB
[2025-04-02 19:23:13 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][228/234]	eta 0:00:05 lr 0.000994	time 0.8973 (0.8854)	loss 0.5112 (0.4875)	grad_norm 2.7161 (3.0477)	mem 20675MB
[2025-04-02 19:23:15 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][230/234]	eta 0:00:03 lr 0.000993	time 0.8770 (0.8853)	loss 0.5626 (0.4881)	grad_norm 2.5260 (3.0464)	mem 20675MB
[2025-04-02 19:23:17 simmim_finetune] (main_finetune.py 252): INFO Train: [8/30][232/234]	eta 0:00:01 lr 0.000993	time 0.8769 (0.8853)	loss 0.5337 (0.4881)	grad_norm 3.2735 (3.0453)	mem 20675MB
[2025-04-02 19:23:18 simmim_finetune] (main_finetune.py 260): INFO EPOCH 8 training takes 0:03:27
[2025-04-02 19:23:19 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.517 (1.517)	Loss 0.8409 (0.8409)	Acc@1 60.156 (60.156)	Mem 20675MB
[2025-04-02 19:23:20 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 68.508
[2025-04-02 19:23:20 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 68.5%
[2025-04-02 19:23:20 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 68.51%
[2025-04-02 19:23:20 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.721298688063185e-06, 3.721298688063185e-06, 5.697354046200705e-06, 5.697354046200705e-06, 8.73743921256612e-06, 8.73743921256612e-06, 1.3414493314666759e-05, 1.3414493314666759e-05, 2.0609961164052356e-05, 2.0609961164052356e-05, 3.1679911701568664e-05, 3.1679911701568664e-05, 4.8710604836209125e-05, 4.8710604836209125e-05, 7.491167119719445e-05, 7.491167119719445e-05, 0.0001152210040602488, 0.0001152210040602488, 0.00017723536231110167, 0.00017723536231110167, 0.0002726420673124137, 0.0002726420673124137, 0.0004194216134682784, 0.0004194216134682784, 0.0006452362998619165, 0.0006452362998619165, 0.0009926435096982826, 0.0009926435096982826]
[2025-04-02 19:23:23 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][0/234]	eta 0:11:44 lr 0.000992	time 3.0105 (3.0105)	loss 0.5827 (0.5827)	grad_norm 2.4706 (2.4706)	mem 20675MB
[2025-04-02 19:23:24 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][2/234]	eta 0:06:10 lr 0.000992	time 0.8770 (1.5977)	loss 0.5383 (0.4899)	grad_norm 3.1846 (2.6051)	mem 20675MB
[2025-04-02 19:23:26 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][4/234]	eta 0:05:01 lr 0.000992	time 0.8768 (1.3101)	loss 0.5498 (0.4994)	grad_norm 1.9672 (2.3757)	mem 20675MB
[2025-04-02 19:23:28 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][6/234]	eta 0:04:30 lr 0.000991	time 0.8774 (1.1867)	loss 0.3601 (0.4698)	grad_norm 1.9512 (2.4868)	mem 20675MB
[2025-04-02 19:23:30 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][8/234]	eta 0:04:12 lr 0.000991	time 0.8827 (1.1190)	loss 0.5344 (0.4866)	grad_norm 2.3042 (2.4310)	mem 20675MB
[2025-04-02 19:23:31 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][10/234]	eta 0:04:00 lr 0.000990	time 0.8780 (1.0758)	loss 0.5793 (0.5055)	grad_norm 2.2542 (2.4066)	mem 20675MB
[2025-04-02 19:23:33 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][12/234]	eta 0:03:52 lr 0.000990	time 0.8845 (1.0462)	loss 0.5088 (0.4981)	grad_norm 2.5733 (2.3776)	mem 20675MB
[2025-04-02 19:23:35 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][14/234]	eta 0:03:45 lr 0.000989	time 0.9241 (1.0270)	loss 0.5304 (0.4919)	grad_norm 2.2707 (2.4248)	mem 20675MB
[2025-04-02 19:23:37 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][16/234]	eta 0:03:40 lr 0.000989	time 0.8819 (1.0097)	loss 0.4658 (0.4899)	grad_norm 2.5067 (2.4433)	mem 20675MB
[2025-04-02 19:23:39 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][18/234]	eta 0:03:35 lr 0.000988	time 0.8852 (0.9963)	loss 0.4753 (0.4877)	grad_norm 2.0598 (2.3948)	mem 20675MB
[2025-04-02 19:23:40 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][20/234]	eta 0:03:30 lr 0.000988	time 0.8778 (0.9850)	loss 0.4875 (0.4902)	grad_norm 2.9568 (2.4580)	mem 20675MB
[2025-04-02 19:23:42 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][22/234]	eta 0:03:26 lr 0.000987	time 0.8763 (0.9758)	loss 0.3726 (0.4837)	grad_norm 2.2853 (2.4815)	mem 20675MB
[2025-04-02 19:23:44 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][24/234]	eta 0:03:23 lr 0.000987	time 0.8772 (0.9683)	loss 0.5557 (0.4871)	grad_norm 2.6895 (2.4733)	mem 20675MB
[2025-04-02 19:23:46 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][26/234]	eta 0:03:20 lr 0.000987	time 0.8770 (0.9616)	loss 0.5256 (0.4940)	grad_norm 3.8209 (2.6144)	mem 20675MB
[2025-04-02 19:23:47 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][28/234]	eta 0:03:16 lr 0.000986	time 0.8782 (0.9559)	loss 0.5557 (0.4995)	grad_norm 1.6399 (2.5768)	mem 20675MB
[2025-04-02 19:23:49 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][30/234]	eta 0:03:14 lr 0.000986	time 0.8870 (0.9514)	loss 0.5835 (0.5042)	grad_norm 1.6441 (2.5110)	mem 20675MB
[2025-04-02 19:23:51 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][32/234]	eta 0:03:11 lr 0.000985	time 0.8780 (0.9470)	loss 0.4103 (0.5011)	grad_norm 3.9900 (2.5257)	mem 20675MB
[2025-04-02 19:23:53 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][34/234]	eta 0:03:08 lr 0.000985	time 0.8818 (0.9434)	loss 0.4747 (0.5010)	grad_norm 1.9990 (2.5080)	mem 20675MB
[2025-04-02 19:23:54 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][36/234]	eta 0:03:06 lr 0.000984	time 0.8782 (0.9404)	loss 0.4406 (0.5005)	grad_norm 2.5991 (2.5083)	mem 20675MB
[2025-04-02 19:23:56 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][38/234]	eta 0:03:03 lr 0.000984	time 0.8775 (0.9373)	loss 0.5657 (0.5028)	grad_norm 1.4923 (2.5051)	mem 20675MB
[2025-04-02 19:23:58 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][40/234]	eta 0:03:01 lr 0.000983	time 0.8767 (0.9345)	loss 0.4521 (0.5020)	grad_norm 2.6179 (2.4923)	mem 20675MB
[2025-04-02 19:24:00 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][42/234]	eta 0:02:58 lr 0.000983	time 0.8781 (0.9319)	loss 0.3498 (0.4991)	grad_norm 3.8527 (2.5356)	mem 20675MB
[2025-04-02 19:24:01 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][44/234]	eta 0:02:56 lr 0.000982	time 0.8773 (0.9295)	loss 0.5470 (0.4980)	grad_norm 2.9179 (2.5659)	mem 20675MB
[2025-04-02 19:24:03 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][46/234]	eta 0:02:54 lr 0.000982	time 0.9243 (0.9285)	loss 0.4341 (0.4963)	grad_norm 3.3675 (2.5857)	mem 20675MB
[2025-04-02 19:24:05 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][48/234]	eta 0:02:52 lr 0.000981	time 0.8907 (0.9271)	loss 0.5413 (0.4987)	grad_norm 4.4273 (2.6589)	mem 20675MB
[2025-04-02 19:24:07 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][50/234]	eta 0:02:50 lr 0.000981	time 0.8824 (0.9256)	loss 0.5747 (0.5020)	grad_norm 2.1372 (2.6405)	mem 20675MB
[2025-04-02 19:24:09 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][52/234]	eta 0:02:48 lr 0.000981	time 0.8781 (0.9239)	loss 0.3418 (0.5002)	grad_norm 2.7982 (2.6492)	mem 20675MB
[2025-04-02 19:24:10 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][54/234]	eta 0:02:46 lr 0.000980	time 0.8779 (0.9224)	loss 0.4854 (0.4985)	grad_norm 2.2946 (2.6887)	mem 20675MB
[2025-04-02 19:24:12 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][56/234]	eta 0:02:43 lr 0.000980	time 0.8790 (0.9209)	loss 0.5727 (0.4991)	grad_norm 2.2475 (2.6711)	mem 20675MB
[2025-04-02 19:24:14 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][58/234]	eta 0:02:41 lr 0.000979	time 0.8782 (0.9195)	loss 0.4836 (0.4996)	grad_norm 3.4703 (2.6772)	mem 20675MB
[2025-04-02 19:24:16 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][60/234]	eta 0:02:39 lr 0.000979	time 0.8773 (0.9181)	loss 0.4895 (0.4998)	grad_norm 2.4592 (2.6837)	mem 20675MB
[2025-04-02 19:24:17 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][62/234]	eta 0:02:37 lr 0.000978	time 0.8772 (0.9168)	loss 0.3780 (0.4970)	grad_norm 2.5573 (2.6773)	mem 20675MB
[2025-04-02 19:24:19 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][64/234]	eta 0:02:35 lr 0.000978	time 0.8793 (0.9157)	loss 0.6519 (0.5004)	grad_norm 5.3055 (2.7480)	mem 20675MB
[2025-04-02 19:24:21 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][66/234]	eta 0:02:33 lr 0.000977	time 0.8790 (0.9147)	loss 0.3799 (0.4981)	grad_norm 2.7614 (2.7740)	mem 20675MB
[2025-04-02 19:24:23 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][68/234]	eta 0:02:31 lr 0.000977	time 0.8783 (0.9137)	loss 0.4220 (0.4976)	grad_norm 5.6126 (2.8061)	mem 20675MB
[2025-04-02 19:24:24 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][70/234]	eta 0:02:29 lr 0.000976	time 0.8793 (0.9127)	loss 0.5596 (0.4982)	grad_norm 3.9717 (2.8225)	mem 20675MB
[2025-04-02 19:24:26 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][72/234]	eta 0:02:27 lr 0.000976	time 0.8821 (0.9119)	loss 0.5149 (0.4995)	grad_norm 4.1776 (2.8454)	mem 20675MB
[2025-04-02 19:24:28 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][74/234]	eta 0:02:25 lr 0.000975	time 0.8778 (0.9110)	loss 0.4541 (0.4997)	grad_norm 3.7618 (2.8623)	mem 20675MB
[2025-04-02 19:24:30 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][76/234]	eta 0:02:23 lr 0.000975	time 0.8777 (0.9102)	loss 0.4361 (0.4996)	grad_norm 2.6429 (2.8764)	mem 20675MB
[2025-04-02 19:24:31 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][78/234]	eta 0:02:21 lr 0.000975	time 0.8770 (0.9094)	loss 0.5991 (0.4995)	grad_norm 2.3322 (2.8701)	mem 20675MB
[2025-04-02 19:24:33 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][80/234]	eta 0:02:19 lr 0.000974	time 0.8776 (0.9086)	loss 0.5404 (0.5014)	grad_norm 2.1822 (2.8567)	mem 20675MB
[2025-04-02 19:24:35 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][82/234]	eta 0:02:18 lr 0.000974	time 0.9005 (0.9083)	loss 0.4951 (0.5015)	grad_norm 2.2285 (2.8455)	mem 20675MB
[2025-04-02 19:24:37 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][84/234]	eta 0:02:16 lr 0.000973	time 0.8769 (0.9076)	loss 0.5913 (0.5030)	grad_norm 1.7270 (2.8214)	mem 20675MB
[2025-04-02 19:24:39 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][86/234]	eta 0:02:14 lr 0.000973	time 0.8777 (0.9070)	loss 0.5566 (0.5023)	grad_norm 3.1880 (2.8246)	mem 20675MB
[2025-04-02 19:24:40 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][88/234]	eta 0:02:12 lr 0.000972	time 0.8869 (0.9065)	loss 0.4682 (0.5010)	grad_norm 2.7648 (2.8193)	mem 20675MB
[2025-04-02 19:24:42 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][90/234]	eta 0:02:10 lr 0.000972	time 0.8784 (0.9058)	loss 0.4460 (0.4988)	grad_norm 3.5377 (2.8392)	mem 20675MB
[2025-04-02 19:24:44 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][92/234]	eta 0:02:08 lr 0.000971	time 0.8780 (0.9053)	loss 0.3717 (0.4973)	grad_norm 2.5703 (2.8315)	mem 20675MB
[2025-04-02 19:24:46 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][94/234]	eta 0:02:06 lr 0.000971	time 0.8778 (0.9047)	loss 0.5358 (0.4967)	grad_norm 2.6582 (2.8407)	mem 20675MB
[2025-04-02 19:24:47 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][96/234]	eta 0:02:04 lr 0.000970	time 0.8784 (0.9042)	loss 0.5292 (0.4976)	grad_norm 3.7400 (2.8529)	mem 20675MB
[2025-04-02 19:24:49 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][98/234]	eta 0:02:02 lr 0.000970	time 0.8780 (0.9037)	loss 0.3941 (0.4967)	grad_norm 3.5623 (2.8501)	mem 20675MB
[2025-04-02 19:24:51 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][100/234]	eta 0:02:01 lr 0.000969	time 0.8786 (0.9032)	loss 0.4858 (0.4958)	grad_norm 2.4587 (2.8534)	mem 20675MB
[2025-04-02 19:24:53 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][102/234]	eta 0:01:59 lr 0.000969	time 0.8792 (0.9028)	loss 0.6023 (0.4959)	grad_norm 3.0178 (2.8573)	mem 20675MB
[2025-04-02 19:24:54 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][104/234]	eta 0:01:57 lr 0.000968	time 0.8789 (0.9024)	loss 0.4309 (0.4962)	grad_norm 2.8306 (2.8673)	mem 20675MB
[2025-04-02 19:24:56 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][106/234]	eta 0:01:55 lr 0.000968	time 0.8778 (0.9020)	loss 0.4124 (0.4966)	grad_norm 1.9666 (2.8493)	mem 20675MB
[2025-04-02 19:24:58 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][108/234]	eta 0:01:53 lr 0.000968	time 0.8782 (0.9016)	loss 0.4785 (0.4964)	grad_norm 1.8792 (2.8364)	mem 20675MB
[2025-04-02 19:25:00 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][110/234]	eta 0:01:51 lr 0.000967	time 0.8779 (0.9012)	loss 0.5241 (0.4955)	grad_norm 1.7487 (2.8246)	mem 20675MB
[2025-04-02 19:25:01 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][112/234]	eta 0:01:49 lr 0.000967	time 0.8784 (0.9008)	loss 0.3432 (0.4942)	grad_norm 2.9612 (2.8251)	mem 20675MB
[2025-04-02 19:25:03 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][114/234]	eta 0:01:48 lr 0.000966	time 0.8776 (0.9004)	loss 0.3376 (0.4928)	grad_norm 2.5552 (2.8193)	mem 20675MB
[2025-04-02 19:25:05 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][116/234]	eta 0:01:46 lr 0.000966	time 0.8776 (0.9000)	loss 0.4346 (0.4921)	grad_norm 6.1882 (2.8655)	mem 20675MB
[2025-04-02 19:25:07 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][118/234]	eta 0:01:44 lr 0.000965	time 0.8781 (0.8997)	loss 0.3434 (0.4911)	grad_norm 3.4878 (2.8687)	mem 20675MB
[2025-04-02 19:25:08 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][120/234]	eta 0:01:42 lr 0.000965	time 0.8784 (0.8993)	loss 0.4214 (0.4918)	grad_norm 2.4039 (2.8990)	mem 20675MB
[2025-04-02 19:25:10 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][122/234]	eta 0:01:40 lr 0.000964	time 0.8800 (0.8991)	loss 0.4943 (0.4920)	grad_norm 2.3400 (2.8975)	mem 20675MB
[2025-04-02 19:25:12 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][124/234]	eta 0:01:38 lr 0.000964	time 0.8790 (0.8987)	loss 0.4591 (0.4924)	grad_norm 3.0222 (2.9003)	mem 20675MB
[2025-04-02 19:25:14 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][126/234]	eta 0:01:37 lr 0.000963	time 0.8797 (0.8985)	loss 0.5259 (0.4916)	grad_norm 3.0567 (2.9053)	mem 20675MB
[2025-04-02 19:25:15 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][128/234]	eta 0:01:35 lr 0.000963	time 0.8780 (0.8982)	loss 0.5474 (0.4915)	grad_norm 3.5632 (2.9137)	mem 20675MB
[2025-04-02 19:25:17 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][130/234]	eta 0:01:33 lr 0.000962	time 0.8774 (0.8979)	loss 0.4537 (0.4914)	grad_norm 2.4897 (2.9020)	mem 20675MB
[2025-04-02 19:25:19 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][132/234]	eta 0:01:31 lr 0.000962	time 0.8784 (0.8976)	loss 0.5244 (0.4913)	grad_norm 3.2060 (2.9172)	mem 20675MB
[2025-04-02 19:25:21 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][134/234]	eta 0:01:29 lr 0.000961	time 0.8788 (0.8974)	loss 0.4620 (0.4918)	grad_norm 1.5420 (2.9118)	mem 20675MB
[2025-04-02 19:25:23 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][136/234]	eta 0:01:27 lr 0.000961	time 0.8796 (0.8973)	loss 0.4496 (0.4916)	grad_norm 2.2688 (2.9023)	mem 20675MB
[2025-04-02 19:25:24 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][138/234]	eta 0:01:26 lr 0.000961	time 0.8789 (0.8971)	loss 0.4109 (0.4906)	grad_norm 3.6274 (2.8994)	mem 20675MB
[2025-04-02 19:25:26 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][140/234]	eta 0:01:24 lr 0.000960	time 0.8782 (0.8969)	loss 0.5071 (0.4904)	grad_norm 3.4965 (2.9032)	mem 20675MB
[2025-04-02 19:25:28 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][142/234]	eta 0:01:22 lr 0.000960	time 0.8770 (0.8966)	loss 0.4402 (0.4889)	grad_norm 3.7376 (2.9225)	mem 20675MB
[2025-04-02 19:25:30 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][144/234]	eta 0:01:20 lr 0.000959	time 0.8774 (0.8964)	loss 0.5142 (0.4884)	grad_norm 2.2368 (2.9226)	mem 20675MB
[2025-04-02 19:25:31 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][146/234]	eta 0:01:18 lr 0.000959	time 0.8784 (0.8961)	loss 0.4234 (0.4890)	grad_norm 2.8235 (2.9298)	mem 20675MB
[2025-04-02 19:25:33 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][148/234]	eta 0:01:17 lr 0.000958	time 0.8783 (0.8959)	loss 0.4662 (0.4894)	grad_norm 6.0824 (2.9502)	mem 20675MB
[2025-04-02 19:25:35 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][150/234]	eta 0:01:15 lr 0.000958	time 0.8792 (0.8957)	loss 0.6433 (0.4910)	grad_norm 4.2159 (2.9878)	mem 20675MB
[2025-04-02 19:25:37 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][152/234]	eta 0:01:13 lr 0.000957	time 0.8778 (0.8955)	loss 0.3989 (0.4897)	grad_norm 2.3236 (2.9785)	mem 20675MB
[2025-04-02 19:25:38 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][154/234]	eta 0:01:11 lr 0.000957	time 0.8790 (0.8953)	loss 0.4970 (0.4891)	grad_norm 2.6241 (2.9761)	mem 20675MB
[2025-04-02 19:25:40 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][156/234]	eta 0:01:09 lr 0.000956	time 0.8794 (0.8951)	loss 0.4832 (0.4895)	grad_norm 3.2612 (2.9855)	mem 20675MB
[2025-04-02 19:25:42 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][158/234]	eta 0:01:08 lr 0.000956	time 0.8775 (0.8949)	loss 0.5565 (0.4909)	grad_norm 1.7299 (2.9826)	mem 20675MB
[2025-04-02 19:25:44 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][160/234]	eta 0:01:06 lr 0.000955	time 0.8767 (0.8947)	loss 0.4864 (0.4912)	grad_norm 4.8474 (2.9965)	mem 20675MB
[2025-04-02 19:25:45 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][162/234]	eta 0:01:04 lr 0.000955	time 0.8769 (0.8945)	loss 0.4708 (0.4917)	grad_norm 3.9874 (3.0058)	mem 20675MB
[2025-04-02 19:25:47 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][164/234]	eta 0:01:02 lr 0.000954	time 0.8818 (0.8944)	loss 0.4690 (0.4911)	grad_norm 1.9477 (3.0025)	mem 20675MB
[2025-04-02 19:25:49 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][166/234]	eta 0:01:00 lr 0.000954	time 0.8776 (0.8942)	loss 0.5263 (0.4916)	grad_norm 5.0879 (3.0178)	mem 20675MB
[2025-04-02 19:25:51 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][168/234]	eta 0:00:59 lr 0.000953	time 0.8770 (0.8940)	loss 0.5194 (0.4922)	grad_norm 1.7156 (3.0119)	mem 20675MB
[2025-04-02 19:25:52 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][170/234]	eta 0:00:57 lr 0.000953	time 0.8835 (0.8939)	loss 0.5144 (0.4924)	grad_norm 3.3609 (3.0077)	mem 20675MB
[2025-04-02 19:25:54 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][172/234]	eta 0:00:55 lr 0.000952	time 0.8823 (0.8937)	loss 0.3837 (0.4919)	grad_norm 5.0923 (3.0211)	mem 20675MB
[2025-04-02 19:25:56 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][174/234]	eta 0:00:53 lr 0.000952	time 0.8773 (0.8935)	loss 0.6261 (0.4932)	grad_norm 2.2525 (3.0114)	mem 20675MB
[2025-04-02 19:25:58 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][176/234]	eta 0:00:51 lr 0.000952	time 0.8843 (0.8934)	loss 0.4013 (0.4928)	grad_norm 2.3071 (3.0060)	mem 20675MB
[2025-04-02 19:26:00 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][178/234]	eta 0:00:50 lr 0.000951	time 0.8911 (0.8933)	loss 0.4252 (0.4921)	grad_norm 4.7637 (3.0220)	mem 20675MB
[2025-04-02 19:26:01 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][180/234]	eta 0:00:48 lr 0.000951	time 0.8791 (0.8932)	loss 0.4887 (0.4922)	grad_norm 2.1177 (3.0107)	mem 20675MB
[2025-04-02 19:26:03 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][182/234]	eta 0:00:46 lr 0.000950	time 0.8785 (0.8930)	loss 0.3958 (0.4914)	grad_norm 2.5737 (3.0083)	mem 20675MB
[2025-04-02 19:26:05 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][184/234]	eta 0:00:44 lr 0.000950	time 0.8778 (0.8929)	loss 0.5692 (0.4928)	grad_norm 3.4188 (3.0141)	mem 20675MB
[2025-04-02 19:26:07 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][186/234]	eta 0:00:42 lr 0.000949	time 0.8779 (0.8927)	loss 0.3783 (0.4926)	grad_norm 3.0076 (3.0084)	mem 20675MB
[2025-04-02 19:26:08 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][188/234]	eta 0:00:41 lr 0.000949	time 0.8786 (0.8926)	loss 0.4070 (0.4919)	grad_norm 3.8882 (3.0087)	mem 20675MB
[2025-04-02 19:26:10 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][190/234]	eta 0:00:39 lr 0.000948	time 0.8789 (0.8925)	loss 0.5828 (0.4924)	grad_norm 2.6668 (2.9994)	mem 20675MB
[2025-04-02 19:26:12 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][192/234]	eta 0:00:37 lr 0.000948	time 0.8787 (0.8923)	loss 0.5646 (0.4927)	grad_norm 3.5179 (3.0021)	mem 20675MB
[2025-04-02 19:26:14 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][194/234]	eta 0:00:35 lr 0.000947	time 0.8795 (0.8922)	loss 0.4422 (0.4918)	grad_norm 3.2738 (2.9971)	mem 20675MB
[2025-04-02 19:26:15 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][196/234]	eta 0:00:33 lr 0.000947	time 0.8779 (0.8921)	loss 0.5410 (0.4917)	grad_norm 3.0250 (2.9976)	mem 20675MB
[2025-04-02 19:26:17 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][198/234]	eta 0:00:32 lr 0.000946	time 0.8784 (0.8919)	loss 0.4187 (0.4916)	grad_norm 3.7804 (2.9986)	mem 20675MB
[2025-04-02 19:26:19 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][200/234]	eta 0:00:30 lr 0.000946	time 0.8786 (0.8918)	loss 0.4137 (0.4914)	grad_norm 3.8892 (3.0016)	mem 20675MB
[2025-04-02 19:26:21 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][202/234]	eta 0:00:28 lr 0.000945	time 0.8835 (0.8918)	loss 0.5296 (0.4915)	grad_norm 3.5472 (3.0016)	mem 20675MB
[2025-04-02 19:26:22 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][204/234]	eta 0:00:26 lr 0.000945	time 0.8857 (0.8917)	loss 0.3875 (0.4904)	grad_norm 2.8908 (2.9990)	mem 20675MB
[2025-04-02 19:26:24 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][206/234]	eta 0:00:24 lr 0.000944	time 0.8779 (0.8916)	loss 0.5686 (0.4906)	grad_norm 3.1202 (2.9966)	mem 20675MB
[2025-04-02 19:26:26 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][208/234]	eta 0:00:23 lr 0.000944	time 0.8844 (0.8915)	loss 0.5128 (0.4909)	grad_norm 3.6480 (2.9943)	mem 20675MB
[2025-04-02 19:26:28 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][210/234]	eta 0:00:21 lr 0.000943	time 0.8783 (0.8914)	loss 0.4751 (0.4908)	grad_norm 2.6839 (2.9897)	mem 20675MB
[2025-04-02 19:26:29 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][212/234]	eta 0:00:19 lr 0.000943	time 0.8778 (0.8913)	loss 0.4690 (0.4909)	grad_norm 2.3618 (2.9878)	mem 20675MB
[2025-04-02 19:26:31 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][214/234]	eta 0:00:17 lr 0.000942	time 0.8791 (0.8912)	loss 0.3802 (0.4906)	grad_norm 2.8147 (2.9833)	mem 20675MB
[2025-04-02 19:26:33 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][216/234]	eta 0:00:16 lr 0.000942	time 0.8790 (0.8911)	loss 0.5618 (0.4912)	grad_norm 1.6855 (2.9709)	mem 20675MB
[2025-04-02 19:26:35 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][218/234]	eta 0:00:14 lr 0.000941	time 0.8776 (0.8910)	loss 0.5805 (0.4916)	grad_norm 2.9706 (2.9671)	mem 20675MB
[2025-04-02 19:26:36 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][220/234]	eta 0:00:12 lr 0.000941	time 0.8782 (0.8909)	loss 0.5578 (0.4918)	grad_norm 1.8981 (2.9583)	mem 20675MB
[2025-04-02 19:26:38 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][222/234]	eta 0:00:10 lr 0.000940	time 0.8784 (0.8908)	loss 0.4964 (0.4917)	grad_norm 2.5210 (2.9626)	mem 20675MB
[2025-04-02 19:26:40 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][224/234]	eta 0:00:08 lr 0.000940	time 0.8773 (0.8907)	loss 0.4648 (0.4915)	grad_norm 2.3327 (2.9586)	mem 20675MB
[2025-04-02 19:26:42 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][226/234]	eta 0:00:07 lr 0.000939	time 0.8813 (0.8906)	loss 0.3942 (0.4915)	grad_norm 2.1448 (2.9543)	mem 20675MB
[2025-04-02 19:26:44 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][228/234]	eta 0:00:05 lr 0.000939	time 0.8768 (0.8905)	loss 0.5601 (0.4918)	grad_norm 2.5978 (2.9486)	mem 20675MB
[2025-04-02 19:26:45 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][230/234]	eta 0:00:03 lr 0.000939	time 0.8793 (0.8904)	loss 0.4581 (0.4921)	grad_norm 2.1527 (2.9408)	mem 20675MB
[2025-04-02 19:26:47 simmim_finetune] (main_finetune.py 252): INFO Train: [9/30][232/234]	eta 0:00:01 lr 0.000938	time 0.8765 (0.8903)	loss 0.4687 (0.4915)	grad_norm 2.1115 (2.9391)	mem 20675MB
[2025-04-02 19:26:48 simmim_finetune] (main_finetune.py 260): INFO EPOCH 9 training takes 0:03:28
[2025-04-02 19:26:50 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.655 (1.655)	Loss 0.8570 (0.8570)	Acc@1 57.031 (57.031)	Mem 20675MB
[2025-04-02 19:26:50 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 66.298
[2025-04-02 19:26:50 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 66.3%
[2025-04-02 19:26:50 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 68.51%
[2025-04-02 19:26:50 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.5294775327313414e-06, 3.5294775327313414e-06, 5.396337671482796e-06, 5.396337671482796e-06, 8.268430192638881e-06, 8.268430192638881e-06, 1.268703407134055e-05, 1.268703407134055e-05, 1.948488619242004e-05, 1.948488619242004e-05, 2.9943120224850025e-05, 2.9943120224850025e-05, 4.603271104397307e-05, 4.603271104397307e-05, 7.078592768877776e-05, 7.078592768877776e-05, 0.00010886779945001575, 0.00010886779945001575, 0.00016745529446730498, 0.00016745529446730498, 0.0002575899021862114, 0.0002575899021862114, 0.00039625852944606747, 0.00039625852944606747, 0.0006095948790766154, 0.0006095948790766154, 0.0009378046477389966, 0.0009378046477389966]
[2025-04-02 19:26:53 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][0/234]	eta 0:11:29 lr 0.000938	time 2.9470 (2.9470)	loss 0.5324 (0.5324)	grad_norm 2.9407 (2.9407)	mem 20675MB
[2025-04-02 19:26:55 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][2/234]	eta 0:06:03 lr 0.000937	time 0.8759 (1.5671)	loss 0.5136 (0.5042)	grad_norm 2.4015 (2.6041)	mem 20675MB
[2025-04-02 19:26:56 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][4/234]	eta 0:04:57 lr 0.000937	time 0.8803 (1.2926)	loss 0.4811 (0.4924)	grad_norm 4.5857 (3.0568)	mem 20675MB
[2025-04-02 19:26:58 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][6/234]	eta 0:04:27 lr 0.000936	time 0.8807 (1.1754)	loss 0.5693 (0.5066)	grad_norm 3.1368 (2.9222)	mem 20675MB
[2025-04-02 19:27:00 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][8/234]	eta 0:04:11 lr 0.000936	time 0.8922 (1.1113)	loss 0.4369 (0.5027)	grad_norm 2.3716 (2.8179)	mem 20675MB
[2025-04-02 19:27:02 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][10/234]	eta 0:03:59 lr 0.000935	time 0.8775 (1.0689)	loss 0.3427 (0.4915)	grad_norm 2.9973 (2.7653)	mem 20675MB
[2025-04-02 19:27:04 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][12/234]	eta 0:03:50 lr 0.000935	time 0.8773 (1.0396)	loss 0.5063 (0.4888)	grad_norm 1.6955 (2.6431)	mem 20675MB
[2025-04-02 19:27:05 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][14/234]	eta 0:03:43 lr 0.000934	time 0.8765 (1.0181)	loss 0.4396 (0.4842)	grad_norm 2.7748 (2.6799)	mem 20675MB
[2025-04-02 19:27:07 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][16/234]	eta 0:03:38 lr 0.000934	time 0.8796 (1.0018)	loss 0.4557 (0.4788)	grad_norm 2.4103 (2.6209)	mem 20675MB
[2025-04-02 19:27:09 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][18/234]	eta 0:03:33 lr 0.000933	time 0.8768 (0.9889)	loss 0.3949 (0.4820)	grad_norm 3.4196 (2.7226)	mem 20675MB
[2025-04-02 19:27:11 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][20/234]	eta 0:03:29 lr 0.000933	time 0.8771 (0.9783)	loss 0.5594 (0.4797)	grad_norm 3.4094 (2.7996)	mem 20675MB
[2025-04-02 19:27:12 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][22/234]	eta 0:03:25 lr 0.000932	time 0.8803 (0.9697)	loss 0.4257 (0.4781)	grad_norm 3.7662 (2.8991)	mem 20675MB
[2025-04-02 19:27:14 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][24/234]	eta 0:03:22 lr 0.000932	time 0.8784 (0.9624)	loss 0.4876 (0.4822)	grad_norm 2.4521 (2.8777)	mem 20675MB
[2025-04-02 19:27:16 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][26/234]	eta 0:03:18 lr 0.000931	time 0.8769 (0.9562)	loss 0.3877 (0.4820)	grad_norm 4.2010 (2.9649)	mem 20675MB
[2025-04-02 19:27:18 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][28/234]	eta 0:03:15 lr 0.000931	time 0.8770 (0.9508)	loss 0.3858 (0.4755)	grad_norm 2.6667 (2.9479)	mem 20675MB
[2025-04-02 19:27:19 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][30/234]	eta 0:03:13 lr 0.000930	time 0.8773 (0.9461)	loss 0.4523 (0.4762)	grad_norm 2.5502 (2.9075)	mem 20675MB
[2025-04-02 19:27:21 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][32/234]	eta 0:03:10 lr 0.000930	time 0.8786 (0.9420)	loss 0.5862 (0.4819)	grad_norm 2.1899 (2.8730)	mem 20675MB
[2025-04-02 19:27:23 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][34/234]	eta 0:03:07 lr 0.000929	time 0.8768 (0.9384)	loss 0.3106 (0.4782)	grad_norm 2.4812 (2.8622)	mem 20675MB
[2025-04-02 19:27:25 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][36/234]	eta 0:03:05 lr 0.000929	time 0.8769 (0.9351)	loss 0.5340 (0.4811)	grad_norm 2.7326 (2.8470)	mem 20675MB
[2025-04-02 19:27:26 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][38/234]	eta 0:03:02 lr 0.000928	time 0.8857 (0.9324)	loss 0.4173 (0.4777)	grad_norm 3.8556 (2.8712)	mem 20675MB
[2025-04-02 19:27:28 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][40/234]	eta 0:03:00 lr 0.000928	time 0.8773 (0.9297)	loss 0.5513 (0.4814)	grad_norm 2.1810 (2.8335)	mem 20675MB
[2025-04-02 19:27:30 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][42/234]	eta 0:02:58 lr 0.000927	time 0.8768 (0.9273)	loss 0.5534 (0.4833)	grad_norm 4.3190 (2.8709)	mem 20675MB
[2025-04-02 19:27:32 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][44/234]	eta 0:02:55 lr 0.000927	time 0.8965 (0.9256)	loss 0.5342 (0.4812)	grad_norm 2.6196 (2.8707)	mem 20675MB
[2025-04-02 19:27:33 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][46/234]	eta 0:02:53 lr 0.000926	time 0.8766 (0.9240)	loss 0.4120 (0.4806)	grad_norm 3.1312 (2.8941)	mem 20675MB
[2025-04-02 19:27:35 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][48/234]	eta 0:02:51 lr 0.000926	time 0.8770 (0.9221)	loss 0.4949 (0.4784)	grad_norm 4.0693 (2.9174)	mem 20675MB
[2025-04-02 19:27:37 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][50/234]	eta 0:02:49 lr 0.000925	time 0.8774 (0.9204)	loss 0.4819 (0.4773)	grad_norm 4.1947 (3.0281)	mem 20675MB
[2025-04-02 19:27:39 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][52/234]	eta 0:02:47 lr 0.000925	time 0.8766 (0.9188)	loss 0.5129 (0.4769)	grad_norm 2.7576 (2.9990)	mem 20675MB
[2025-04-02 19:27:40 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][54/234]	eta 0:02:45 lr 0.000924	time 0.8782 (0.9174)	loss 0.4301 (0.4776)	grad_norm 4.4414 (3.0976)	mem 20675MB
[2025-04-02 19:27:42 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][56/234]	eta 0:02:43 lr 0.000924	time 0.8817 (0.9161)	loss 0.4039 (0.4772)	grad_norm 3.3786 (3.0845)	mem 20675MB
[2025-04-02 19:27:44 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][58/234]	eta 0:02:41 lr 0.000923	time 0.8767 (0.9148)	loss 0.6015 (0.4774)	grad_norm 2.8439 (3.0823)	mem 20675MB
[2025-04-02 19:27:46 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][60/234]	eta 0:02:38 lr 0.000923	time 0.8774 (0.9136)	loss 0.5632 (0.4779)	grad_norm 2.5777 (3.0756)	mem 20675MB
[2025-04-02 19:27:48 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][62/234]	eta 0:02:36 lr 0.000922	time 0.8773 (0.9126)	loss 0.5515 (0.4795)	grad_norm 2.4467 (3.0665)	mem 20675MB
[2025-04-02 19:27:49 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][64/234]	eta 0:02:35 lr 0.000922	time 0.8832 (0.9119)	loss 0.5024 (0.4811)	grad_norm 2.3696 (3.0689)	mem 20675MB
[2025-04-02 19:27:51 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][66/234]	eta 0:02:33 lr 0.000921	time 0.8773 (0.9111)	loss 0.5729 (0.4834)	grad_norm 2.4506 (3.0886)	mem 20675MB
[2025-04-02 19:27:53 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][68/234]	eta 0:02:31 lr 0.000921	time 0.8775 (0.9101)	loss 0.4386 (0.4834)	grad_norm 2.6518 (3.0731)	mem 20675MB
[2025-04-02 19:27:55 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][70/234]	eta 0:02:29 lr 0.000920	time 0.8779 (0.9093)	loss 0.3936 (0.4814)	grad_norm 3.5495 (3.0699)	mem 20675MB
[2025-04-02 19:27:56 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][72/234]	eta 0:02:27 lr 0.000920	time 0.8766 (0.9085)	loss 0.5722 (0.4824)	grad_norm 2.9537 (3.0773)	mem 20675MB
[2025-04-02 19:27:58 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][74/234]	eta 0:02:25 lr 0.000919	time 0.8774 (0.9077)	loss 0.5504 (0.4818)	grad_norm 2.1891 (3.0516)	mem 20675MB
[2025-04-02 19:28:00 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][76/234]	eta 0:02:23 lr 0.000919	time 0.8771 (0.9069)	loss 0.3546 (0.4779)	grad_norm 3.8010 (3.0554)	mem 20675MB
[2025-04-02 19:28:02 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][78/234]	eta 0:02:21 lr 0.000918	time 0.8779 (0.9063)	loss 0.4406 (0.4774)	grad_norm 3.0259 (3.0537)	mem 20675MB
[2025-04-02 19:28:03 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][80/234]	eta 0:02:19 lr 0.000918	time 0.8772 (0.9056)	loss 0.5023 (0.4790)	grad_norm 2.2061 (3.0514)	mem 20675MB
[2025-04-02 19:28:05 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][82/234]	eta 0:02:17 lr 0.000917	time 0.8871 (0.9051)	loss 0.5281 (0.4785)	grad_norm 2.5530 (3.0612)	mem 20675MB
[2025-04-02 19:28:07 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][84/234]	eta 0:02:15 lr 0.000917	time 0.8838 (0.9046)	loss 0.4484 (0.4789)	grad_norm 2.1205 (3.0538)	mem 20675MB
[2025-04-02 19:28:09 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][86/234]	eta 0:02:13 lr 0.000917	time 0.8766 (0.9040)	loss 0.5260 (0.4780)	grad_norm 1.7410 (3.0344)	mem 20675MB
[2025-04-02 19:28:10 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][88/234]	eta 0:02:11 lr 0.000916	time 0.8767 (0.9035)	loss 0.5367 (0.4800)	grad_norm 2.2230 (3.0163)	mem 20675MB
[2025-04-02 19:28:12 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][90/234]	eta 0:02:10 lr 0.000916	time 0.8764 (0.9029)	loss 0.5264 (0.4804)	grad_norm 1.6797 (3.0124)	mem 20675MB
[2025-04-02 19:28:14 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][92/234]	eta 0:02:08 lr 0.000915	time 0.8776 (0.9025)	loss 0.5154 (0.4803)	grad_norm 2.2019 (2.9915)	mem 20675MB
[2025-04-02 19:28:16 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][94/234]	eta 0:02:06 lr 0.000915	time 0.8811 (0.9020)	loss 0.5057 (0.4806)	grad_norm 2.1761 (2.9744)	mem 20675MB
[2025-04-02 19:28:17 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][96/234]	eta 0:02:04 lr 0.000914	time 0.8820 (0.9018)	loss 0.3754 (0.4805)	grad_norm 2.2845 (2.9613)	mem 20675MB
[2025-04-02 19:28:19 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][98/234]	eta 0:02:02 lr 0.000914	time 0.8771 (0.9013)	loss 0.5057 (0.4806)	grad_norm 1.6714 (2.9452)	mem 20675MB
[2025-04-02 19:28:21 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][100/234]	eta 0:02:00 lr 0.000913	time 0.8782 (0.9009)	loss 0.5176 (0.4812)	grad_norm 2.9360 (2.9350)	mem 20675MB
[2025-04-02 19:28:23 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][102/234]	eta 0:01:58 lr 0.000913	time 0.8763 (0.9004)	loss 0.4278 (0.4807)	grad_norm 2.0035 (2.9216)	mem 20675MB
[2025-04-02 19:28:25 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][104/234]	eta 0:01:57 lr 0.000912	time 0.8767 (0.9002)	loss 0.5269 (0.4816)	grad_norm 3.1933 (2.9275)	mem 20675MB
[2025-04-02 19:28:26 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][106/234]	eta 0:01:55 lr 0.000912	time 0.8771 (0.8997)	loss 0.4504 (0.4815)	grad_norm 3.9716 (2.9392)	mem 20675MB
[2025-04-02 19:28:28 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][108/234]	eta 0:01:53 lr 0.000911	time 0.8818 (0.8994)	loss 0.5672 (0.4830)	grad_norm 2.2509 (2.9374)	mem 20675MB
[2025-04-02 19:28:30 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][110/234]	eta 0:01:51 lr 0.000911	time 0.8766 (0.8991)	loss 0.4663 (0.4839)	grad_norm 3.1532 (2.9418)	mem 20675MB
[2025-04-02 19:28:32 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][112/234]	eta 0:01:49 lr 0.000910	time 0.8802 (0.8987)	loss 0.5827 (0.4854)	grad_norm 1.8404 (2.9220)	mem 20675MB
[2025-04-02 19:28:33 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][114/234]	eta 0:01:47 lr 0.000910	time 0.8765 (0.8985)	loss 0.5777 (0.4868)	grad_norm 1.9464 (2.9093)	mem 20675MB
[2025-04-02 19:28:35 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][116/234]	eta 0:01:45 lr 0.000909	time 0.8777 (0.8981)	loss 0.3764 (0.4862)	grad_norm 3.3396 (2.9033)	mem 20675MB
[2025-04-02 19:28:37 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][118/234]	eta 0:01:44 lr 0.000909	time 0.8785 (0.8978)	loss 0.4134 (0.4852)	grad_norm 3.8435 (2.9024)	mem 20675MB
[2025-04-02 19:28:39 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][120/234]	eta 0:01:42 lr 0.000908	time 0.8824 (0.8975)	loss 0.5912 (0.4862)	grad_norm 1.9710 (2.8921)	mem 20675MB
[2025-04-02 19:28:40 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][122/234]	eta 0:01:40 lr 0.000908	time 0.8785 (0.8973)	loss 0.4263 (0.4843)	grad_norm 4.9209 (2.9129)	mem 20675MB
[2025-04-02 19:28:42 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][124/234]	eta 0:01:38 lr 0.000907	time 0.8789 (0.8971)	loss 0.5229 (0.4837)	grad_norm 3.6074 (2.9267)	mem 20675MB
[2025-04-02 19:28:44 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][126/234]	eta 0:01:36 lr 0.000907	time 0.8788 (0.8968)	loss 0.5351 (0.4826)	grad_norm 2.7836 (2.9244)	mem 20675MB
[2025-04-02 19:28:46 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][128/234]	eta 0:01:35 lr 0.000906	time 0.8796 (0.8965)	loss 0.5208 (0.4830)	grad_norm 3.4783 (2.9272)	mem 20675MB
[2025-04-02 19:28:47 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][130/234]	eta 0:01:33 lr 0.000906	time 0.8793 (0.8963)	loss 0.4914 (0.4832)	grad_norm 3.4642 (2.9258)	mem 20675MB
[2025-04-02 19:28:49 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][132/234]	eta 0:01:31 lr 0.000905	time 0.8797 (0.8962)	loss 0.4339 (0.4822)	grad_norm 3.5972 (2.9319)	mem 20675MB
[2025-04-02 19:28:51 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][134/234]	eta 0:01:29 lr 0.000905	time 0.9226 (0.8964)	loss 0.5223 (0.4820)	grad_norm 1.9544 (2.9299)	mem 20675MB
[2025-04-02 19:28:53 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][136/234]	eta 0:01:27 lr 0.000904	time 0.8805 (0.8961)	loss 0.5125 (0.4820)	grad_norm 3.3235 (2.9317)	mem 20675MB
[2025-04-02 19:28:55 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][138/234]	eta 0:01:26 lr 0.000904	time 0.8796 (0.8959)	loss 0.5230 (0.4821)	grad_norm 2.8633 (2.9300)	mem 20675MB
[2025-04-02 19:28:56 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][140/234]	eta 0:01:24 lr 0.000903	time 0.8793 (0.8957)	loss 0.4860 (0.4817)	grad_norm 2.2300 (2.9227)	mem 20675MB
[2025-04-02 19:28:58 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][142/234]	eta 0:01:22 lr 0.000903	time 0.8794 (0.8956)	loss 0.5076 (0.4813)	grad_norm 1.9896 (2.9130)	mem 20675MB
[2025-04-02 19:29:00 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][144/234]	eta 0:01:20 lr 0.000902	time 0.8840 (0.8954)	loss 0.5588 (0.4816)	grad_norm 2.7376 (2.9118)	mem 20675MB
[2025-04-02 19:29:02 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][146/234]	eta 0:01:18 lr 0.000902	time 0.8796 (0.8952)	loss 0.4723 (0.4823)	grad_norm 2.7883 (2.9109)	mem 20675MB
[2025-04-02 19:29:03 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][148/234]	eta 0:01:16 lr 0.000901	time 0.8786 (0.8950)	loss 0.4831 (0.4826)	grad_norm 2.5921 (2.9090)	mem 20675MB
[2025-04-02 19:29:05 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][150/234]	eta 0:01:15 lr 0.000901	time 0.8791 (0.8949)	loss 0.5048 (0.4835)	grad_norm 3.3237 (2.9072)	mem 20675MB
[2025-04-02 19:29:07 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][152/234]	eta 0:01:13 lr 0.000900	time 0.8795 (0.8947)	loss 0.4120 (0.4832)	grad_norm 2.7107 (2.9018)	mem 20675MB
[2025-04-02 19:29:09 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][154/234]	eta 0:01:11 lr 0.000900	time 0.8810 (0.8945)	loss 0.4578 (0.4824)	grad_norm 2.2984 (2.8933)	mem 20675MB
[2025-04-02 19:29:10 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][156/234]	eta 0:01:09 lr 0.000899	time 0.8782 (0.8943)	loss 0.3813 (0.4817)	grad_norm 2.3231 (2.8870)	mem 20675MB
[2025-04-02 19:29:12 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][158/234]	eta 0:01:07 lr 0.000899	time 0.8795 (0.8941)	loss 0.4690 (0.4813)	grad_norm 3.4959 (2.8946)	mem 20675MB
[2025-04-02 19:29:14 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][160/234]	eta 0:01:06 lr 0.000898	time 0.8817 (0.8941)	loss 0.5998 (0.4824)	grad_norm 3.0081 (2.9026)	mem 20675MB
[2025-04-02 19:29:16 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][162/234]	eta 0:01:04 lr 0.000898	time 0.8778 (0.8939)	loss 0.4360 (0.4814)	grad_norm 4.0720 (2.9287)	mem 20675MB
[2025-04-02 19:29:17 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][164/234]	eta 0:01:02 lr 0.000897	time 0.8932 (0.8938)	loss 0.5531 (0.4816)	grad_norm 4.9871 (2.9533)	mem 20675MB
[2025-04-02 19:29:19 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][166/234]	eta 0:01:00 lr 0.000897	time 0.8782 (0.8937)	loss 0.3973 (0.4812)	grad_norm 6.1809 (2.9805)	mem 20675MB
[2025-04-02 19:29:21 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][168/234]	eta 0:00:58 lr 0.000896	time 0.8776 (0.8935)	loss 0.5309 (0.4815)	grad_norm 5.0915 (2.9977)	mem 20675MB
[2025-04-02 19:29:23 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][170/234]	eta 0:00:57 lr 0.000896	time 0.9029 (0.8935)	loss 0.5182 (0.4815)	grad_norm 2.6247 (2.9980)	mem 20675MB
[2025-04-02 19:29:25 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][172/234]	eta 0:00:55 lr 0.000895	time 0.8789 (0.8934)	loss 0.3920 (0.4815)	grad_norm 5.2131 (3.0115)	mem 20675MB
[2025-04-02 19:29:26 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][174/234]	eta 0:00:53 lr 0.000895	time 0.8810 (0.8932)	loss 0.5319 (0.4823)	grad_norm 2.9947 (3.0156)	mem 20675MB
[2025-04-02 19:29:28 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][176/234]	eta 0:00:51 lr 0.000894	time 0.8789 (0.8931)	loss 0.5825 (0.4827)	grad_norm 2.5464 (3.0163)	mem 20675MB
[2025-04-02 19:29:30 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][178/234]	eta 0:00:50 lr 0.000894	time 0.8782 (0.8929)	loss 0.5631 (0.4830)	grad_norm 2.3785 (3.0077)	mem 20675MB
[2025-04-02 19:29:32 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][180/234]	eta 0:00:48 lr 0.000893	time 0.8779 (0.8928)	loss 0.4361 (0.4829)	grad_norm 2.5386 (2.9998)	mem 20675MB
[2025-04-02 19:29:33 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][182/234]	eta 0:00:46 lr 0.000892	time 0.8782 (0.8926)	loss 0.3957 (0.4826)	grad_norm 2.4401 (2.9932)	mem 20675MB
[2025-04-02 19:29:35 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][184/234]	eta 0:00:44 lr 0.000892	time 0.8971 (0.8926)	loss 0.5217 (0.4829)	grad_norm 1.6076 (2.9845)	mem 20675MB
[2025-04-02 19:29:37 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][186/234]	eta 0:00:42 lr 0.000891	time 0.8784 (0.8925)	loss 0.4575 (0.4838)	grad_norm 2.6165 (2.9814)	mem 20675MB
[2025-04-02 19:29:39 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][188/234]	eta 0:00:41 lr 0.000891	time 0.8786 (0.8923)	loss 0.5926 (0.4845)	grad_norm 2.8753 (2.9847)	mem 20675MB
[2025-04-02 19:29:40 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][190/234]	eta 0:00:39 lr 0.000890	time 0.8785 (0.8922)	loss 0.5539 (0.4845)	grad_norm 2.5331 (2.9798)	mem 20675MB
[2025-04-02 19:29:42 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][192/234]	eta 0:00:37 lr 0.000890	time 0.8792 (0.8921)	loss 0.4564 (0.4844)	grad_norm 4.6149 (2.9849)	mem 20675MB
[2025-04-02 19:29:44 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][194/234]	eta 0:00:35 lr 0.000889	time 0.8795 (0.8919)	loss 0.5715 (0.4850)	grad_norm 2.0119 (2.9782)	mem 20675MB
[2025-04-02 19:29:46 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][196/234]	eta 0:00:33 lr 0.000889	time 0.8793 (0.8918)	loss 0.5313 (0.4850)	grad_norm 2.5789 (2.9736)	mem 20675MB
[2025-04-02 19:29:47 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][198/234]	eta 0:00:32 lr 0.000888	time 0.8966 (0.8918)	loss 0.3662 (0.4845)	grad_norm 3.6935 (2.9765)	mem 20675MB
[2025-04-02 19:29:49 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][200/234]	eta 0:00:30 lr 0.000888	time 0.8789 (0.8917)	loss 0.4068 (0.4844)	grad_norm 2.3264 (2.9695)	mem 20675MB
[2025-04-02 19:29:51 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][202/234]	eta 0:00:28 lr 0.000887	time 0.8793 (0.8916)	loss 0.5706 (0.4846)	grad_norm 2.9058 (2.9668)	mem 20675MB
[2025-04-02 19:29:53 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][204/234]	eta 0:00:26 lr 0.000887	time 0.8847 (0.8915)	loss 0.5172 (0.4844)	grad_norm 2.8187 (2.9617)	mem 20675MB
[2025-04-02 19:29:55 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][206/234]	eta 0:00:24 lr 0.000886	time 0.8790 (0.8914)	loss 0.5659 (0.4847)	grad_norm 4.0321 (2.9750)	mem 20675MB
[2025-04-02 19:29:56 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][208/234]	eta 0:00:23 lr 0.000886	time 0.8775 (0.8913)	loss 0.5204 (0.4850)	grad_norm 2.4204 (2.9713)	mem 20675MB
[2025-04-02 19:29:58 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][210/234]	eta 0:00:21 lr 0.000885	time 0.8773 (0.8912)	loss 0.5403 (0.4855)	grad_norm 2.4009 (2.9686)	mem 20675MB
[2025-04-02 19:30:00 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][212/234]	eta 0:00:19 lr 0.000885	time 0.8776 (0.8910)	loss 0.5350 (0.4862)	grad_norm 2.0987 (2.9619)	mem 20675MB
[2025-04-02 19:30:02 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][214/234]	eta 0:00:17 lr 0.000884	time 0.8777 (0.8909)	loss 0.5333 (0.4867)	grad_norm 2.1322 (2.9542)	mem 20675MB
[2025-04-02 19:30:03 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][216/234]	eta 0:00:16 lr 0.000884	time 0.8769 (0.8908)	loss 0.5638 (0.4871)	grad_norm 2.0594 (2.9461)	mem 20675MB
[2025-04-02 19:30:05 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][218/234]	eta 0:00:14 lr 0.000883	time 0.8775 (0.8907)	loss 0.5375 (0.4876)	grad_norm 2.3207 (2.9432)	mem 20675MB
[2025-04-02 19:30:07 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][220/234]	eta 0:00:12 lr 0.000883	time 0.8767 (0.8906)	loss 0.5184 (0.4880)	grad_norm 1.9819 (2.9344)	mem 20675MB
[2025-04-02 19:30:09 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][222/234]	eta 0:00:10 lr 0.000882	time 0.8772 (0.8905)	loss 0.4632 (0.4877)	grad_norm 2.6008 (2.9306)	mem 20675MB
[2025-04-02 19:30:10 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][224/234]	eta 0:00:08 lr 0.000882	time 0.8779 (0.8904)	loss 0.5613 (0.4881)	grad_norm 2.8960 (2.9376)	mem 20675MB
[2025-04-02 19:30:12 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][226/234]	eta 0:00:07 lr 0.000881	time 0.8777 (0.8904)	loss 0.4767 (0.4880)	grad_norm 2.2731 (2.9304)	mem 20675MB
[2025-04-02 19:30:14 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][228/234]	eta 0:00:05 lr 0.000881	time 0.8772 (0.8902)	loss 0.4887 (0.4882)	grad_norm 2.2444 (2.9237)	mem 20675MB
[2025-04-02 19:30:16 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][230/234]	eta 0:00:03 lr 0.000880	time 0.8855 (0.8902)	loss 0.5153 (0.4886)	grad_norm 3.1711 (2.9233)	mem 20675MB
[2025-04-02 19:30:17 simmim_finetune] (main_finetune.py 252): INFO Train: [10/30][232/234]	eta 0:00:01 lr 0.000880	time 0.8787 (0.8901)	loss 0.5309 (0.4884)	grad_norm 3.6536 (2.9249)	mem 20675MB
[2025-04-02 19:30:18 simmim_finetune] (main_finetune.py 260): INFO EPOCH 10 training takes 0:03:28
[2025-04-02 19:30:19 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt10.pth saving......
[2025-04-02 19:30:22 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt10.pth saved !!!
[2025-04-02 19:30:23 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.128 (1.128)	Loss 0.9462 (0.9462)	Acc@1 56.250 (56.250)	Mem 20675MB
[2025-04-02 19:30:23 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 65.746
[2025-04-02 19:30:23 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 65.7%
[2025-04-02 19:30:23 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 68.51%
[2025-04-02 19:30:23 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.3256733100132143e-06, 3.3256733100132143e-06, 5.076516804130176e-06, 5.076516804130176e-06, 7.770122179694733e-06, 7.770122179694733e-06, 1.1914130449794052e-05, 1.1914130449794052e-05, 1.8289527788408386e-05, 1.8289527788408386e-05, 2.8097831386276598e-05, 2.8097831386276598e-05, 4.318752922915076e-05, 4.318752922915076e-05, 6.64024489874187e-05, 6.64024489874187e-05, 0.00010211771015398477, 0.00010211771015398477, 0.00015706426579485566, 0.00015706426579485566, 0.00024159742831927238, 0.00024159742831927238, 0.0003716484475876058, 0.0003716484475876058, 0.0005717269387696572, 0.0005717269387696572, 0.0008795400021266594, 0.0008795400021266594]
[2025-04-02 19:30:25 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][0/234]	eta 0:07:33 lr 0.000879	time 1.9360 (1.9360)	loss 0.3701 (0.3701)	grad_norm 2.4181 (2.4181)	mem 20675MB
[2025-04-02 19:30:27 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][2/234]	eta 0:04:45 lr 0.000879	time 0.8795 (1.2321)	loss 0.4035 (0.4374)	grad_norm 4.9067 (3.1389)	mem 20675MB
[2025-04-02 19:30:29 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][4/234]	eta 0:04:10 lr 0.000878	time 0.8779 (1.0909)	loss 0.7222 (0.5083)	grad_norm 6.6321 (3.9392)	mem 20675MB
[2025-04-02 19:30:30 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][6/234]	eta 0:03:54 lr 0.000878	time 0.8782 (1.0304)	loss 0.5748 (0.5155)	grad_norm 3.0563 (3.6260)	mem 20675MB
[2025-04-02 19:30:32 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][8/234]	eta 0:03:45 lr 0.000877	time 0.8787 (0.9968)	loss 0.4587 (0.5088)	grad_norm 4.2055 (3.5894)	mem 20675MB
[2025-04-02 19:30:34 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][10/234]	eta 0:03:38 lr 0.000877	time 0.8793 (0.9756)	loss 0.4050 (0.4911)	grad_norm 3.6184 (3.5508)	mem 20675MB
[2025-04-02 19:30:36 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][12/234]	eta 0:03:33 lr 0.000876	time 0.8791 (0.9608)	loss 0.5668 (0.5000)	grad_norm 4.2538 (3.4874)	mem 20675MB
[2025-04-02 19:30:38 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][14/234]	eta 0:03:29 lr 0.000876	time 0.8792 (0.9500)	loss 0.3993 (0.4956)	grad_norm 2.5688 (3.4346)	mem 20675MB
[2025-04-02 19:30:39 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][16/234]	eta 0:03:25 lr 0.000875	time 0.8781 (0.9417)	loss 0.5041 (0.4904)	grad_norm 2.7756 (3.5031)	mem 20675MB
[2025-04-02 19:30:41 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][18/234]	eta 0:03:21 lr 0.000875	time 0.8786 (0.9351)	loss 0.5420 (0.4901)	grad_norm 3.5703 (3.4508)	mem 20675MB
[2025-04-02 19:30:43 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][20/234]	eta 0:03:18 lr 0.000874	time 0.8786 (0.9298)	loss 0.4298 (0.4785)	grad_norm 3.7027 (3.4677)	mem 20675MB
[2025-04-02 19:30:45 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][22/234]	eta 0:03:16 lr 0.000874	time 0.8798 (0.9255)	loss 0.5152 (0.4808)	grad_norm 2.8332 (3.3836)	mem 20675MB
[2025-04-02 19:30:46 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][24/234]	eta 0:03:13 lr 0.000873	time 0.8792 (0.9218)	loss 0.6009 (0.4886)	grad_norm 3.4365 (3.3567)	mem 20675MB
[2025-04-02 19:30:48 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][26/234]	eta 0:03:11 lr 0.000873	time 0.8790 (0.9187)	loss 0.5680 (0.4936)	grad_norm 3.5900 (3.3318)	mem 20675MB
[2025-04-02 19:30:50 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][28/234]	eta 0:03:08 lr 0.000872	time 0.8786 (0.9160)	loss 0.4802 (0.4924)	grad_norm 1.7783 (3.2301)	mem 20675MB
[2025-04-02 19:30:52 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][30/234]	eta 0:03:06 lr 0.000872	time 0.8790 (0.9136)	loss 0.4787 (0.4871)	grad_norm 2.6241 (3.1961)	mem 20675MB
[2025-04-02 19:30:53 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][32/234]	eta 0:03:04 lr 0.000871	time 0.8788 (0.9116)	loss 0.5439 (0.4883)	grad_norm 2.4723 (3.1561)	mem 20675MB
[2025-04-02 19:30:55 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][34/234]	eta 0:03:01 lr 0.000871	time 0.8799 (0.9098)	loss 0.4408 (0.4828)	grad_norm 2.7252 (3.1194)	mem 20675MB
[2025-04-02 19:30:57 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][36/234]	eta 0:02:59 lr 0.000870	time 0.8789 (0.9081)	loss 0.4306 (0.4785)	grad_norm 2.1961 (3.0946)	mem 20675MB
[2025-04-02 19:30:59 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][38/234]	eta 0:02:57 lr 0.000870	time 0.8817 (0.9068)	loss 0.4726 (0.4794)	grad_norm 4.1175 (3.1352)	mem 20675MB
[2025-04-02 19:31:00 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][40/234]	eta 0:02:55 lr 0.000869	time 0.8795 (0.9055)	loss 0.6222 (0.4840)	grad_norm 4.0641 (3.1374)	mem 20675MB
[2025-04-02 19:31:02 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][42/234]	eta 0:02:53 lr 0.000869	time 0.8790 (0.9044)	loss 0.5533 (0.4824)	grad_norm 4.8684 (3.2394)	mem 20675MB
[2025-04-02 19:31:04 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][44/234]	eta 0:02:51 lr 0.000868	time 0.8794 (0.9033)	loss 0.5182 (0.4823)	grad_norm 4.0701 (3.2454)	mem 20675MB
[2025-04-02 19:31:06 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][46/234]	eta 0:02:49 lr 0.000867	time 0.8793 (0.9024)	loss 0.4463 (0.4835)	grad_norm 2.4152 (3.2447)	mem 20675MB
[2025-04-02 19:31:07 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][48/234]	eta 0:02:47 lr 0.000867	time 0.8794 (0.9015)	loss 0.5367 (0.4843)	grad_norm 2.5760 (3.1984)	mem 20675MB
[2025-04-02 19:31:09 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][50/234]	eta 0:02:45 lr 0.000866	time 0.8791 (0.9006)	loss 0.5016 (0.4828)	grad_norm 3.7503 (3.2049)	mem 20675MB
[2025-04-02 19:31:11 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][52/234]	eta 0:02:43 lr 0.000866	time 0.8798 (0.8999)	loss 0.6085 (0.4852)	grad_norm 3.4690 (3.1987)	mem 20675MB
[2025-04-02 19:31:13 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][54/234]	eta 0:02:41 lr 0.000865	time 0.8810 (0.8992)	loss 0.3833 (0.4845)	grad_norm 2.5575 (3.1628)	mem 20675MB
[2025-04-02 19:31:14 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][56/234]	eta 0:02:39 lr 0.000865	time 0.8788 (0.8985)	loss 0.5027 (0.4841)	grad_norm 3.1581 (3.1604)	mem 20675MB
[2025-04-02 19:31:16 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][58/234]	eta 0:02:38 lr 0.000864	time 0.8789 (0.8979)	loss 0.5620 (0.4849)	grad_norm 2.3856 (3.1415)	mem 20675MB
[2025-04-02 19:31:18 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][60/234]	eta 0:02:36 lr 0.000864	time 0.8780 (0.8973)	loss 0.3823 (0.4841)	grad_norm 2.4583 (3.1154)	mem 20675MB
[2025-04-02 19:31:20 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][62/234]	eta 0:02:34 lr 0.000863	time 0.8792 (0.8967)	loss 0.4374 (0.4842)	grad_norm 3.2540 (3.1121)	mem 20675MB
[2025-04-02 19:31:22 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][64/234]	eta 0:02:32 lr 0.000863	time 0.8790 (0.8962)	loss 0.4873 (0.4852)	grad_norm 2.5363 (3.0943)	mem 20675MB
[2025-04-02 19:31:23 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][66/234]	eta 0:02:30 lr 0.000862	time 0.8784 (0.8957)	loss 0.6047 (0.4876)	grad_norm 2.8925 (3.0703)	mem 20675MB
[2025-04-02 19:31:25 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][68/234]	eta 0:02:28 lr 0.000862	time 0.8787 (0.8952)	loss 0.4407 (0.4887)	grad_norm 2.3690 (3.0592)	mem 20675MB
[2025-04-02 19:31:27 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][70/234]	eta 0:02:26 lr 0.000861	time 0.8791 (0.8948)	loss 0.5411 (0.4908)	grad_norm 1.5475 (3.0257)	mem 20675MB
[2025-04-02 19:31:29 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][72/234]	eta 0:02:24 lr 0.000861	time 0.8792 (0.8944)	loss 0.5648 (0.4902)	grad_norm 1.8020 (3.0041)	mem 20675MB
[2025-04-02 19:31:30 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][74/234]	eta 0:02:23 lr 0.000860	time 0.8792 (0.8940)	loss 0.5695 (0.4921)	grad_norm 1.9952 (2.9712)	mem 20675MB
[2025-04-02 19:31:32 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][76/234]	eta 0:02:21 lr 0.000860	time 0.8798 (0.8937)	loss 0.3525 (0.4889)	grad_norm 5.5068 (3.0238)	mem 20675MB
[2025-04-02 19:31:34 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][78/234]	eta 0:02:19 lr 0.000859	time 0.8790 (0.8933)	loss 0.3702 (0.4862)	grad_norm 3.4008 (3.0408)	mem 20675MB
[2025-04-02 19:31:36 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][80/234]	eta 0:02:17 lr 0.000859	time 0.8789 (0.8930)	loss 0.5851 (0.4875)	grad_norm 4.4696 (3.0573)	mem 20675MB
[2025-04-02 19:31:37 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][82/234]	eta 0:02:15 lr 0.000858	time 0.8785 (0.8927)	loss 0.5225 (0.4880)	grad_norm 2.6033 (3.0445)	mem 20675MB
[2025-04-02 19:31:39 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][84/234]	eta 0:02:13 lr 0.000858	time 0.8784 (0.8923)	loss 0.4683 (0.4886)	grad_norm 3.3624 (3.0518)	mem 20675MB
[2025-04-02 19:31:41 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][86/234]	eta 0:02:12 lr 0.000857	time 0.8788 (0.8921)	loss 0.4444 (0.4886)	grad_norm 2.5164 (3.0390)	mem 20675MB
[2025-04-02 19:31:43 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][88/234]	eta 0:02:10 lr 0.000857	time 0.8782 (0.8918)	loss 0.5931 (0.4891)	grad_norm 2.7535 (3.0361)	mem 20675MB
[2025-04-02 19:31:44 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][90/234]	eta 0:02:08 lr 0.000856	time 0.8782 (0.8915)	loss 0.4862 (0.4887)	grad_norm 2.5498 (3.0299)	mem 20675MB
[2025-04-02 19:31:46 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][92/234]	eta 0:02:06 lr 0.000856	time 0.8791 (0.8912)	loss 0.4441 (0.4883)	grad_norm 2.2572 (3.0416)	mem 20675MB
[2025-04-02 19:31:48 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][94/234]	eta 0:02:04 lr 0.000855	time 0.8791 (0.8910)	loss 0.5582 (0.4890)	grad_norm 2.8262 (3.0352)	mem 20675MB
[2025-04-02 19:31:50 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][96/234]	eta 0:02:02 lr 0.000855	time 0.8790 (0.8908)	loss 0.4966 (0.4892)	grad_norm 2.1537 (3.0315)	mem 20675MB
[2025-04-02 19:31:51 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][98/234]	eta 0:02:01 lr 0.000854	time 0.8799 (0.8906)	loss 0.5128 (0.4887)	grad_norm 4.2960 (3.0520)	mem 20675MB
[2025-04-02 19:31:53 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][100/234]	eta 0:01:59 lr 0.000853	time 0.8792 (0.8903)	loss 0.4242 (0.4893)	grad_norm 4.6193 (3.0685)	mem 20675MB
[2025-04-02 19:31:55 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][102/234]	eta 0:01:57 lr 0.000853	time 0.8787 (0.8901)	loss 0.5389 (0.4902)	grad_norm 3.9355 (3.0719)	mem 20675MB
[2025-04-02 19:31:57 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][104/234]	eta 0:01:55 lr 0.000852	time 0.8788 (0.8899)	loss 0.5418 (0.4912)	grad_norm 2.0361 (3.0525)	mem 20675MB
[2025-04-02 19:31:58 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][106/234]	eta 0:01:53 lr 0.000852	time 0.8780 (0.8897)	loss 0.5285 (0.4920)	grad_norm 2.5597 (3.0490)	mem 20675MB
[2025-04-02 19:32:00 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][108/234]	eta 0:01:52 lr 0.000851	time 0.8785 (0.8896)	loss 0.5337 (0.4911)	grad_norm 2.0171 (3.0380)	mem 20675MB
[2025-04-02 19:32:02 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][110/234]	eta 0:01:50 lr 0.000851	time 0.8787 (0.8894)	loss 0.4640 (0.4897)	grad_norm 2.3972 (3.0294)	mem 20675MB
[2025-04-02 19:32:04 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][112/234]	eta 0:01:48 lr 0.000850	time 0.8782 (0.8892)	loss 0.6387 (0.4919)	grad_norm 4.7358 (3.0395)	mem 20675MB
[2025-04-02 19:32:06 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][114/234]	eta 0:01:46 lr 0.000850	time 0.8780 (0.8890)	loss 0.5132 (0.4919)	grad_norm 3.3901 (3.0519)	mem 20675MB
[2025-04-02 19:32:07 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][116/234]	eta 0:01:44 lr 0.000849	time 0.8784 (0.8888)	loss 0.5129 (0.4922)	grad_norm 2.1407 (3.0358)	mem 20675MB
[2025-04-02 19:32:09 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][118/234]	eta 0:01:43 lr 0.000849	time 0.8791 (0.8887)	loss 0.4042 (0.4915)	grad_norm 4.5241 (3.0620)	mem 20675MB
[2025-04-02 19:32:11 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][120/234]	eta 0:01:41 lr 0.000848	time 0.8801 (0.8886)	loss 0.5569 (0.4915)	grad_norm 4.0613 (3.0614)	mem 20675MB
[2025-04-02 19:32:13 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][122/234]	eta 0:01:39 lr 0.000848	time 0.8796 (0.8884)	loss 0.5579 (0.4917)	grad_norm 2.4229 (3.0718)	mem 20675MB
[2025-04-02 19:32:14 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][124/234]	eta 0:01:37 lr 0.000847	time 0.8799 (0.8883)	loss 0.5850 (0.4928)	grad_norm 2.4917 (3.0717)	mem 20675MB
[2025-04-02 19:32:16 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][126/234]	eta 0:01:35 lr 0.000847	time 0.8803 (0.8882)	loss 0.4522 (0.4924)	grad_norm 3.3305 (3.0678)	mem 20675MB
[2025-04-02 19:32:18 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][128/234]	eta 0:01:34 lr 0.000846	time 0.8793 (0.8881)	loss 0.4547 (0.4919)	grad_norm 2.6187 (3.0585)	mem 20675MB
[2025-04-02 19:32:20 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][130/234]	eta 0:01:32 lr 0.000846	time 0.8799 (0.8879)	loss 0.5209 (0.4928)	grad_norm 2.0661 (3.0438)	mem 20675MB
[2025-04-02 19:32:21 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][132/234]	eta 0:01:30 lr 0.000845	time 0.8784 (0.8878)	loss 0.5955 (0.4946)	grad_norm 2.2076 (3.0420)	mem 20675MB
[2025-04-02 19:32:23 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][134/234]	eta 0:01:28 lr 0.000845	time 0.8799 (0.8877)	loss 0.4919 (0.4946)	grad_norm 2.0177 (3.0259)	mem 20675MB
[2025-04-02 19:32:25 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][136/234]	eta 0:01:26 lr 0.000844	time 0.8781 (0.8876)	loss 0.4429 (0.4943)	grad_norm 2.9952 (3.0182)	mem 20675MB
[2025-04-02 19:32:27 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][138/234]	eta 0:01:25 lr 0.000844	time 0.8780 (0.8875)	loss 0.4900 (0.4948)	grad_norm 2.5875 (3.0116)	mem 20675MB
[2025-04-02 19:32:28 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][140/234]	eta 0:01:23 lr 0.000843	time 0.8788 (0.8873)	loss 0.5351 (0.4948)	grad_norm 1.9358 (3.0045)	mem 20675MB
[2025-04-02 19:32:30 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][142/234]	eta 0:01:21 lr 0.000843	time 0.8799 (0.8872)	loss 0.3731 (0.4943)	grad_norm 3.8256 (3.0028)	mem 20675MB
[2025-04-02 19:32:32 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][144/234]	eta 0:01:19 lr 0.000842	time 0.8793 (0.8872)	loss 0.5342 (0.4946)	grad_norm 3.4417 (3.0005)	mem 20675MB
[2025-04-02 19:32:34 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][146/234]	eta 0:01:18 lr 0.000841	time 0.8772 (0.8870)	loss 0.4778 (0.4936)	grad_norm 2.6676 (3.0053)	mem 20675MB
[2025-04-02 19:32:35 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][148/234]	eta 0:01:16 lr 0.000841	time 0.8790 (0.8869)	loss 0.4749 (0.4930)	grad_norm 2.3686 (3.0023)	mem 20675MB
[2025-04-02 19:32:37 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][150/234]	eta 0:01:14 lr 0.000840	time 0.8792 (0.8868)	loss 0.6883 (0.4952)	grad_norm 4.0089 (3.0165)	mem 20675MB
[2025-04-02 19:32:39 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][152/234]	eta 0:01:12 lr 0.000840	time 0.8790 (0.8867)	loss 0.5578 (0.4955)	grad_norm 3.7924 (3.0205)	mem 20675MB
[2025-04-02 19:32:41 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][154/234]	eta 0:01:10 lr 0.000839	time 0.8792 (0.8866)	loss 0.5436 (0.4959)	grad_norm 2.5815 (3.0120)	mem 20675MB
[2025-04-02 19:32:42 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][156/234]	eta 0:01:09 lr 0.000839	time 0.8788 (0.8866)	loss 0.4858 (0.4958)	grad_norm 1.8248 (3.0021)	mem 20675MB
[2025-04-02 19:32:44 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][158/234]	eta 0:01:07 lr 0.000838	time 0.8792 (0.8865)	loss 0.5793 (0.4960)	grad_norm 1.6297 (2.9936)	mem 20675MB
[2025-04-02 19:32:46 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][160/234]	eta 0:01:05 lr 0.000838	time 0.8796 (0.8864)	loss 0.4167 (0.4952)	grad_norm 2.1676 (2.9943)	mem 20675MB
[2025-04-02 19:32:48 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][162/234]	eta 0:01:03 lr 0.000837	time 0.8797 (0.8863)	loss 0.5203 (0.4946)	grad_norm 1.8320 (2.9927)	mem 20675MB
[2025-04-02 19:32:50 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][164/234]	eta 0:01:02 lr 0.000837	time 0.8791 (0.8862)	loss 0.4817 (0.4936)	grad_norm 2.7959 (2.9939)	mem 20675MB
[2025-04-02 19:32:51 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][166/234]	eta 0:01:00 lr 0.000836	time 0.8796 (0.8862)	loss 0.3761 (0.4925)	grad_norm 3.2919 (2.9954)	mem 20675MB
[2025-04-02 19:32:53 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][168/234]	eta 0:00:58 lr 0.000836	time 0.8798 (0.8861)	loss 0.5408 (0.4927)	grad_norm 2.4912 (2.9887)	mem 20675MB
[2025-04-02 19:32:55 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][170/234]	eta 0:00:56 lr 0.000835	time 0.8803 (0.8860)	loss 0.5754 (0.4932)	grad_norm 3.6296 (2.9897)	mem 20675MB
[2025-04-02 19:32:57 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][172/234]	eta 0:00:54 lr 0.000835	time 0.8800 (0.8860)	loss 0.3917 (0.4929)	grad_norm 3.7405 (2.9921)	mem 20675MB
[2025-04-02 19:32:58 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][174/234]	eta 0:00:53 lr 0.000834	time 0.8794 (0.8859)	loss 0.4014 (0.4925)	grad_norm 2.5731 (2.9853)	mem 20675MB
[2025-04-02 19:33:00 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][176/234]	eta 0:00:51 lr 0.000834	time 0.8796 (0.8859)	loss 0.4729 (0.4925)	grad_norm 3.7067 (2.9867)	mem 20675MB
[2025-04-02 19:33:02 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][178/234]	eta 0:00:49 lr 0.000833	time 0.8785 (0.8858)	loss 0.3732 (0.4923)	grad_norm 2.5863 (2.9840)	mem 20675MB
[2025-04-02 19:33:04 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][180/234]	eta 0:00:47 lr 0.000833	time 0.8802 (0.8857)	loss 0.3637 (0.4921)	grad_norm 2.7720 (2.9795)	mem 20675MB
[2025-04-02 19:33:05 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][182/234]	eta 0:00:46 lr 0.000832	time 0.8797 (0.8857)	loss 0.5020 (0.4923)	grad_norm 2.0531 (2.9790)	mem 20675MB
[2025-04-02 19:33:07 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][184/234]	eta 0:00:44 lr 0.000831	time 0.8794 (0.8856)	loss 0.4125 (0.4916)	grad_norm 3.0576 (2.9782)	mem 20675MB
[2025-04-02 19:33:09 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][186/234]	eta 0:00:42 lr 0.000831	time 0.8784 (0.8856)	loss 0.3904 (0.4912)	grad_norm 4.6293 (2.9801)	mem 20675MB
[2025-04-02 19:33:11 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][188/234]	eta 0:00:40 lr 0.000830	time 0.8797 (0.8855)	loss 0.6037 (0.4921)	grad_norm 1.9481 (2.9717)	mem 20675MB
[2025-04-02 19:33:12 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][190/234]	eta 0:00:38 lr 0.000830	time 0.8791 (0.8854)	loss 0.6091 (0.4931)	grad_norm 2.5357 (2.9647)	mem 20675MB
[2025-04-02 19:33:14 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][192/234]	eta 0:00:37 lr 0.000829	time 0.8801 (0.8854)	loss 0.5310 (0.4936)	grad_norm 1.6308 (2.9575)	mem 20675MB
[2025-04-02 19:33:16 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][194/234]	eta 0:00:35 lr 0.000829	time 0.8790 (0.8854)	loss 0.4453 (0.4936)	grad_norm 2.9587 (2.9523)	mem 20675MB
[2025-04-02 19:33:18 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][196/234]	eta 0:00:33 lr 0.000828	time 0.8794 (0.8853)	loss 0.5374 (0.4938)	grad_norm 4.3417 (2.9598)	mem 20675MB
[2025-04-02 19:33:19 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][198/234]	eta 0:00:31 lr 0.000828	time 0.8798 (0.8853)	loss 0.5273 (0.4937)	grad_norm 1.7182 (2.9560)	mem 20675MB
[2025-04-02 19:33:21 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][200/234]	eta 0:00:30 lr 0.000827	time 0.8800 (0.8852)	loss 0.4564 (0.4932)	grad_norm 2.2265 (2.9499)	mem 20675MB
[2025-04-02 19:33:23 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][202/234]	eta 0:00:28 lr 0.000827	time 0.8793 (0.8851)	loss 0.5862 (0.4934)	grad_norm 4.2542 (2.9595)	mem 20675MB
[2025-04-02 19:33:25 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][204/234]	eta 0:00:26 lr 0.000826	time 0.8790 (0.8851)	loss 0.4788 (0.4933)	grad_norm 3.1296 (2.9609)	mem 20675MB
[2025-04-02 19:33:26 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][206/234]	eta 0:00:24 lr 0.000826	time 0.8805 (0.8851)	loss 0.5152 (0.4927)	grad_norm 3.2298 (2.9619)	mem 20675MB
[2025-04-02 19:33:28 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][208/234]	eta 0:00:23 lr 0.000825	time 0.8795 (0.8850)	loss 0.5517 (0.4929)	grad_norm 2.7808 (2.9666)	mem 20675MB
[2025-04-02 19:33:30 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][210/234]	eta 0:00:21 lr 0.000825	time 0.9092 (0.8851)	loss 0.4028 (0.4917)	grad_norm 2.3227 (2.9662)	mem 20675MB
[2025-04-02 19:33:32 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][212/234]	eta 0:00:19 lr 0.000824	time 0.8842 (0.8852)	loss 0.3439 (0.4907)	grad_norm 4.0632 (2.9720)	mem 20675MB
[2025-04-02 19:33:34 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][214/234]	eta 0:00:17 lr 0.000824	time 0.8800 (0.8851)	loss 0.3489 (0.4902)	grad_norm 3.8791 (2.9748)	mem 20675MB
[2025-04-02 19:33:35 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][216/234]	eta 0:00:15 lr 0.000823	time 0.8801 (0.8851)	loss 0.5049 (0.4903)	grad_norm 2.0424 (2.9706)	mem 20675MB
[2025-04-02 19:33:37 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][218/234]	eta 0:00:14 lr 0.000822	time 0.8821 (0.8851)	loss 0.3646 (0.4890)	grad_norm 2.6324 (2.9700)	mem 20675MB
[2025-04-02 19:33:39 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][220/234]	eta 0:00:12 lr 0.000822	time 0.8789 (0.8851)	loss 0.4856 (0.4887)	grad_norm 2.5471 (2.9658)	mem 20675MB
[2025-04-02 19:33:41 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][222/234]	eta 0:00:10 lr 0.000821	time 0.8791 (0.8851)	loss 0.5450 (0.4886)	grad_norm 2.1065 (2.9643)	mem 20675MB
[2025-04-02 19:33:42 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][224/234]	eta 0:00:08 lr 0.000821	time 0.8894 (0.8851)	loss 0.4520 (0.4888)	grad_norm 4.1290 (2.9691)	mem 20675MB
[2025-04-02 19:33:44 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][226/234]	eta 0:00:07 lr 0.000820	time 0.8775 (0.8851)	loss 0.5138 (0.4889)	grad_norm 2.3669 (2.9650)	mem 20675MB
[2025-04-02 19:33:46 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][228/234]	eta 0:00:05 lr 0.000820	time 0.8791 (0.8851)	loss 0.4807 (0.4884)	grad_norm 2.1927 (2.9679)	mem 20675MB
[2025-04-02 19:33:48 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][230/234]	eta 0:00:03 lr 0.000819	time 0.8777 (0.8850)	loss 0.5190 (0.4882)	grad_norm 1.6741 (2.9649)	mem 20675MB
[2025-04-02 19:33:49 simmim_finetune] (main_finetune.py 252): INFO Train: [11/30][232/234]	eta 0:00:01 lr 0.000819	time 0.8798 (0.8850)	loss 0.4649 (0.4882)	grad_norm 1.8654 (2.9564)	mem 20675MB
[2025-04-02 19:33:51 simmim_finetune] (main_finetune.py 260): INFO EPOCH 11 training takes 0:03:27
[2025-04-02 19:33:53 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 2.304 (2.304)	Loss 0.8900 (0.8900)	Acc@1 56.250 (56.250)	Mem 20675MB
[2025-04-02 19:33:53 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 65.746
[2025-04-02 19:33:53 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 65.7%
[2025-04-02 19:33:53 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 68.51%
[2025-04-02 19:33:53 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.112118941621678e-06, 3.112118941621678e-06, 4.741395468492378e-06, 4.741395468492378e-06, 7.247974740601148e-06, 7.247974740601148e-06, 1.110425054384541e-05, 1.110425054384541e-05, 1.7036982548836577e-05, 1.7036982548836577e-05, 2.6164262556515302e-05, 2.6164262556515302e-05, 4.020623179909795e-05, 4.020623179909795e-05, 6.180926140307124e-05, 6.180926140307124e-05, 9.504469156303018e-05, 9.504469156303018e-05, 0.00014617612257835163, 0.00014617612257835163, 0.00022483986260192303, 0.00022483986260192303, 0.0003458610010997252, 0.0003458610010997252, 0.0005320473680194209, 0.0005320473680194209, 0.0008184879325112604, 0.0008184879325112604]
[2025-04-02 19:33:56 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][0/234]	eta 0:12:26 lr 0.000818	time 3.1893 (3.1893)	loss 0.5188 (0.5188)	grad_norm 3.2738 (3.2738)	mem 20675MB
[2025-04-02 19:33:58 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][2/234]	eta 0:06:23 lr 0.000818	time 0.8942 (1.6551)	loss 0.5131 (0.5147)	grad_norm 2.5742 (2.6001)	mem 20675MB
[2025-04-02 19:34:00 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][4/234]	eta 0:05:09 lr 0.000817	time 0.8801 (1.3460)	loss 0.4887 (0.4779)	grad_norm 1.8414 (2.7169)	mem 20675MB
[2025-04-02 19:34:02 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][6/234]	eta 0:04:36 lr 0.000817	time 0.8789 (1.2137)	loss 0.3757 (0.4718)	grad_norm 3.1956 (2.7681)	mem 20675MB
[2025-04-02 19:34:04 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][8/234]	eta 0:04:17 lr 0.000816	time 0.8793 (1.1410)	loss 0.6010 (0.4904)	grad_norm 4.1458 (2.8359)	mem 20675MB
[2025-04-02 19:34:05 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][10/234]	eta 0:04:05 lr 0.000816	time 0.8899 (1.0954)	loss 0.4917 (0.4793)	grad_norm 2.5369 (2.8068)	mem 20675MB
[2025-04-02 19:34:07 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][12/234]	eta 0:03:55 lr 0.000815	time 0.8798 (1.0625)	loss 0.5339 (0.4928)	grad_norm 3.1530 (2.9382)	mem 20675MB
[2025-04-02 19:34:09 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][14/234]	eta 0:03:48 lr 0.000814	time 0.8806 (1.0395)	loss 0.4698 (0.4896)	grad_norm 2.9461 (2.9261)	mem 20675MB
[2025-04-02 19:34:11 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][16/234]	eta 0:03:42 lr 0.000814	time 0.8798 (1.0208)	loss 0.5166 (0.4818)	grad_norm 2.2352 (2.9102)	mem 20675MB
[2025-04-02 19:34:12 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][18/234]	eta 0:03:37 lr 0.000813	time 0.8802 (1.0063)	loss 0.3634 (0.4775)	grad_norm 2.5248 (2.8593)	mem 20675MB
[2025-04-02 19:34:14 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][20/234]	eta 0:03:32 lr 0.000813	time 0.8819 (0.9944)	loss 0.4925 (0.4824)	grad_norm 2.2238 (2.7649)	mem 20675MB
[2025-04-02 19:34:16 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][22/234]	eta 0:03:28 lr 0.000812	time 0.8841 (0.9847)	loss 0.3884 (0.4789)	grad_norm 2.2062 (2.7058)	mem 20675MB
[2025-04-02 19:34:18 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][24/234]	eta 0:03:25 lr 0.000812	time 0.8775 (0.9769)	loss 0.5239 (0.4761)	grad_norm 2.3686 (2.6955)	mem 20675MB
[2025-04-02 19:34:19 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][26/234]	eta 0:03:21 lr 0.000811	time 0.8934 (0.9704)	loss 0.3932 (0.4765)	grad_norm 2.2851 (2.7357)	mem 20675MB
[2025-04-02 19:34:21 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][28/234]	eta 0:03:18 lr 0.000811	time 0.8798 (0.9642)	loss 0.4605 (0.4793)	grad_norm 6.3386 (2.8400)	mem 20675MB
[2025-04-02 19:34:23 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][30/234]	eta 0:03:15 lr 0.000810	time 0.8830 (0.9591)	loss 0.4342 (0.4757)	grad_norm 2.4011 (2.8270)	mem 20675MB
[2025-04-02 19:34:25 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][32/234]	eta 0:03:12 lr 0.000810	time 0.8791 (0.9544)	loss 0.3483 (0.4769)	grad_norm 2.3818 (2.8416)	mem 20675MB
[2025-04-02 19:34:27 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][34/234]	eta 0:03:10 lr 0.000809	time 0.8782 (0.9501)	loss 0.3100 (0.4709)	grad_norm 4.8593 (2.9163)	mem 20675MB
[2025-04-02 19:34:28 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][36/234]	eta 0:03:07 lr 0.000809	time 0.8815 (0.9467)	loss 0.5557 (0.4703)	grad_norm 2.6989 (2.8796)	mem 20675MB
[2025-04-02 19:34:30 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][38/234]	eta 0:03:04 lr 0.000808	time 0.8793 (0.9432)	loss 0.4012 (0.4689)	grad_norm 4.2922 (2.9460)	mem 20675MB
[2025-04-02 19:34:32 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][40/234]	eta 0:03:02 lr 0.000808	time 0.8830 (0.9402)	loss 0.5702 (0.4700)	grad_norm 2.9938 (2.9496)	mem 20675MB
[2025-04-02 19:34:34 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][42/234]	eta 0:03:00 lr 0.000807	time 0.8772 (0.9386)	loss 0.4066 (0.4693)	grad_norm 4.5144 (2.9967)	mem 20675MB
[2025-04-02 19:34:35 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][44/234]	eta 0:02:57 lr 0.000806	time 0.8801 (0.9364)	loss 0.5184 (0.4707)	grad_norm 2.7772 (2.9869)	mem 20675MB
[2025-04-02 19:34:37 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][46/234]	eta 0:02:55 lr 0.000806	time 0.8782 (0.9345)	loss 0.4071 (0.4676)	grad_norm 3.8132 (2.9882)	mem 20675MB
[2025-04-02 19:34:39 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][48/234]	eta 0:02:53 lr 0.000805	time 0.8771 (0.9325)	loss 0.3888 (0.4646)	grad_norm 2.0915 (2.9981)	mem 20675MB
[2025-04-02 19:34:41 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][50/234]	eta 0:02:51 lr 0.000805	time 0.8800 (0.9306)	loss 0.3624 (0.4609)	grad_norm 4.5148 (3.0444)	mem 20675MB
[2025-04-02 19:34:42 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][52/234]	eta 0:02:49 lr 0.000804	time 0.8771 (0.9286)	loss 0.3918 (0.4585)	grad_norm 6.8772 (3.1014)	mem 20675MB
[2025-04-02 19:34:44 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][54/234]	eta 0:02:46 lr 0.000804	time 0.8785 (0.9268)	loss 0.4589 (0.4581)	grad_norm 4.9549 (3.1287)	mem 20675MB
[2025-04-02 19:34:46 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][56/234]	eta 0:02:44 lr 0.000803	time 0.8924 (0.9255)	loss 0.5757 (0.4611)	grad_norm 3.6896 (3.1457)	mem 20675MB
[2025-04-02 19:34:48 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][58/234]	eta 0:02:42 lr 0.000803	time 0.8785 (0.9239)	loss 0.3688 (0.4607)	grad_norm 5.3538 (3.2081)	mem 20675MB
[2025-04-02 19:34:50 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][60/234]	eta 0:02:40 lr 0.000802	time 0.8811 (0.9225)	loss 0.5643 (0.4608)	grad_norm 3.1950 (3.2423)	mem 20675MB
[2025-04-02 19:34:51 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][62/234]	eta 0:02:38 lr 0.000802	time 0.8787 (0.9212)	loss 0.4468 (0.4609)	grad_norm 4.1430 (3.2440)	mem 20675MB
[2025-04-02 19:34:53 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][64/234]	eta 0:02:36 lr 0.000801	time 0.8775 (0.9199)	loss 0.5258 (0.4602)	grad_norm 3.3405 (3.2479)	mem 20675MB
[2025-04-02 19:34:55 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][66/234]	eta 0:02:34 lr 0.000801	time 0.9267 (0.9194)	loss 0.4925 (0.4620)	grad_norm 2.3358 (3.2242)	mem 20675MB
[2025-04-02 19:34:57 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][68/234]	eta 0:02:32 lr 0.000800	time 0.8777 (0.9183)	loss 0.5150 (0.4635)	grad_norm 3.2536 (3.2090)	mem 20675MB
[2025-04-02 19:34:58 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][70/234]	eta 0:02:30 lr 0.000800	time 0.8928 (0.9176)	loss 0.3772 (0.4614)	grad_norm 3.8190 (3.2393)	mem 20675MB
[2025-04-02 19:35:00 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][72/234]	eta 0:02:28 lr 0.000799	time 0.8792 (0.9167)	loss 0.5066 (0.4628)	grad_norm 3.0991 (3.2158)	mem 20675MB
[2025-04-02 19:35:02 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][74/234]	eta 0:02:26 lr 0.000798	time 0.8806 (0.9160)	loss 0.5223 (0.4641)	grad_norm 3.5820 (3.2065)	mem 20675MB
[2025-04-02 19:35:04 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][76/234]	eta 0:02:24 lr 0.000798	time 0.8894 (0.9153)	loss 0.4401 (0.4638)	grad_norm 2.8436 (3.2455)	mem 20675MB
[2025-04-02 19:35:06 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][78/234]	eta 0:02:22 lr 0.000797	time 0.8789 (0.9145)	loss 0.3169 (0.4607)	grad_norm 4.9106 (3.2824)	mem 20675MB
[2025-04-02 19:35:07 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][80/234]	eta 0:02:20 lr 0.000797	time 0.8804 (0.9137)	loss 0.4658 (0.4593)	grad_norm 3.0077 (3.2835)	mem 20675MB
[2025-04-02 19:35:09 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][82/234]	eta 0:02:18 lr 0.000796	time 0.8918 (0.9130)	loss 0.5414 (0.4607)	grad_norm 2.8627 (3.2728)	mem 20675MB
[2025-04-02 19:35:11 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][84/234]	eta 0:02:16 lr 0.000796	time 0.8887 (0.9125)	loss 0.5452 (0.4628)	grad_norm 2.1026 (3.2679)	mem 20675MB
[2025-04-02 19:35:13 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][86/234]	eta 0:02:14 lr 0.000795	time 0.8797 (0.9118)	loss 0.4735 (0.4638)	grad_norm 2.7138 (3.2543)	mem 20675MB
[2025-04-02 19:35:14 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][88/234]	eta 0:02:13 lr 0.000795	time 0.8821 (0.9111)	loss 0.4751 (0.4638)	grad_norm 2.0621 (3.2333)	mem 20675MB
[2025-04-02 19:35:16 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][90/234]	eta 0:02:11 lr 0.000794	time 0.8779 (0.9104)	loss 0.4886 (0.4652)	grad_norm 1.6846 (3.2057)	mem 20675MB
[2025-04-02 19:35:18 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][92/234]	eta 0:02:09 lr 0.000794	time 0.8991 (0.9100)	loss 0.3989 (0.4645)	grad_norm 1.7933 (3.1794)	mem 20675MB
[2025-04-02 19:35:20 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][94/234]	eta 0:02:07 lr 0.000793	time 0.9001 (0.9097)	loss 0.3536 (0.4638)	grad_norm 4.5776 (3.1837)	mem 20675MB
[2025-04-02 19:35:21 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][96/234]	eta 0:02:05 lr 0.000793	time 0.8813 (0.9091)	loss 0.3541 (0.4639)	grad_norm 3.5188 (3.1850)	mem 20675MB
[2025-04-02 19:35:23 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][98/234]	eta 0:02:03 lr 0.000792	time 0.8811 (0.9086)	loss 0.5006 (0.4638)	grad_norm 2.2942 (3.1768)	mem 20675MB
[2025-04-02 19:35:25 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][100/234]	eta 0:02:01 lr 0.000791	time 0.8790 (0.9081)	loss 0.3863 (0.4640)	grad_norm 3.0788 (3.1674)	mem 20675MB
[2025-04-02 19:35:27 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][102/234]	eta 0:01:59 lr 0.000791	time 0.8796 (0.9076)	loss 0.5617 (0.4638)	grad_norm 3.6424 (3.1873)	mem 20675MB
[2025-04-02 19:35:29 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][104/234]	eta 0:01:57 lr 0.000790	time 0.8788 (0.9071)	loss 0.5463 (0.4651)	grad_norm 3.4218 (3.1917)	mem 20675MB
[2025-04-02 19:35:30 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][106/234]	eta 0:01:56 lr 0.000790	time 0.8782 (0.9066)	loss 0.4647 (0.4655)	grad_norm 2.6359 (3.1814)	mem 20675MB
[2025-04-02 19:35:32 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][108/234]	eta 0:01:54 lr 0.000789	time 0.8790 (0.9061)	loss 0.5146 (0.4667)	grad_norm 2.7335 (3.1717)	mem 20675MB
[2025-04-02 19:35:34 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][110/234]	eta 0:01:52 lr 0.000789	time 0.8785 (0.9057)	loss 0.3884 (0.4657)	grad_norm 2.9837 (3.1672)	mem 20675MB
[2025-04-02 19:35:36 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][112/234]	eta 0:01:50 lr 0.000788	time 0.8780 (0.9052)	loss 0.4424 (0.4648)	grad_norm 2.9766 (3.1592)	mem 20675MB
[2025-04-02 19:35:37 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][114/234]	eta 0:01:48 lr 0.000788	time 0.8777 (0.9048)	loss 0.4437 (0.4654)	grad_norm 1.5880 (3.1392)	mem 20675MB
[2025-04-02 19:35:39 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][116/234]	eta 0:01:46 lr 0.000787	time 0.8777 (0.9044)	loss 0.4842 (0.4653)	grad_norm 4.0665 (3.1656)	mem 20675MB
[2025-04-02 19:35:41 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][118/234]	eta 0:01:44 lr 0.000787	time 0.8779 (0.9039)	loss 0.5327 (0.4670)	grad_norm 1.7922 (3.1592)	mem 20675MB
[2025-04-02 19:35:43 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][120/234]	eta 0:01:43 lr 0.000786	time 0.8785 (0.9036)	loss 0.5965 (0.4690)	grad_norm 2.6587 (3.1493)	mem 20675MB
[2025-04-02 19:35:44 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][122/234]	eta 0:01:41 lr 0.000786	time 0.8789 (0.9032)	loss 0.5109 (0.4689)	grad_norm 3.2622 (3.1410)	mem 20675MB
[2025-04-02 19:35:46 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][124/234]	eta 0:01:39 lr 0.000785	time 0.8780 (0.9028)	loss 0.4834 (0.4693)	grad_norm 2.6666 (3.1263)	mem 20675MB
[2025-04-02 19:35:48 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][126/234]	eta 0:01:37 lr 0.000784	time 0.8785 (0.9026)	loss 0.5256 (0.4705)	grad_norm 3.0009 (3.1116)	mem 20675MB
[2025-04-02 19:35:50 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][128/234]	eta 0:01:35 lr 0.000784	time 0.8782 (0.9022)	loss 0.5237 (0.4714)	grad_norm 2.8083 (3.1007)	mem 20675MB
[2025-04-02 19:35:51 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][130/234]	eta 0:01:33 lr 0.000783	time 0.8784 (0.9019)	loss 0.5225 (0.4720)	grad_norm 2.0864 (3.0911)	mem 20675MB
[2025-04-02 19:35:53 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][132/234]	eta 0:01:31 lr 0.000783	time 0.8785 (0.9016)	loss 0.5597 (0.4724)	grad_norm 2.6062 (3.0821)	mem 20675MB
[2025-04-02 19:35:55 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][134/234]	eta 0:01:30 lr 0.000782	time 0.8811 (0.9013)	loss 0.3663 (0.4726)	grad_norm 2.4263 (3.0745)	mem 20675MB
[2025-04-02 19:35:57 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][136/234]	eta 0:01:28 lr 0.000782	time 0.8785 (0.9011)	loss 0.4842 (0.4721)	grad_norm 2.0919 (3.0692)	mem 20675MB
[2025-04-02 19:35:58 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][138/234]	eta 0:01:26 lr 0.000781	time 0.8776 (0.9008)	loss 0.3434 (0.4712)	grad_norm 4.2983 (3.0770)	mem 20675MB
[2025-04-02 19:36:00 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][140/234]	eta 0:01:24 lr 0.000781	time 0.8789 (0.9006)	loss 0.3737 (0.4705)	grad_norm 3.8565 (3.0868)	mem 20675MB
[2025-04-02 19:36:02 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][142/234]	eta 0:01:22 lr 0.000780	time 0.8975 (0.9007)	loss 0.4689 (0.4706)	grad_norm 3.1251 (3.0860)	mem 20675MB
[2025-04-02 19:36:04 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][144/234]	eta 0:01:21 lr 0.000780	time 0.8794 (0.9004)	loss 0.5543 (0.4704)	grad_norm 4.1055 (3.1167)	mem 20675MB
[2025-04-02 19:36:06 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][146/234]	eta 0:01:19 lr 0.000779	time 0.8777 (0.9002)	loss 0.5676 (0.4702)	grad_norm 2.4684 (3.1148)	mem 20675MB
[2025-04-02 19:36:07 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][148/234]	eta 0:01:17 lr 0.000778	time 0.8868 (0.8999)	loss 0.4478 (0.4705)	grad_norm 4.8046 (3.1431)	mem 20675MB
[2025-04-02 19:36:09 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][150/234]	eta 0:01:15 lr 0.000778	time 0.8803 (0.8997)	loss 0.4132 (0.4705)	grad_norm 2.8375 (3.1379)	mem 20675MB
[2025-04-02 19:36:11 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][152/234]	eta 0:01:13 lr 0.000777	time 0.8784 (0.8994)	loss 0.4366 (0.4704)	grad_norm 2.0750 (3.1273)	mem 20675MB
[2025-04-02 19:36:13 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][154/234]	eta 0:01:11 lr 0.000777	time 0.8790 (0.8992)	loss 0.4235 (0.4697)	grad_norm 2.6932 (3.1227)	mem 20675MB
[2025-04-02 19:36:14 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][156/234]	eta 0:01:10 lr 0.000776	time 0.8798 (0.8989)	loss 0.4078 (0.4696)	grad_norm 3.5664 (3.1159)	mem 20675MB
[2025-04-02 19:36:16 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][158/234]	eta 0:01:08 lr 0.000776	time 0.8813 (0.8988)	loss 0.4874 (0.4701)	grad_norm 3.6066 (3.1241)	mem 20675MB
[2025-04-02 19:36:18 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][160/234]	eta 0:01:06 lr 0.000775	time 0.8802 (0.8985)	loss 0.4854 (0.4701)	grad_norm 2.4274 (3.1190)	mem 20675MB
[2025-04-02 19:36:20 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][162/234]	eta 0:01:04 lr 0.000775	time 0.8843 (0.8984)	loss 0.4982 (0.4701)	grad_norm 2.2363 (3.1172)	mem 20675MB
[2025-04-02 19:36:21 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][164/234]	eta 0:01:02 lr 0.000774	time 0.8805 (0.8981)	loss 0.4057 (0.4705)	grad_norm 2.8045 (3.1132)	mem 20675MB
[2025-04-02 19:36:23 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][166/234]	eta 0:01:01 lr 0.000774	time 0.8790 (0.8979)	loss 0.5045 (0.4712)	grad_norm 2.3350 (3.1091)	mem 20675MB
[2025-04-02 19:36:25 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][168/234]	eta 0:00:59 lr 0.000773	time 0.8773 (0.8977)	loss 0.5504 (0.4719)	grad_norm 2.8086 (3.1039)	mem 20675MB
[2025-04-02 19:36:27 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][170/234]	eta 0:00:57 lr 0.000772	time 0.8947 (0.8976)	loss 0.4740 (0.4718)	grad_norm 2.6754 (3.0951)	mem 20675MB
[2025-04-02 19:36:29 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][172/234]	eta 0:00:55 lr 0.000772	time 0.8798 (0.8974)	loss 0.3372 (0.4707)	grad_norm 3.8603 (3.0955)	mem 20675MB
[2025-04-02 19:36:30 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][174/234]	eta 0:00:53 lr 0.000771	time 0.8794 (0.8972)	loss 0.4192 (0.4700)	grad_norm 2.5135 (3.0975)	mem 20675MB
[2025-04-02 19:36:32 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][176/234]	eta 0:00:52 lr 0.000771	time 0.8783 (0.8971)	loss 0.3583 (0.4694)	grad_norm 3.2532 (3.0939)	mem 20675MB
[2025-04-02 19:36:34 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][178/234]	eta 0:00:50 lr 0.000770	time 0.8853 (0.8970)	loss 0.5570 (0.4705)	grad_norm 5.5753 (3.1049)	mem 20675MB
[2025-04-02 19:36:36 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][180/234]	eta 0:00:48 lr 0.000770	time 0.8797 (0.8968)	loss 0.4996 (0.4707)	grad_norm 3.0126 (3.0995)	mem 20675MB
[2025-04-02 19:36:37 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][182/234]	eta 0:00:46 lr 0.000769	time 0.8866 (0.8967)	loss 0.4470 (0.4708)	grad_norm 1.9361 (3.0916)	mem 20675MB
[2025-04-02 19:36:39 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][184/234]	eta 0:00:44 lr 0.000769	time 0.9025 (0.8966)	loss 0.5546 (0.4712)	grad_norm 1.9426 (3.0821)	mem 20675MB
[2025-04-02 19:36:41 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][186/234]	eta 0:00:43 lr 0.000768	time 0.8788 (0.8965)	loss 0.3140 (0.4708)	grad_norm 2.3445 (3.0752)	mem 20675MB
[2025-04-02 19:36:43 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][188/234]	eta 0:00:41 lr 0.000768	time 0.8916 (0.8966)	loss 0.6007 (0.4715)	grad_norm 2.1566 (3.0700)	mem 20675MB
[2025-04-02 19:36:44 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][190/234]	eta 0:00:39 lr 0.000767	time 0.8874 (0.8965)	loss 0.3773 (0.4710)	grad_norm 3.9928 (3.0699)	mem 20675MB
[2025-04-02 19:36:46 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][192/234]	eta 0:00:37 lr 0.000767	time 0.8781 (0.8963)	loss 0.3817 (0.4710)	grad_norm 2.4591 (3.0684)	mem 20675MB
[2025-04-02 19:36:48 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][194/234]	eta 0:00:35 lr 0.000766	time 0.8897 (0.8962)	loss 0.5318 (0.4721)	grad_norm 2.6234 (3.0663)	mem 20675MB
[2025-04-02 19:36:50 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][196/234]	eta 0:00:34 lr 0.000765	time 0.8794 (0.8961)	loss 0.5039 (0.4717)	grad_norm 2.5264 (3.0645)	mem 20675MB
[2025-04-02 19:36:52 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][198/234]	eta 0:00:32 lr 0.000765	time 0.8800 (0.8960)	loss 0.4871 (0.4718)	grad_norm 1.9677 (3.0571)	mem 20675MB
[2025-04-02 19:36:53 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][200/234]	eta 0:00:30 lr 0.000764	time 0.8916 (0.8959)	loss 0.4798 (0.4714)	grad_norm 1.5690 (3.0463)	mem 20675MB
[2025-04-02 19:36:55 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][202/234]	eta 0:00:28 lr 0.000764	time 0.8821 (0.8958)	loss 0.5315 (0.4717)	grad_norm 1.5804 (3.0338)	mem 20675MB
[2025-04-02 19:36:57 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][204/234]	eta 0:00:26 lr 0.000763	time 0.8776 (0.8956)	loss 0.5632 (0.4723)	grad_norm 2.6939 (3.0259)	mem 20675MB
[2025-04-02 19:36:59 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][206/234]	eta 0:00:25 lr 0.000763	time 0.8804 (0.8955)	loss 0.4379 (0.4725)	grad_norm 2.7744 (3.0269)	mem 20675MB
[2025-04-02 19:37:00 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][208/234]	eta 0:00:23 lr 0.000762	time 0.8793 (0.8954)	loss 0.4367 (0.4728)	grad_norm 2.3234 (3.0201)	mem 20675MB
[2025-04-02 19:37:02 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][210/234]	eta 0:00:21 lr 0.000762	time 0.8784 (0.8953)	loss 0.4735 (0.4729)	grad_norm 2.2397 (3.0144)	mem 20675MB
[2025-04-02 19:37:04 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][212/234]	eta 0:00:19 lr 0.000761	time 0.8789 (0.8952)	loss 0.5834 (0.4732)	grad_norm 2.0788 (3.0058)	mem 20675MB
[2025-04-02 19:37:06 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][214/234]	eta 0:00:17 lr 0.000761	time 0.8784 (0.8951)	loss 0.4285 (0.4724)	grad_norm 1.7877 (2.9977)	mem 20675MB
[2025-04-02 19:37:07 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][216/234]	eta 0:00:16 lr 0.000760	time 0.8802 (0.8949)	loss 0.3565 (0.4726)	grad_norm 3.0025 (2.9947)	mem 20675MB
[2025-04-02 19:37:09 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][218/234]	eta 0:00:14 lr 0.000759	time 0.8951 (0.8949)	loss 0.4821 (0.4723)	grad_norm 4.3715 (3.0002)	mem 20675MB
[2025-04-02 19:37:11 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][220/234]	eta 0:00:12 lr 0.000759	time 0.8779 (0.8948)	loss 0.4922 (0.4724)	grad_norm 2.2138 (2.9944)	mem 20675MB
[2025-04-02 19:37:13 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][222/234]	eta 0:00:10 lr 0.000758	time 0.8790 (0.8946)	loss 0.4840 (0.4720)	grad_norm 2.8670 (2.9917)	mem 20675MB
[2025-04-02 19:37:15 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][224/234]	eta 0:00:08 lr 0.000758	time 0.8871 (0.8945)	loss 0.5502 (0.4726)	grad_norm 3.4424 (2.9906)	mem 20675MB
[2025-04-02 19:37:16 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][226/234]	eta 0:00:07 lr 0.000757	time 0.8773 (0.8944)	loss 0.5339 (0.4732)	grad_norm 2.5023 (3.0003)	mem 20675MB
[2025-04-02 19:37:18 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][228/234]	eta 0:00:05 lr 0.000757	time 0.8767 (0.8943)	loss 0.5060 (0.4736)	grad_norm 2.8890 (3.0076)	mem 20675MB
[2025-04-02 19:37:20 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][230/234]	eta 0:00:03 lr 0.000756	time 0.8798 (0.8942)	loss 0.4561 (0.4738)	grad_norm 2.3862 (3.0026)	mem 20675MB
[2025-04-02 19:37:22 simmim_finetune] (main_finetune.py 252): INFO Train: [12/30][232/234]	eta 0:00:01 lr 0.000756	time 0.8772 (0.8941)	loss 0.3589 (0.4731)	grad_norm 3.4690 (3.0057)	mem 20675MB
[2025-04-02 19:37:23 simmim_finetune] (main_finetune.py 260): INFO EPOCH 12 training takes 0:03:29
[2025-04-02 19:37:25 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 2.168 (2.168)	Loss 0.8738 (0.8738)	Acc@1 57.031 (57.031)	Mem 20675MB
[2025-04-02 19:37:25 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 66.298
[2025-04-02 19:37:25 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 66.3%
[2025-04-02 19:37:25 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 68.51%
[2025-04-02 19:37:25 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.8911541739059547e-06, 2.8911541739059547e-06, 4.394645324051297e-06, 4.394645324051297e-06, 6.707708631967209e-06, 6.707708631967209e-06, 1.0266267567222457e-05, 1.0266267567222457e-05, 1.57409736214613e-05, 1.57409736214613e-05, 2.416359832029029e-05, 2.416359832029029e-05, 3.712148247233489e-05, 3.712148247233489e-05, 5.70566888600958e-05, 5.70566888600958e-05, 8.772623714895876e-05, 8.772623714895876e-05, 0.00013491015759336332, 0.00013491015759336332, 0.00020750080443090874, 0.00020750080443090874, 0.0003191787226425171, 0.0003191787226425171, 0.0004909909045065301, 0.0004909909045065301, 0.0007553173381434729, 0.0007553173381434729]
[2025-04-02 19:37:29 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][0/234]	eta 0:14:00 lr 0.000755	time 3.5925 (3.5925)	loss 0.5224 (0.5224)	grad_norm 2.9874 (2.9874)	mem 20675MB
[2025-04-02 19:37:31 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][2/234]	eta 0:06:54 lr 0.000754	time 0.8839 (1.7856)	loss 0.5596 (0.5415)	grad_norm 1.7133 (2.4301)	mem 20675MB
[2025-04-02 19:37:32 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][4/234]	eta 0:05:27 lr 0.000754	time 0.8855 (1.4255)	loss 0.3856 (0.4779)	grad_norm 3.0968 (2.8962)	mem 20675MB
[2025-04-02 19:37:34 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][6/234]	eta 0:04:50 lr 0.000753	time 0.9017 (1.2736)	loss 0.5500 (0.4938)	grad_norm 2.2591 (2.7021)	mem 20675MB
[2025-04-02 19:37:36 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][8/234]	eta 0:04:28 lr 0.000753	time 0.8774 (1.1861)	loss 0.5183 (0.4929)	grad_norm 1.3372 (2.7418)	mem 20675MB
[2025-04-02 19:37:38 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][10/234]	eta 0:04:13 lr 0.000752	time 0.8780 (1.1309)	loss 0.4459 (0.4926)	grad_norm 3.0270 (2.7677)	mem 20675MB
[2025-04-02 19:37:40 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][12/234]	eta 0:04:02 lr 0.000752	time 0.8774 (1.0921)	loss 0.5426 (0.4928)	grad_norm 3.6467 (2.7910)	mem 20675MB
[2025-04-02 19:37:41 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][14/234]	eta 0:03:54 lr 0.000751	time 0.8802 (1.0641)	loss 0.5319 (0.4892)	grad_norm 2.8150 (2.8244)	mem 20675MB
[2025-04-02 19:37:43 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][16/234]	eta 0:03:47 lr 0.000751	time 0.8872 (1.0430)	loss 0.3890 (0.4854)	grad_norm 3.6453 (2.8377)	mem 20675MB
[2025-04-02 19:37:45 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][18/234]	eta 0:03:41 lr 0.000750	time 0.8777 (1.0258)	loss 0.5097 (0.4814)	grad_norm 2.3771 (2.8362)	mem 20675MB
[2025-04-02 19:37:47 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][20/234]	eta 0:03:36 lr 0.000750	time 0.8843 (1.0125)	loss 0.4785 (0.4812)	grad_norm 3.5837 (2.8609)	mem 20675MB
[2025-04-02 19:37:48 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][22/234]	eta 0:03:32 lr 0.000749	time 0.8792 (1.0011)	loss 0.3778 (0.4739)	grad_norm 2.3937 (2.8906)	mem 20675MB
[2025-04-02 19:37:50 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][24/234]	eta 0:03:28 lr 0.000748	time 0.8840 (0.9916)	loss 0.4696 (0.4745)	grad_norm 7.7432 (3.1640)	mem 20675MB
[2025-04-02 19:37:52 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][26/234]	eta 0:03:24 lr 0.000748	time 0.8782 (0.9836)	loss 0.5819 (0.4797)	grad_norm 3.8265 (3.2139)	mem 20675MB
[2025-04-02 19:37:54 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][28/234]	eta 0:03:21 lr 0.000747	time 0.8778 (0.9763)	loss 0.4704 (0.4799)	grad_norm 2.9045 (3.1923)	mem 20675MB
[2025-04-02 19:37:55 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][30/234]	eta 0:03:17 lr 0.000747	time 0.8784 (0.9701)	loss 0.4946 (0.4834)	grad_norm 2.7725 (3.2304)	mem 20675MB
[2025-04-02 19:37:57 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][32/234]	eta 0:03:14 lr 0.000746	time 0.8788 (0.9646)	loss 0.3363 (0.4815)	grad_norm 3.5627 (3.1952)	mem 20675MB
[2025-04-02 19:37:59 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][34/234]	eta 0:03:12 lr 0.000746	time 0.8851 (0.9601)	loss 0.3618 (0.4780)	grad_norm 2.5716 (3.1661)	mem 20675MB
[2025-04-02 19:38:01 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][36/234]	eta 0:03:09 lr 0.000745	time 0.8900 (0.9561)	loss 0.5751 (0.4808)	grad_norm 2.4977 (3.1410)	mem 20675MB
[2025-04-02 19:38:02 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][38/234]	eta 0:03:06 lr 0.000745	time 0.8782 (0.9523)	loss 0.4070 (0.4771)	grad_norm 3.5548 (3.1493)	mem 20675MB
[2025-04-02 19:38:04 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][40/234]	eta 0:03:04 lr 0.000744	time 0.8842 (0.9491)	loss 0.3491 (0.4750)	grad_norm 3.5587 (3.1250)	mem 20675MB
[2025-04-02 19:38:06 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][42/234]	eta 0:03:01 lr 0.000744	time 0.8783 (0.9460)	loss 0.3733 (0.4747)	grad_norm 2.2403 (3.1106)	mem 20675MB
[2025-04-02 19:38:08 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][44/234]	eta 0:02:59 lr 0.000743	time 0.8786 (0.9431)	loss 0.5200 (0.4737)	grad_norm 3.5825 (3.1397)	mem 20675MB
[2025-04-02 19:38:10 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][46/234]	eta 0:02:56 lr 0.000742	time 0.8817 (0.9407)	loss 0.3464 (0.4724)	grad_norm 3.4195 (3.1673)	mem 20675MB
[2025-04-02 19:38:11 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][48/234]	eta 0:02:54 lr 0.000742	time 0.8781 (0.9382)	loss 0.5164 (0.4733)	grad_norm 3.2122 (3.1640)	mem 20675MB
[2025-04-02 19:38:13 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][50/234]	eta 0:02:52 lr 0.000741	time 0.8935 (0.9365)	loss 0.5059 (0.4745)	grad_norm 2.3411 (3.1193)	mem 20675MB
[2025-04-02 19:38:15 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][52/234]	eta 0:02:50 lr 0.000741	time 0.8785 (0.9345)	loss 0.5410 (0.4785)	grad_norm 3.1017 (3.1361)	mem 20675MB
[2025-04-02 19:38:17 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][54/234]	eta 0:02:47 lr 0.000740	time 0.8794 (0.9326)	loss 0.4597 (0.4794)	grad_norm 1.8737 (3.1058)	mem 20675MB
[2025-04-02 19:38:18 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][56/234]	eta 0:02:45 lr 0.000740	time 0.8826 (0.9309)	loss 0.4087 (0.4779)	grad_norm 3.5239 (3.0989)	mem 20675MB
[2025-04-02 19:38:20 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][58/234]	eta 0:02:43 lr 0.000739	time 0.8773 (0.9292)	loss 0.5415 (0.4778)	grad_norm 2.1585 (3.0794)	mem 20675MB
[2025-04-02 19:38:22 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][60/234]	eta 0:02:41 lr 0.000739	time 0.9188 (0.9284)	loss 0.4206 (0.4751)	grad_norm 2.4587 (3.0678)	mem 20675MB
[2025-04-02 19:38:24 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][62/234]	eta 0:02:39 lr 0.000738	time 0.8801 (0.9268)	loss 0.4251 (0.4730)	grad_norm 2.6878 (3.0549)	mem 20675MB
[2025-04-02 19:38:25 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][64/234]	eta 0:02:37 lr 0.000737	time 0.8786 (0.9255)	loss 0.5182 (0.4739)	grad_norm 2.2019 (3.0457)	mem 20675MB
[2025-04-02 19:38:27 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][66/234]	eta 0:02:35 lr 0.000737	time 0.8860 (0.9242)	loss 0.4442 (0.4744)	grad_norm 4.4763 (3.0511)	mem 20675MB
[2025-04-02 19:38:29 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][68/234]	eta 0:02:33 lr 0.000736	time 0.8853 (0.9231)	loss 0.4827 (0.4763)	grad_norm 4.0264 (3.0798)	mem 20675MB
[2025-04-02 19:38:31 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][70/234]	eta 0:02:31 lr 0.000736	time 0.8777 (0.9219)	loss 0.3305 (0.4744)	grad_norm 2.6428 (3.0498)	mem 20675MB
[2025-04-02 19:38:33 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][72/234]	eta 0:02:29 lr 0.000735	time 0.8776 (0.9207)	loss 0.4843 (0.4743)	grad_norm 2.9416 (3.0481)	mem 20675MB
[2025-04-02 19:38:34 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][74/234]	eta 0:02:27 lr 0.000735	time 0.8807 (0.9199)	loss 0.4642 (0.4756)	grad_norm 2.8852 (3.0523)	mem 20675MB
[2025-04-02 19:38:36 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][76/234]	eta 0:02:25 lr 0.000734	time 0.8792 (0.9189)	loss 0.4121 (0.4751)	grad_norm 2.0797 (3.0296)	mem 20675MB
[2025-04-02 19:38:38 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][78/234]	eta 0:02:23 lr 0.000734	time 0.8790 (0.9179)	loss 0.5010 (0.4764)	grad_norm 3.2568 (3.0307)	mem 20675MB
[2025-04-02 19:38:40 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][80/234]	eta 0:02:21 lr 0.000733	time 0.8849 (0.9171)	loss 0.4793 (0.4762)	grad_norm 1.6814 (3.0397)	mem 20675MB
[2025-04-02 19:38:41 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][82/234]	eta 0:02:19 lr 0.000733	time 0.9082 (0.9165)	loss 0.5505 (0.4770)	grad_norm 2.8641 (3.0317)	mem 20675MB
[2025-04-02 19:38:43 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][84/234]	eta 0:02:17 lr 0.000732	time 0.8800 (0.9157)	loss 0.4822 (0.4755)	grad_norm 4.2783 (3.0363)	mem 20675MB
[2025-04-02 19:38:45 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][86/234]	eta 0:02:15 lr 0.000731	time 0.8800 (0.9151)	loss 0.3352 (0.4741)	grad_norm 3.7807 (3.0464)	mem 20675MB
[2025-04-02 19:38:47 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][88/234]	eta 0:02:13 lr 0.000731	time 0.8787 (0.9143)	loss 0.5653 (0.4765)	grad_norm 2.6060 (3.0568)	mem 20675MB
[2025-04-02 19:38:48 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][90/234]	eta 0:02:11 lr 0.000730	time 0.8937 (0.9137)	loss 0.4930 (0.4774)	grad_norm 1.9462 (3.0356)	mem 20675MB
[2025-04-02 19:38:50 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][92/234]	eta 0:02:09 lr 0.000730	time 0.8779 (0.9130)	loss 0.4809 (0.4767)	grad_norm 3.4270 (3.0295)	mem 20675MB
[2025-04-02 19:38:52 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][94/234]	eta 0:02:07 lr 0.000729	time 0.8788 (0.9123)	loss 0.3353 (0.4751)	grad_norm 2.7527 (3.0154)	mem 20675MB
[2025-04-02 19:38:54 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][96/234]	eta 0:02:05 lr 0.000729	time 0.8784 (0.9116)	loss 0.3903 (0.4736)	grad_norm 3.3054 (3.0416)	mem 20675MB
[2025-04-02 19:38:55 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][98/234]	eta 0:02:03 lr 0.000728	time 0.8795 (0.9110)	loss 0.4056 (0.4713)	grad_norm 2.7693 (3.0536)	mem 20675MB
[2025-04-02 19:38:57 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][100/234]	eta 0:02:01 lr 0.000728	time 0.8782 (0.9103)	loss 0.5106 (0.4715)	grad_norm 3.5449 (3.0537)	mem 20675MB
[2025-04-02 19:38:59 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][102/234]	eta 0:02:00 lr 0.000727	time 0.8791 (0.9097)	loss 0.5520 (0.4734)	grad_norm 3.9385 (3.0703)	mem 20675MB
[2025-04-02 19:39:01 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][104/234]	eta 0:01:58 lr 0.000726	time 0.8856 (0.9092)	loss 0.5143 (0.4732)	grad_norm 2.6604 (3.0759)	mem 20675MB
[2025-04-02 19:39:03 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][106/234]	eta 0:01:56 lr 0.000726	time 0.8785 (0.9086)	loss 0.4845 (0.4735)	grad_norm 3.5178 (3.0777)	mem 20675MB
[2025-04-02 19:39:04 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][108/234]	eta 0:01:54 lr 0.000725	time 0.8778 (0.9081)	loss 0.3415 (0.4712)	grad_norm 3.0425 (3.0768)	mem 20675MB
[2025-04-02 19:39:06 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][110/234]	eta 0:01:52 lr 0.000725	time 0.8775 (0.9075)	loss 0.4556 (0.4711)	grad_norm 1.8577 (3.0560)	mem 20675MB
[2025-04-02 19:39:08 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][112/234]	eta 0:01:50 lr 0.000724	time 0.8782 (0.9070)	loss 0.4021 (0.4715)	grad_norm 2.7800 (3.0510)	mem 20675MB
[2025-04-02 19:39:10 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][114/234]	eta 0:01:48 lr 0.000724	time 0.8791 (0.9066)	loss 0.4579 (0.4720)	grad_norm 2.1796 (3.0380)	mem 20675MB
[2025-04-02 19:39:11 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][116/234]	eta 0:01:46 lr 0.000723	time 0.8781 (0.9061)	loss 0.5567 (0.4718)	grad_norm 1.6706 (3.0350)	mem 20675MB
[2025-04-02 19:39:13 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][118/234]	eta 0:01:45 lr 0.000723	time 0.8771 (0.9056)	loss 0.3842 (0.4706)	grad_norm 3.1742 (3.0277)	mem 20675MB
[2025-04-02 19:39:15 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][120/234]	eta 0:01:43 lr 0.000722	time 0.8774 (0.9051)	loss 0.5144 (0.4706)	grad_norm 3.5375 (3.0327)	mem 20675MB
[2025-04-02 19:39:17 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][122/234]	eta 0:01:41 lr 0.000721	time 0.8763 (0.9047)	loss 0.5672 (0.4715)	grad_norm 2.5972 (3.0216)	mem 20675MB
[2025-04-02 19:39:18 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][124/234]	eta 0:01:39 lr 0.000721	time 0.8773 (0.9043)	loss 0.3997 (0.4707)	grad_norm 3.0528 (3.0178)	mem 20675MB
[2025-04-02 19:39:20 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][126/234]	eta 0:01:37 lr 0.000720	time 0.8789 (0.9039)	loss 0.4786 (0.4709)	grad_norm 2.0546 (3.0048)	mem 20675MB
[2025-04-02 19:39:22 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][128/234]	eta 0:01:35 lr 0.000720	time 0.8856 (0.9035)	loss 0.5218 (0.4709)	grad_norm 2.2828 (3.0060)	mem 20675MB
[2025-04-02 19:39:24 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][130/234]	eta 0:01:33 lr 0.000719	time 0.8770 (0.9032)	loss 0.5831 (0.4716)	grad_norm 3.4727 (3.0192)	mem 20675MB
[2025-04-02 19:39:25 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][132/234]	eta 0:01:32 lr 0.000719	time 0.8777 (0.9028)	loss 0.2894 (0.4700)	grad_norm 2.5419 (3.0205)	mem 20675MB
[2025-04-02 19:39:27 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][134/234]	eta 0:01:30 lr 0.000718	time 0.8771 (0.9025)	loss 0.4754 (0.4704)	grad_norm 1.3643 (3.0109)	mem 20675MB
[2025-04-02 19:39:29 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][136/234]	eta 0:01:28 lr 0.000718	time 0.8773 (0.9022)	loss 0.4657 (0.4709)	grad_norm 2.9852 (3.0071)	mem 20675MB
[2025-04-02 19:39:31 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][138/234]	eta 0:01:26 lr 0.000717	time 0.8870 (0.9019)	loss 0.5278 (0.4722)	grad_norm 2.1206 (3.0033)	mem 20675MB
[2025-04-02 19:39:32 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][140/234]	eta 0:01:24 lr 0.000717	time 0.8772 (0.9016)	loss 0.4135 (0.4719)	grad_norm 3.4652 (3.0067)	mem 20675MB
[2025-04-02 19:39:34 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][142/234]	eta 0:01:22 lr 0.000716	time 0.8766 (0.9013)	loss 0.4055 (0.4710)	grad_norm 2.8614 (3.0070)	mem 20675MB
[2025-04-02 19:39:36 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][144/234]	eta 0:01:21 lr 0.000715	time 0.8766 (0.9010)	loss 0.2904 (0.4703)	grad_norm 3.6792 (3.0124)	mem 20675MB
[2025-04-02 19:39:38 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][146/234]	eta 0:01:19 lr 0.000715	time 0.8773 (0.9006)	loss 0.4684 (0.4704)	grad_norm 1.9758 (3.0011)	mem 20675MB
[2025-04-02 19:39:39 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][148/234]	eta 0:01:17 lr 0.000714	time 0.8790 (0.9004)	loss 0.5149 (0.4704)	grad_norm 4.4522 (3.0074)	mem 20675MB
[2025-04-02 19:39:41 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][150/234]	eta 0:01:15 lr 0.000714	time 0.8964 (0.9003)	loss 0.6159 (0.4706)	grad_norm 3.8093 (3.0123)	mem 20675MB
[2025-04-02 19:39:43 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][152/234]	eta 0:01:13 lr 0.000713	time 0.8787 (0.9000)	loss 0.5294 (0.4709)	grad_norm 1.9897 (3.0030)	mem 20675MB
[2025-04-02 19:39:45 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][154/234]	eta 0:01:11 lr 0.000713	time 0.8795 (0.8997)	loss 0.5102 (0.4710)	grad_norm 1.9088 (2.9980)	mem 20675MB
[2025-04-02 19:39:47 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][156/234]	eta 0:01:10 lr 0.000712	time 0.8763 (0.8995)	loss 0.5313 (0.4715)	grad_norm 3.5458 (3.0090)	mem 20675MB
[2025-04-02 19:39:48 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][158/234]	eta 0:01:08 lr 0.000712	time 0.8782 (0.8992)	loss 0.5230 (0.4720)	grad_norm 2.4444 (3.0038)	mem 20675MB
[2025-04-02 19:39:50 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][160/234]	eta 0:01:06 lr 0.000711	time 0.8771 (0.8990)	loss 0.5828 (0.4723)	grad_norm 2.8926 (3.0089)	mem 20675MB
[2025-04-02 19:39:52 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][162/234]	eta 0:01:04 lr 0.000710	time 0.8788 (0.8988)	loss 0.4875 (0.4727)	grad_norm 1.9723 (3.0102)	mem 20675MB
[2025-04-02 19:39:54 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][164/234]	eta 0:01:02 lr 0.000710	time 0.8773 (0.8986)	loss 0.4409 (0.4729)	grad_norm 2.5100 (3.0093)	mem 20675MB
[2025-04-02 19:39:55 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][166/234]	eta 0:01:01 lr 0.000709	time 0.8778 (0.8984)	loss 0.3934 (0.4716)	grad_norm 3.5913 (3.0111)	mem 20675MB
[2025-04-02 19:39:57 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][168/234]	eta 0:00:59 lr 0.000709	time 0.8789 (0.8983)	loss 0.5827 (0.4726)	grad_norm 5.2670 (3.0258)	mem 20675MB
[2025-04-02 19:39:59 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][170/234]	eta 0:00:57 lr 0.000708	time 0.8775 (0.8981)	loss 0.5235 (0.4735)	grad_norm 2.6491 (3.0213)	mem 20675MB
[2025-04-02 19:40:01 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][172/234]	eta 0:00:55 lr 0.000708	time 0.8843 (0.8979)	loss 0.4572 (0.4740)	grad_norm 3.6688 (3.0237)	mem 20675MB
[2025-04-02 19:40:02 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][174/234]	eta 0:00:53 lr 0.000707	time 0.8830 (0.8977)	loss 0.4056 (0.4732)	grad_norm 2.2324 (3.0225)	mem 20675MB
[2025-04-02 19:40:04 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][176/234]	eta 0:00:52 lr 0.000707	time 0.8774 (0.8975)	loss 0.4925 (0.4732)	grad_norm 1.8641 (3.0147)	mem 20675MB
[2025-04-02 19:40:06 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][178/234]	eta 0:00:50 lr 0.000706	time 0.8777 (0.8973)	loss 0.5580 (0.4740)	grad_norm 3.8874 (3.0171)	mem 20675MB
[2025-04-02 19:40:08 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][180/234]	eta 0:00:48 lr 0.000705	time 0.8797 (0.8971)	loss 0.4467 (0.4735)	grad_norm 2.7398 (3.0219)	mem 20675MB
[2025-04-02 19:40:09 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][182/234]	eta 0:00:46 lr 0.000705	time 0.8787 (0.8970)	loss 0.4541 (0.4734)	grad_norm 2.2143 (3.0134)	mem 20675MB
[2025-04-02 19:40:11 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][184/234]	eta 0:00:44 lr 0.000704	time 0.8791 (0.8968)	loss 0.4622 (0.4740)	grad_norm 2.9766 (3.0112)	mem 20675MB
[2025-04-02 19:40:13 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][186/234]	eta 0:00:43 lr 0.000704	time 0.8858 (0.8967)	loss 0.5054 (0.4734)	grad_norm 2.5492 (3.0043)	mem 20675MB
[2025-04-02 19:40:15 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][188/234]	eta 0:00:41 lr 0.000703	time 0.8833 (0.8965)	loss 0.4807 (0.4738)	grad_norm 2.3875 (3.0008)	mem 20675MB
[2025-04-02 19:40:17 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][190/234]	eta 0:00:39 lr 0.000703	time 0.8780 (0.8964)	loss 0.5262 (0.4743)	grad_norm 3.0522 (2.9951)	mem 20675MB
[2025-04-02 19:40:18 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][192/234]	eta 0:00:37 lr 0.000702	time 0.8806 (0.8962)	loss 0.6169 (0.4754)	grad_norm 3.9281 (2.9996)	mem 20675MB
[2025-04-02 19:40:20 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][194/234]	eta 0:00:35 lr 0.000702	time 0.8785 (0.8961)	loss 0.5853 (0.4760)	grad_norm 2.0058 (2.9903)	mem 20675MB
[2025-04-02 19:40:22 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][196/234]	eta 0:00:34 lr 0.000701	time 0.8900 (0.8960)	loss 0.4649 (0.4766)	grad_norm 2.8626 (2.9811)	mem 20675MB
[2025-04-02 19:40:24 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][198/234]	eta 0:00:32 lr 0.000700	time 0.8791 (0.8958)	loss 0.5363 (0.4773)	grad_norm 2.2670 (2.9714)	mem 20675MB
[2025-04-02 19:40:25 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][200/234]	eta 0:00:30 lr 0.000700	time 0.8789 (0.8957)	loss 0.4132 (0.4771)	grad_norm 2.2765 (2.9618)	mem 20675MB
[2025-04-02 19:40:27 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][202/234]	eta 0:00:28 lr 0.000699	time 0.8798 (0.8956)	loss 0.4664 (0.4774)	grad_norm 3.3525 (2.9570)	mem 20675MB
[2025-04-02 19:40:29 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][204/234]	eta 0:00:26 lr 0.000699	time 0.8787 (0.8954)	loss 0.4985 (0.4769)	grad_norm 2.6101 (2.9543)	mem 20675MB
[2025-04-02 19:40:31 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][206/234]	eta 0:00:25 lr 0.000698	time 0.8778 (0.8953)	loss 0.4776 (0.4773)	grad_norm 2.2413 (2.9481)	mem 20675MB
[2025-04-02 19:40:32 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][208/234]	eta 0:00:23 lr 0.000698	time 0.8779 (0.8951)	loss 0.5768 (0.4779)	grad_norm 1.7238 (2.9384)	mem 20675MB
[2025-04-02 19:40:34 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][210/234]	eta 0:00:21 lr 0.000697	time 0.8775 (0.8950)	loss 0.3758 (0.4780)	grad_norm 2.5098 (2.9317)	mem 20675MB
[2025-04-02 19:40:36 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][212/234]	eta 0:00:19 lr 0.000697	time 0.8781 (0.8948)	loss 0.4509 (0.4782)	grad_norm 2.1050 (2.9236)	mem 20675MB
[2025-04-02 19:40:38 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][214/234]	eta 0:00:17 lr 0.000696	time 0.8853 (0.8947)	loss 0.4245 (0.4783)	grad_norm 3.1935 (2.9272)	mem 20675MB
[2025-04-02 19:40:39 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][216/234]	eta 0:00:16 lr 0.000695	time 0.8832 (0.8946)	loss 0.5438 (0.4790)	grad_norm 3.4554 (2.9276)	mem 20675MB
[2025-04-02 19:40:41 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][218/234]	eta 0:00:14 lr 0.000695	time 0.8811 (0.8945)	loss 0.4223 (0.4788)	grad_norm 3.0294 (2.9249)	mem 20675MB
[2025-04-02 19:40:43 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][220/234]	eta 0:00:12 lr 0.000694	time 0.8783 (0.8944)	loss 0.5518 (0.4787)	grad_norm 1.7680 (2.9203)	mem 20675MB
[2025-04-02 19:40:45 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][222/234]	eta 0:00:10 lr 0.000694	time 0.8783 (0.8942)	loss 0.5094 (0.4790)	grad_norm 3.1098 (2.9181)	mem 20675MB
[2025-04-02 19:40:46 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][224/234]	eta 0:00:08 lr 0.000693	time 0.8779 (0.8941)	loss 0.4540 (0.4791)	grad_norm 2.3326 (2.9178)	mem 20675MB
[2025-04-02 19:40:48 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][226/234]	eta 0:00:07 lr 0.000693	time 0.8848 (0.8940)	loss 0.5798 (0.4792)	grad_norm 2.4753 (2.9143)	mem 20675MB
[2025-04-02 19:40:50 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][228/234]	eta 0:00:05 lr 0.000692	time 0.8766 (0.8939)	loss 0.4117 (0.4785)	grad_norm 3.6292 (2.9174)	mem 20675MB
[2025-04-02 19:40:52 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][230/234]	eta 0:00:03 lr 0.000692	time 0.8771 (0.8937)	loss 0.4736 (0.4783)	grad_norm 2.7379 (2.9225)	mem 20675MB
[2025-04-02 19:40:54 simmim_finetune] (main_finetune.py 252): INFO Train: [13/30][232/234]	eta 0:00:01 lr 0.000691	time 0.8769 (0.8936)	loss 0.6191 (0.4791)	grad_norm 3.8119 (2.9234)	mem 20675MB
[2025-04-02 19:40:55 simmim_finetune] (main_finetune.py 260): INFO EPOCH 13 training takes 0:03:29
[2025-04-02 19:40:56 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.610 (1.610)	Loss 0.8846 (0.8846)	Acc@1 57.031 (57.031)	Mem 20675MB
[2025-04-02 19:40:57 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 66.851
[2025-04-02 19:40:57 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 66.9%
[2025-04-02 19:40:57 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 68.51%
[2025-04-02 19:40:57 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.6651999431009907e-06, 2.6651999431009907e-06, 4.0400654379515665e-06, 4.0400654379515665e-06, 6.155243122337068e-06, 6.155243122337068e-06, 9.409362636776301e-06, 9.409362636776301e-06, 1.4415700351298195e-05, 1.4415700351298195e-05, 2.2117758373639578e-05, 2.2117758373639578e-05, 3.396707840801093e-05, 3.396707840801093e-05, 5.219680153781299e-05, 5.219680153781299e-05, 8.02425294298162e-05, 8.02425294298162e-05, 0.0001233898031098211, 0.0001233898031098211, 0.0001897702241559825, 0.0001897702241559825, 0.00029189394884238465, 0.00029189394884238465, 0.0004490073714368495, 0.0004490073714368495, 0.0006907203292744877, 0.0006907203292744877]
[2025-04-02 19:41:00 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][0/234]	eta 0:12:42 lr 0.000690	time 3.2605 (3.2605)	loss 0.5339 (0.5339)	grad_norm 2.8494 (2.8494)	mem 20675MB
[2025-04-02 19:41:02 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][2/234]	eta 0:06:28 lr 0.000690	time 0.8803 (1.6743)	loss 0.4494 (0.4634)	grad_norm 3.8362 (3.0959)	mem 20675MB
[2025-04-02 19:41:03 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][4/234]	eta 0:05:12 lr 0.000689	time 0.8785 (1.3569)	loss 0.5414 (0.4678)	grad_norm 2.6637 (3.0441)	mem 20675MB
[2025-04-02 19:41:05 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][6/234]	eta 0:04:38 lr 0.000689	time 0.8790 (1.2206)	loss 0.5395 (0.4729)	grad_norm 2.9989 (3.1118)	mem 20675MB
[2025-04-02 19:41:07 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][8/234]	eta 0:04:18 lr 0.000688	time 0.8805 (1.1450)	loss 0.3582 (0.4607)	grad_norm 3.5280 (3.1653)	mem 20675MB
[2025-04-02 19:41:09 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][10/234]	eta 0:04:05 lr 0.000688	time 0.8790 (1.0968)	loss 0.4354 (0.4581)	grad_norm 2.2146 (2.9912)	mem 20675MB
[2025-04-02 19:41:11 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][12/234]	eta 0:03:56 lr 0.000687	time 0.8784 (1.0633)	loss 0.4502 (0.4624)	grad_norm 2.1680 (2.9101)	mem 20675MB
[2025-04-02 19:41:12 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][14/234]	eta 0:03:48 lr 0.000687	time 0.8788 (1.0388)	loss 0.4629 (0.4716)	grad_norm 1.7242 (2.9904)	mem 20675MB
[2025-04-02 19:41:14 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][16/234]	eta 0:03:42 lr 0.000686	time 0.8795 (1.0202)	loss 0.5654 (0.4786)	grad_norm 2.4477 (2.9836)	mem 20675MB
[2025-04-02 19:41:16 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][18/234]	eta 0:03:37 lr 0.000685	time 0.8797 (1.0057)	loss 0.5157 (0.4849)	grad_norm 3.0444 (2.9655)	mem 20675MB
[2025-04-02 19:41:18 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][20/234]	eta 0:03:32 lr 0.000685	time 0.8786 (0.9940)	loss 0.5058 (0.4891)	grad_norm 3.8467 (3.1051)	mem 20675MB
[2025-04-02 19:41:19 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][22/234]	eta 0:03:28 lr 0.000684	time 0.8781 (0.9840)	loss 0.3349 (0.4805)	grad_norm 2.4129 (3.0103)	mem 20675MB
[2025-04-02 19:41:21 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][24/234]	eta 0:03:24 lr 0.000684	time 0.8783 (0.9756)	loss 0.3521 (0.4762)	grad_norm 4.5679 (3.0589)	mem 20675MB
[2025-04-02 19:41:23 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][26/234]	eta 0:03:21 lr 0.000683	time 0.8808 (0.9693)	loss 0.5561 (0.4821)	grad_norm 2.7499 (3.0142)	mem 20675MB
[2025-04-02 19:41:25 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][28/234]	eta 0:03:18 lr 0.000683	time 0.8796 (0.9632)	loss 0.5596 (0.4824)	grad_norm 3.1172 (3.0512)	mem 20675MB
[2025-04-02 19:41:26 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][30/234]	eta 0:03:15 lr 0.000682	time 0.8781 (0.9580)	loss 0.4978 (0.4833)	grad_norm 5.0000 (3.0870)	mem 20675MB
[2025-04-02 19:41:28 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][32/234]	eta 0:03:12 lr 0.000682	time 0.8783 (0.9533)	loss 0.4434 (0.4811)	grad_norm 2.4672 (3.1008)	mem 20675MB
[2025-04-02 19:41:30 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][34/234]	eta 0:03:09 lr 0.000681	time 0.8779 (0.9491)	loss 0.5687 (0.4857)	grad_norm 2.4519 (3.0624)	mem 20675MB
[2025-04-02 19:41:32 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][36/234]	eta 0:03:07 lr 0.000680	time 0.8791 (0.9454)	loss 0.3971 (0.4819)	grad_norm 2.9281 (3.0807)	mem 20675MB
[2025-04-02 19:41:33 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][38/234]	eta 0:03:04 lr 0.000680	time 0.8796 (0.9421)	loss 0.5695 (0.4878)	grad_norm 2.9210 (3.0682)	mem 20675MB
[2025-04-02 19:41:35 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][40/234]	eta 0:03:02 lr 0.000679	time 0.8899 (0.9395)	loss 0.5613 (0.4871)	grad_norm 2.8232 (3.0492)	mem 20675MB
[2025-04-02 19:41:37 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][42/234]	eta 0:02:59 lr 0.000679	time 0.8828 (0.9368)	loss 0.4595 (0.4879)	grad_norm 2.4652 (3.0083)	mem 20675MB
[2025-04-02 19:41:39 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][44/234]	eta 0:02:57 lr 0.000678	time 0.8782 (0.9345)	loss 0.5275 (0.4886)	grad_norm 1.9096 (2.9811)	mem 20675MB
[2025-04-02 19:41:41 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][46/234]	eta 0:02:55 lr 0.000678	time 0.8782 (0.9322)	loss 0.3240 (0.4845)	grad_norm 2.6181 (2.9748)	mem 20675MB
[2025-04-02 19:41:42 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][48/234]	eta 0:02:53 lr 0.000677	time 0.8775 (0.9301)	loss 0.5682 (0.4837)	grad_norm 2.1715 (2.9747)	mem 20675MB
[2025-04-02 19:41:44 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][50/234]	eta 0:02:50 lr 0.000677	time 0.8826 (0.9283)	loss 0.3427 (0.4785)	grad_norm 4.0281 (3.0450)	mem 20675MB
[2025-04-02 19:41:46 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][52/234]	eta 0:02:48 lr 0.000676	time 0.8850 (0.9267)	loss 0.5123 (0.4804)	grad_norm 3.4752 (3.0602)	mem 20675MB
[2025-04-02 19:41:48 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][54/234]	eta 0:02:46 lr 0.000675	time 0.8807 (0.9253)	loss 0.4237 (0.4801)	grad_norm 3.3391 (3.0639)	mem 20675MB
[2025-04-02 19:41:49 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][56/234]	eta 0:02:44 lr 0.000675	time 0.8786 (0.9237)	loss 0.4830 (0.4820)	grad_norm 4.2897 (3.1132)	mem 20675MB
[2025-04-02 19:41:51 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][58/234]	eta 0:02:42 lr 0.000674	time 0.8787 (0.9223)	loss 0.2846 (0.4775)	grad_norm 2.4239 (3.1102)	mem 20675MB
[2025-04-02 19:41:53 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][60/234]	eta 0:02:40 lr 0.000674	time 0.8799 (0.9209)	loss 0.3597 (0.4748)	grad_norm 3.5682 (3.1293)	mem 20675MB
[2025-04-02 19:41:55 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][62/234]	eta 0:02:38 lr 0.000673	time 0.8809 (0.9196)	loss 0.4756 (0.4749)	grad_norm 3.0751 (3.1141)	mem 20675MB
[2025-04-02 19:41:56 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][64/234]	eta 0:02:36 lr 0.000673	time 0.8809 (0.9186)	loss 0.5364 (0.4752)	grad_norm 2.6425 (3.1236)	mem 20675MB
[2025-04-02 19:41:58 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][66/234]	eta 0:02:34 lr 0.000672	time 0.8782 (0.9176)	loss 0.4434 (0.4744)	grad_norm 2.2039 (3.1007)	mem 20675MB
[2025-04-02 19:42:00 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][68/234]	eta 0:02:32 lr 0.000672	time 0.8784 (0.9165)	loss 0.4851 (0.4765)	grad_norm 1.7594 (3.0960)	mem 20675MB
[2025-04-02 19:42:02 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][70/234]	eta 0:02:30 lr 0.000671	time 0.8788 (0.9157)	loss 0.5360 (0.4760)	grad_norm 2.7288 (3.1143)	mem 20675MB
[2025-04-02 19:42:03 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][72/234]	eta 0:02:28 lr 0.000670	time 0.8882 (0.9148)	loss 0.5475 (0.4755)	grad_norm 3.0016 (3.1193)	mem 20675MB
[2025-04-02 19:42:05 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][74/234]	eta 0:02:26 lr 0.000670	time 0.8810 (0.9140)	loss 0.5226 (0.4767)	grad_norm 1.5901 (3.0893)	mem 20675MB
[2025-04-02 19:42:07 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][76/234]	eta 0:02:24 lr 0.000669	time 0.8789 (0.9131)	loss 0.5158 (0.4785)	grad_norm 2.8248 (3.0710)	mem 20675MB
[2025-04-02 19:42:09 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][78/234]	eta 0:02:22 lr 0.000669	time 0.8793 (0.9123)	loss 0.3108 (0.4748)	grad_norm 2.8500 (3.0497)	mem 20675MB
[2025-04-02 19:42:11 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][80/234]	eta 0:02:20 lr 0.000668	time 0.8798 (0.9115)	loss 0.5530 (0.4752)	grad_norm 2.0965 (3.0395)	mem 20675MB
[2025-04-02 19:42:12 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][82/234]	eta 0:02:18 lr 0.000668	time 0.8829 (0.9109)	loss 0.5002 (0.4772)	grad_norm 2.2632 (3.0306)	mem 20675MB
[2025-04-02 19:42:14 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][84/234]	eta 0:02:16 lr 0.000667	time 0.8796 (0.9103)	loss 0.5290 (0.4781)	grad_norm 3.3557 (3.0340)	mem 20675MB
[2025-04-02 19:42:16 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][86/234]	eta 0:02:14 lr 0.000666	time 0.8947 (0.9098)	loss 0.5003 (0.4784)	grad_norm 3.0325 (3.0325)	mem 20675MB
[2025-04-02 19:42:18 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][88/234]	eta 0:02:12 lr 0.000666	time 0.8787 (0.9092)	loss 0.3572 (0.4754)	grad_norm 2.7997 (3.0225)	mem 20675MB
[2025-04-02 19:42:19 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][90/234]	eta 0:02:10 lr 0.000665	time 0.8784 (0.9086)	loss 0.6021 (0.4783)	grad_norm 2.9155 (3.0166)	mem 20675MB
[2025-04-02 19:42:21 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][92/234]	eta 0:02:08 lr 0.000665	time 0.8780 (0.9080)	loss 0.4756 (0.4785)	grad_norm 2.2452 (3.0243)	mem 20675MB
[2025-04-02 19:42:23 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][94/234]	eta 0:02:07 lr 0.000664	time 0.8794 (0.9076)	loss 0.5246 (0.4782)	grad_norm 2.3058 (3.0151)	mem 20675MB
[2025-04-02 19:42:25 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][96/234]	eta 0:02:05 lr 0.000664	time 0.8795 (0.9070)	loss 0.4068 (0.4777)	grad_norm 2.8890 (3.0215)	mem 20675MB
[2025-04-02 19:42:26 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][98/234]	eta 0:02:03 lr 0.000663	time 0.8826 (0.9065)	loss 0.4795 (0.4781)	grad_norm 2.7702 (3.0172)	mem 20675MB
[2025-04-02 19:42:28 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][100/234]	eta 0:02:01 lr 0.000663	time 0.8824 (0.9060)	loss 0.5946 (0.4790)	grad_norm 3.7142 (3.0200)	mem 20675MB
[2025-04-02 19:42:30 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][102/234]	eta 0:01:59 lr 0.000662	time 0.8785 (0.9055)	loss 0.5892 (0.4812)	grad_norm 1.8273 (3.0009)	mem 20675MB
[2025-04-02 19:42:32 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][104/234]	eta 0:01:57 lr 0.000661	time 0.8787 (0.9052)	loss 0.4858 (0.4799)	grad_norm 2.5633 (3.0075)	mem 20675MB
[2025-04-02 19:42:34 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][106/234]	eta 0:01:55 lr 0.000661	time 0.8782 (0.9047)	loss 0.5017 (0.4786)	grad_norm 2.1428 (3.0005)	mem 20675MB
[2025-04-02 19:42:35 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][108/234]	eta 0:01:53 lr 0.000660	time 0.8785 (0.9042)	loss 0.4986 (0.4795)	grad_norm 2.2542 (2.9837)	mem 20675MB
[2025-04-02 19:42:37 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][110/234]	eta 0:01:52 lr 0.000660	time 0.8785 (0.9039)	loss 0.5301 (0.4800)	grad_norm 2.0843 (2.9728)	mem 20675MB
[2025-04-02 19:42:39 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][112/234]	eta 0:01:50 lr 0.000659	time 0.8776 (0.9035)	loss 0.3575 (0.4789)	grad_norm 3.4355 (2.9676)	mem 20675MB
[2025-04-02 19:42:41 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][114/234]	eta 0:01:48 lr 0.000659	time 0.8770 (0.9031)	loss 0.5451 (0.4791)	grad_norm 3.2501 (2.9708)	mem 20675MB
[2025-04-02 19:42:42 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][116/234]	eta 0:01:46 lr 0.000658	time 0.8779 (0.9027)	loss 0.4694 (0.4790)	grad_norm 4.2675 (2.9855)	mem 20675MB
[2025-04-02 19:42:44 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][118/234]	eta 0:01:44 lr 0.000658	time 0.8779 (0.9023)	loss 0.4483 (0.4794)	grad_norm 2.4149 (2.9790)	mem 20675MB
[2025-04-02 19:42:46 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][120/234]	eta 0:01:42 lr 0.000657	time 0.8782 (0.9019)	loss 0.3708 (0.4781)	grad_norm 2.2788 (2.9704)	mem 20675MB
[2025-04-02 19:42:48 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][122/234]	eta 0:01:40 lr 0.000656	time 0.8790 (0.9016)	loss 0.3074 (0.4759)	grad_norm 2.5157 (2.9705)	mem 20675MB
[2025-04-02 19:42:49 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][124/234]	eta 0:01:39 lr 0.000656	time 0.8782 (0.9013)	loss 0.3420 (0.4751)	grad_norm 2.9708 (2.9651)	mem 20675MB
[2025-04-02 19:42:51 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][126/234]	eta 0:01:37 lr 0.000655	time 0.8836 (0.9011)	loss 0.6062 (0.4762)	grad_norm 3.5801 (2.9645)	mem 20675MB
[2025-04-02 19:42:53 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][128/234]	eta 0:01:35 lr 0.000655	time 0.8794 (0.9008)	loss 0.5836 (0.4779)	grad_norm 2.9632 (2.9654)	mem 20675MB
[2025-04-02 19:42:55 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][130/234]	eta 0:01:33 lr 0.000654	time 0.8799 (0.9006)	loss 0.5748 (0.4788)	grad_norm 3.7310 (2.9650)	mem 20675MB
[2025-04-02 19:42:56 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][132/234]	eta 0:01:31 lr 0.000654	time 0.8803 (0.9003)	loss 0.3562 (0.4774)	grad_norm 3.2334 (2.9588)	mem 20675MB
[2025-04-02 19:42:58 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][134/234]	eta 0:01:29 lr 0.000653	time 0.8797 (0.9000)	loss 0.4924 (0.4778)	grad_norm 2.8146 (2.9549)	mem 20675MB
[2025-04-02 19:43:00 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][136/234]	eta 0:01:28 lr 0.000653	time 0.8994 (0.8999)	loss 0.4379 (0.4770)	grad_norm 2.4989 (2.9586)	mem 20675MB
[2025-04-02 19:43:02 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][138/234]	eta 0:01:26 lr 0.000652	time 0.8877 (0.8997)	loss 0.5207 (0.4781)	grad_norm 1.8908 (2.9420)	mem 20675MB
[2025-04-02 19:43:04 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][140/234]	eta 0:01:24 lr 0.000651	time 0.8806 (0.8994)	loss 0.6015 (0.4793)	grad_norm 2.8750 (2.9343)	mem 20675MB
[2025-04-02 19:43:05 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][142/234]	eta 0:01:22 lr 0.000651	time 0.8793 (0.8991)	loss 0.5509 (0.4788)	grad_norm 1.9266 (2.9287)	mem 20675MB
[2025-04-02 19:43:07 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][144/234]	eta 0:01:20 lr 0.000650	time 0.8902 (0.8990)	loss 0.4274 (0.4786)	grad_norm 2.5371 (2.9226)	mem 20675MB
[2025-04-02 19:43:09 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][146/234]	eta 0:01:19 lr 0.000650	time 0.8802 (0.8988)	loss 0.5499 (0.4795)	grad_norm 2.5751 (2.9158)	mem 20675MB
[2025-04-02 19:43:11 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][148/234]	eta 0:01:17 lr 0.000649	time 0.8808 (0.8985)	loss 0.5073 (0.4796)	grad_norm 2.9881 (2.9099)	mem 20675MB
[2025-04-02 19:43:12 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][150/234]	eta 0:01:15 lr 0.000649	time 0.8807 (0.8984)	loss 0.4758 (0.4792)	grad_norm 3.1297 (2.9076)	mem 20675MB
[2025-04-02 19:43:14 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][152/234]	eta 0:01:13 lr 0.000648	time 0.8837 (0.8982)	loss 0.4107 (0.4780)	grad_norm 4.2893 (2.9359)	mem 20675MB
[2025-04-02 19:43:16 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][154/234]	eta 0:01:11 lr 0.000647	time 0.8808 (0.8980)	loss 0.3810 (0.4777)	grad_norm 3.4625 (2.9392)	mem 20675MB
[2025-04-02 19:43:18 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][156/234]	eta 0:01:10 lr 0.000647	time 0.8801 (0.8978)	loss 0.4992 (0.4775)	grad_norm 2.3030 (2.9352)	mem 20675MB
[2025-04-02 19:43:19 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][158/234]	eta 0:01:08 lr 0.000646	time 0.8820 (0.8976)	loss 0.4262 (0.4767)	grad_norm 3.5795 (2.9491)	mem 20675MB
[2025-04-02 19:43:21 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][160/234]	eta 0:01:06 lr 0.000646	time 0.8802 (0.8974)	loss 0.3914 (0.4760)	grad_norm 3.6855 (2.9599)	mem 20675MB
[2025-04-02 19:43:23 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][162/234]	eta 0:01:04 lr 0.000645	time 0.8806 (0.8972)	loss 0.5121 (0.4759)	grad_norm 3.1217 (2.9687)	mem 20675MB
[2025-04-02 19:43:25 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][164/234]	eta 0:01:02 lr 0.000645	time 0.8796 (0.8971)	loss 0.4489 (0.4760)	grad_norm 5.4232 (2.9940)	mem 20675MB
[2025-04-02 19:43:26 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][166/234]	eta 0:01:00 lr 0.000644	time 0.8789 (0.8969)	loss 0.5104 (0.4767)	grad_norm 1.9346 (2.9960)	mem 20675MB
[2025-04-02 19:43:28 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][168/234]	eta 0:00:59 lr 0.000644	time 0.8801 (0.8967)	loss 0.5262 (0.4765)	grad_norm 1.9531 (2.9870)	mem 20675MB
[2025-04-02 19:43:30 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][170/234]	eta 0:00:57 lr 0.000643	time 0.8788 (0.8965)	loss 0.4493 (0.4767)	grad_norm 2.7663 (2.9896)	mem 20675MB
[2025-04-02 19:43:32 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][172/234]	eta 0:00:55 lr 0.000642	time 0.8797 (0.8964)	loss 0.3533 (0.4763)	grad_norm 2.7501 (2.9851)	mem 20675MB
[2025-04-02 19:43:34 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][174/234]	eta 0:00:53 lr 0.000642	time 0.8873 (0.8962)	loss 0.4140 (0.4761)	grad_norm 3.2942 (2.9788)	mem 20675MB
[2025-04-02 19:43:35 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][176/234]	eta 0:00:51 lr 0.000641	time 0.8800 (0.8961)	loss 0.5013 (0.4762)	grad_norm 4.7790 (2.9804)	mem 20675MB
[2025-04-02 19:43:37 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][178/234]	eta 0:00:50 lr 0.000641	time 0.8802 (0.8959)	loss 0.6003 (0.4763)	grad_norm 3.3265 (2.9831)	mem 20675MB
[2025-04-02 19:43:39 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][180/234]	eta 0:00:48 lr 0.000640	time 0.8788 (0.8957)	loss 0.5521 (0.4771)	grad_norm 1.6236 (2.9700)	mem 20675MB
[2025-04-02 19:43:41 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][182/234]	eta 0:00:46 lr 0.000640	time 0.8789 (0.8956)	loss 0.3172 (0.4767)	grad_norm 3.8226 (2.9697)	mem 20675MB
[2025-04-02 19:43:42 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][184/234]	eta 0:00:44 lr 0.000639	time 0.8788 (0.8954)	loss 0.5664 (0.4770)	grad_norm 1.8765 (2.9635)	mem 20675MB
[2025-04-02 19:43:44 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][186/234]	eta 0:00:42 lr 0.000639	time 0.8848 (0.8953)	loss 0.5450 (0.4773)	grad_norm 2.2636 (2.9574)	mem 20675MB
[2025-04-02 19:43:46 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][188/234]	eta 0:00:41 lr 0.000638	time 0.8801 (0.8951)	loss 0.3706 (0.4760)	grad_norm 4.2120 (2.9642)	mem 20675MB
[2025-04-02 19:43:48 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][190/234]	eta 0:00:39 lr 0.000637	time 0.8796 (0.8950)	loss 0.5177 (0.4759)	grad_norm 1.9977 (2.9627)	mem 20675MB
[2025-04-02 19:43:49 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][192/234]	eta 0:00:37 lr 0.000637	time 0.8810 (0.8949)	loss 0.5052 (0.4753)	grad_norm 3.1165 (2.9657)	mem 20675MB
[2025-04-02 19:43:51 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][194/234]	eta 0:00:35 lr 0.000636	time 0.8794 (0.8947)	loss 0.4180 (0.4753)	grad_norm 1.9790 (2.9572)	mem 20675MB
[2025-04-02 19:43:53 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][196/234]	eta 0:00:33 lr 0.000636	time 0.8799 (0.8946)	loss 0.4631 (0.4753)	grad_norm 2.3598 (2.9571)	mem 20675MB
[2025-04-02 19:43:55 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][198/234]	eta 0:00:32 lr 0.000635	time 0.8799 (0.8945)	loss 0.4387 (0.4754)	grad_norm 2.6493 (2.9531)	mem 20675MB
[2025-04-02 19:43:56 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][200/234]	eta 0:00:30 lr 0.000635	time 0.8959 (0.8944)	loss 0.3400 (0.4745)	grad_norm 3.4699 (2.9543)	mem 20675MB
[2025-04-02 19:43:58 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][202/234]	eta 0:00:28 lr 0.000634	time 0.8796 (0.8943)	loss 0.5180 (0.4752)	grad_norm 4.2081 (2.9590)	mem 20675MB
[2025-04-02 19:44:00 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][204/234]	eta 0:00:26 lr 0.000634	time 0.8797 (0.8942)	loss 0.5621 (0.4756)	grad_norm 4.0338 (2.9616)	mem 20675MB
[2025-04-02 19:44:02 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][206/234]	eta 0:00:25 lr 0.000633	time 0.8795 (0.8940)	loss 0.3697 (0.4749)	grad_norm 2.6448 (2.9566)	mem 20675MB
[2025-04-02 19:44:04 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][208/234]	eta 0:00:23 lr 0.000632	time 0.8790 (0.8940)	loss 0.4277 (0.4747)	grad_norm 2.1626 (2.9557)	mem 20675MB
[2025-04-02 19:44:05 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][210/234]	eta 0:00:21 lr 0.000632	time 0.8786 (0.8938)	loss 0.5684 (0.4753)	grad_norm 2.0335 (2.9477)	mem 20675MB
[2025-04-02 19:44:07 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][212/234]	eta 0:00:19 lr 0.000631	time 0.8792 (0.8937)	loss 0.4431 (0.4756)	grad_norm 2.6546 (2.9457)	mem 20675MB
[2025-04-02 19:44:09 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][214/234]	eta 0:00:17 lr 0.000631	time 0.8791 (0.8936)	loss 0.4493 (0.4754)	grad_norm 2.2653 (2.9401)	mem 20675MB
[2025-04-02 19:44:11 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][216/234]	eta 0:00:16 lr 0.000630	time 0.8782 (0.8935)	loss 0.4702 (0.4755)	grad_norm 1.9670 (2.9331)	mem 20675MB
[2025-04-02 19:44:12 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][218/234]	eta 0:00:14 lr 0.000630	time 0.9025 (0.8935)	loss 0.3378 (0.4750)	grad_norm 3.0209 (2.9290)	mem 20675MB
[2025-04-02 19:44:14 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][220/234]	eta 0:00:12 lr 0.000629	time 0.8859 (0.8934)	loss 0.3277 (0.4743)	grad_norm 2.5365 (2.9244)	mem 20675MB
[2025-04-02 19:44:16 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][222/234]	eta 0:00:10 lr 0.000628	time 0.8789 (0.8933)	loss 0.3829 (0.4742)	grad_norm 4.1599 (2.9270)	mem 20675MB
[2025-04-02 19:44:18 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][224/234]	eta 0:00:08 lr 0.000628	time 0.8789 (0.8932)	loss 0.4690 (0.4744)	grad_norm 2.8834 (2.9252)	mem 20675MB
[2025-04-02 19:44:19 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][226/234]	eta 0:00:07 lr 0.000627	time 0.8789 (0.8931)	loss 0.5027 (0.4750)	grad_norm 2.2224 (2.9249)	mem 20675MB
[2025-04-02 19:44:21 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][228/234]	eta 0:00:05 lr 0.000627	time 0.8779 (0.8929)	loss 0.4073 (0.4749)	grad_norm 3.6384 (2.9305)	mem 20675MB
[2025-04-02 19:44:23 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][230/234]	eta 0:00:03 lr 0.000626	time 0.8781 (0.8928)	loss 0.5360 (0.4755)	grad_norm 2.6244 (2.9287)	mem 20675MB
[2025-04-02 19:44:25 simmim_finetune] (main_finetune.py 252): INFO Train: [14/30][232/234]	eta 0:00:01 lr 0.000626	time 0.8781 (0.8927)	loss 0.5086 (0.4759)	grad_norm 2.7066 (2.9324)	mem 20675MB
[2025-04-02 19:44:26 simmim_finetune] (main_finetune.py 260): INFO EPOCH 14 training takes 0:03:29
[2025-04-02 19:44:27 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.720 (1.720)	Loss 0.7752 (0.7752)	Acc@1 61.719 (61.719)	Mem 20675MB
[2025-04-02 19:44:28 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 69.613
[2025-04-02 19:44:28 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 69.6%
[2025-04-02 19:44:28 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 69.61%
[2025-04-02 19:44:28 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.4367318510434442e-06, 2.4367318510434442e-06, 3.6815406616259004e-06, 3.6815406616259004e-06, 5.5966311394450645e-06, 5.5966311394450645e-06, 8.542924182243778e-06, 8.542924182243778e-06, 1.3075682709626412e-05, 1.3075682709626412e-05, 2.004915736713816e-05, 2.004915736713816e-05, 3.077757991715623e-05, 3.077757991715623e-05, 4.728284537872249e-05, 4.728284537872249e-05, 7.267556147343982e-05, 7.267556147343982e-05, 0.00011174127854223572, 0.00011174127854223572, 0.0001718423817249986, 0.0001718423817249986, 0.00026430561739078763, 0.00026430561739078763, 0.0004065567491843093, 0.0004065567491843093, 0.0006254046442512657, 0.0006254046442512657]
[2025-04-02 19:44:30 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][0/234]	eta 0:08:24 lr 0.000625	time 2.1548 (2.1548)	loss 0.3371 (0.3371)	grad_norm 2.2356 (2.2356)	mem 20675MB
[2025-04-02 19:44:32 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][2/234]	eta 0:05:02 lr 0.000625	time 0.8801 (1.3053)	loss 0.6539 (0.4935)	grad_norm 5.1735 (3.1554)	mem 20675MB
[2025-04-02 19:44:33 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][4/234]	eta 0:04:21 lr 0.000624	time 0.8781 (1.1348)	loss 0.5012 (0.4646)	grad_norm 2.8478 (3.3475)	mem 20675MB
[2025-04-02 19:44:35 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][6/234]	eta 0:04:02 lr 0.000623	time 0.8789 (1.0618)	loss 0.3445 (0.4482)	grad_norm 2.7758 (3.1321)	mem 20675MB
[2025-04-02 19:44:37 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][8/234]	eta 0:03:50 lr 0.000623	time 0.8790 (1.0213)	loss 0.3925 (0.4483)	grad_norm 2.3833 (3.0232)	mem 20675MB
[2025-04-02 19:44:39 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][10/234]	eta 0:03:42 lr 0.000622	time 0.8782 (0.9954)	loss 0.3660 (0.4482)	grad_norm 3.8081 (3.0495)	mem 20675MB
[2025-04-02 19:44:40 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][12/234]	eta 0:03:36 lr 0.000622	time 0.8781 (0.9774)	loss 0.5536 (0.4559)	grad_norm 3.0773 (3.0007)	mem 20675MB
[2025-04-02 19:44:42 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][14/234]	eta 0:03:32 lr 0.000621	time 0.8775 (0.9642)	loss 0.4520 (0.4655)	grad_norm 2.4736 (3.0434)	mem 20675MB
[2025-04-02 19:44:44 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][16/234]	eta 0:03:28 lr 0.000621	time 0.8785 (0.9542)	loss 0.5333 (0.4635)	grad_norm 2.3161 (2.9481)	mem 20675MB
[2025-04-02 19:44:46 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][18/234]	eta 0:03:24 lr 0.000620	time 0.8785 (0.9463)	loss 0.4902 (0.4668)	grad_norm 2.5811 (2.8952)	mem 20675MB
[2025-04-02 19:44:47 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][20/234]	eta 0:03:21 lr 0.000620	time 0.8780 (0.9400)	loss 0.3970 (0.4652)	grad_norm 4.0359 (2.9123)	mem 20675MB
[2025-04-02 19:44:49 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][22/234]	eta 0:03:18 lr 0.000619	time 0.8781 (0.9346)	loss 0.5405 (0.4712)	grad_norm 3.4752 (2.9345)	mem 20675MB
[2025-04-02 19:44:51 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][24/234]	eta 0:03:15 lr 0.000618	time 0.8784 (0.9302)	loss 0.4785 (0.4689)	grad_norm 1.5751 (2.8698)	mem 20675MB
[2025-04-02 19:44:53 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][26/234]	eta 0:03:12 lr 0.000618	time 0.8787 (0.9265)	loss 0.5021 (0.4676)	grad_norm 2.0136 (2.8255)	mem 20675MB
[2025-04-02 19:44:54 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][28/234]	eta 0:03:10 lr 0.000617	time 0.8779 (0.9232)	loss 0.4383 (0.4646)	grad_norm 5.0716 (2.8769)	mem 20675MB
[2025-04-02 19:44:56 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][30/234]	eta 0:03:07 lr 0.000617	time 0.8801 (0.9204)	loss 0.4245 (0.4600)	grad_norm 1.9942 (2.8789)	mem 20675MB
[2025-04-02 19:44:58 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][32/234]	eta 0:03:05 lr 0.000616	time 0.8800 (0.9182)	loss 0.3606 (0.4556)	grad_norm 3.6329 (2.8850)	mem 20675MB
[2025-04-02 19:45:00 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][34/234]	eta 0:03:03 lr 0.000616	time 0.8791 (0.9161)	loss 0.5409 (0.4566)	grad_norm 4.2544 (2.9095)	mem 20675MB
[2025-04-02 19:45:01 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][36/234]	eta 0:03:01 lr 0.000615	time 0.8788 (0.9142)	loss 0.3740 (0.4540)	grad_norm 4.8788 (3.0069)	mem 20675MB
[2025-04-02 19:45:03 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][38/234]	eta 0:02:58 lr 0.000614	time 0.8774 (0.9124)	loss 0.4690 (0.4592)	grad_norm 3.3599 (3.0290)	mem 20675MB
[2025-04-02 19:45:05 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][40/234]	eta 0:02:56 lr 0.000614	time 0.8784 (0.9109)	loss 0.3632 (0.4592)	grad_norm 4.8564 (3.0589)	mem 20675MB
[2025-04-02 19:45:07 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][42/234]	eta 0:02:54 lr 0.000613	time 0.8809 (0.9098)	loss 0.5770 (0.4633)	grad_norm 2.4484 (3.0418)	mem 20675MB
[2025-04-02 19:45:09 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][44/234]	eta 0:02:52 lr 0.000613	time 0.8795 (0.9086)	loss 0.6023 (0.4633)	grad_norm 2.3484 (3.0139)	mem 20675MB
[2025-04-02 19:45:10 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][46/234]	eta 0:02:50 lr 0.000612	time 0.8789 (0.9074)	loss 0.5499 (0.4664)	grad_norm 2.5565 (3.0254)	mem 20675MB
[2025-04-02 19:45:12 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][48/234]	eta 0:02:48 lr 0.000612	time 0.8784 (0.9063)	loss 0.5939 (0.4697)	grad_norm 2.8679 (2.9971)	mem 20675MB
[2025-04-02 19:45:14 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][50/234]	eta 0:02:46 lr 0.000611	time 0.8773 (0.9052)	loss 0.4540 (0.4714)	grad_norm 1.8286 (2.9500)	mem 20675MB
[2025-04-02 19:45:16 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][52/234]	eta 0:02:44 lr 0.000611	time 0.8780 (0.9042)	loss 0.5188 (0.4715)	grad_norm 2.0343 (2.9176)	mem 20675MB
[2025-04-02 19:45:17 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][54/234]	eta 0:02:42 lr 0.000610	time 0.8789 (0.9032)	loss 0.5161 (0.4742)	grad_norm 1.5205 (2.8764)	mem 20675MB
[2025-04-02 19:45:19 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][56/234]	eta 0:02:40 lr 0.000609	time 0.8774 (0.9024)	loss 0.5223 (0.4731)	grad_norm 2.3759 (2.8841)	mem 20675MB
[2025-04-02 19:45:21 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][58/234]	eta 0:02:38 lr 0.000609	time 0.8777 (0.9016)	loss 0.5548 (0.4749)	grad_norm 2.4719 (2.8770)	mem 20675MB
[2025-04-02 19:45:23 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][60/234]	eta 0:02:36 lr 0.000608	time 0.8770 (0.9008)	loss 0.4032 (0.4720)	grad_norm 2.1228 (2.8784)	mem 20675MB
[2025-04-02 19:45:24 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][62/234]	eta 0:02:34 lr 0.000608	time 0.8768 (0.9000)	loss 0.5359 (0.4747)	grad_norm 3.5746 (2.8971)	mem 20675MB
[2025-04-02 19:45:26 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][64/234]	eta 0:02:32 lr 0.000607	time 0.8783 (0.8994)	loss 0.4682 (0.4748)	grad_norm 2.7638 (2.8911)	mem 20675MB
[2025-04-02 19:45:28 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][66/234]	eta 0:02:30 lr 0.000607	time 0.8784 (0.8988)	loss 0.5377 (0.4758)	grad_norm 2.6659 (2.8793)	mem 20675MB
[2025-04-02 19:45:30 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][68/234]	eta 0:02:29 lr 0.000606	time 0.8781 (0.8982)	loss 0.5324 (0.4784)	grad_norm 3.0931 (2.8913)	mem 20675MB
[2025-04-02 19:45:31 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][70/234]	eta 0:02:27 lr 0.000606	time 0.8785 (0.8977)	loss 0.5003 (0.4779)	grad_norm 2.1183 (2.8696)	mem 20675MB
[2025-04-02 19:45:33 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][72/234]	eta 0:02:25 lr 0.000605	time 0.8776 (0.8971)	loss 0.4484 (0.4762)	grad_norm 1.9157 (2.8571)	mem 20675MB
[2025-04-02 19:45:35 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][74/234]	eta 0:02:23 lr 0.000604	time 0.8795 (0.8966)	loss 0.5901 (0.4776)	grad_norm 2.1302 (2.8383)	mem 20675MB
[2025-04-02 19:45:37 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][76/234]	eta 0:02:21 lr 0.000604	time 0.8788 (0.8962)	loss 0.6018 (0.4793)	grad_norm 2.9566 (2.8253)	mem 20675MB
[2025-04-02 19:45:38 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][78/234]	eta 0:02:19 lr 0.000603	time 0.8789 (0.8958)	loss 0.5439 (0.4807)	grad_norm 2.0085 (2.8105)	mem 20675MB
[2025-04-02 19:45:40 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][80/234]	eta 0:02:17 lr 0.000603	time 0.8785 (0.8954)	loss 0.5017 (0.4797)	grad_norm 2.9936 (2.8133)	mem 20675MB
[2025-04-02 19:45:42 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][82/234]	eta 0:02:16 lr 0.000602	time 0.8802 (0.8950)	loss 0.4065 (0.4788)	grad_norm 3.2998 (2.8116)	mem 20675MB
[2025-04-02 19:45:44 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][84/234]	eta 0:02:14 lr 0.000602	time 0.8788 (0.8947)	loss 0.5874 (0.4792)	grad_norm 2.3680 (2.7983)	mem 20675MB
[2025-04-02 19:45:45 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][86/234]	eta 0:02:12 lr 0.000601	time 0.8790 (0.8943)	loss 0.5156 (0.4788)	grad_norm 3.0124 (2.7999)	mem 20675MB
[2025-04-02 19:45:47 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][88/234]	eta 0:02:10 lr 0.000601	time 0.8794 (0.8940)	loss 0.5566 (0.4790)	grad_norm 2.0725 (2.7911)	mem 20675MB
[2025-04-02 19:45:49 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][90/234]	eta 0:02:08 lr 0.000600	time 0.8794 (0.8937)	loss 0.3916 (0.4779)	grad_norm 3.6797 (2.7901)	mem 20675MB
[2025-04-02 19:45:51 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][92/234]	eta 0:02:06 lr 0.000599	time 0.8786 (0.8934)	loss 0.3421 (0.4764)	grad_norm 2.8333 (2.7885)	mem 20675MB
[2025-04-02 19:45:53 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][94/234]	eta 0:02:05 lr 0.000599	time 0.8798 (0.8931)	loss 0.5629 (0.4781)	grad_norm 3.3525 (2.8084)	mem 20675MB
[2025-04-02 19:45:54 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][96/234]	eta 0:02:03 lr 0.000598	time 0.8789 (0.8928)	loss 0.4558 (0.4777)	grad_norm 3.0632 (2.8121)	mem 20675MB
[2025-04-02 19:45:56 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][98/234]	eta 0:02:01 lr 0.000598	time 0.8780 (0.8925)	loss 0.3232 (0.4766)	grad_norm 2.1042 (2.8066)	mem 20675MB
[2025-04-02 19:45:58 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][100/234]	eta 0:01:59 lr 0.000597	time 0.8780 (0.8922)	loss 0.5572 (0.4783)	grad_norm 3.2623 (2.8300)	mem 20675MB
[2025-04-02 19:46:00 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][102/234]	eta 0:01:57 lr 0.000597	time 0.8768 (0.8920)	loss 0.4737 (0.4784)	grad_norm 2.3038 (2.8151)	mem 20675MB
[2025-04-02 19:46:01 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][104/234]	eta 0:01:55 lr 0.000596	time 0.8768 (0.8917)	loss 0.5099 (0.4796)	grad_norm 2.9498 (2.8126)	mem 20675MB
[2025-04-02 19:46:03 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][106/234]	eta 0:01:54 lr 0.000595	time 0.8773 (0.8914)	loss 0.4815 (0.4797)	grad_norm 3.0601 (2.8095)	mem 20675MB
[2025-04-02 19:46:05 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][108/234]	eta 0:01:52 lr 0.000595	time 0.8774 (0.8912)	loss 0.4035 (0.4793)	grad_norm 2.0788 (2.8240)	mem 20675MB
[2025-04-02 19:46:07 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][110/234]	eta 0:01:50 lr 0.000594	time 0.8765 (0.8909)	loss 0.4586 (0.4790)	grad_norm 2.4707 (2.8157)	mem 20675MB
[2025-04-02 19:46:08 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][112/234]	eta 0:01:48 lr 0.000594	time 0.8773 (0.8907)	loss 0.4029 (0.4783)	grad_norm 3.1395 (2.8191)	mem 20675MB
[2025-04-02 19:46:10 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][114/234]	eta 0:01:46 lr 0.000593	time 0.8767 (0.8905)	loss 0.5790 (0.4795)	grad_norm 2.5617 (2.8131)	mem 20675MB
[2025-04-02 19:46:12 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][116/234]	eta 0:01:45 lr 0.000593	time 0.8766 (0.8903)	loss 0.4943 (0.4801)	grad_norm 2.3163 (2.8011)	mem 20675MB
[2025-04-02 19:46:14 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][118/234]	eta 0:01:43 lr 0.000592	time 0.8766 (0.8900)	loss 0.5790 (0.4813)	grad_norm 2.6907 (2.8051)	mem 20675MB
[2025-04-02 19:46:15 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][120/234]	eta 0:01:41 lr 0.000592	time 0.8767 (0.8898)	loss 0.4688 (0.4812)	grad_norm 1.7901 (2.7937)	mem 20675MB
[2025-04-02 19:46:17 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][122/234]	eta 0:01:39 lr 0.000591	time 0.8764 (0.8896)	loss 0.4565 (0.4816)	grad_norm 2.9453 (2.7888)	mem 20675MB
[2025-04-02 19:46:19 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][124/234]	eta 0:01:37 lr 0.000590	time 0.8768 (0.8894)	loss 0.5040 (0.4817)	grad_norm 1.8517 (2.7894)	mem 20675MB
[2025-04-02 19:46:21 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][126/234]	eta 0:01:36 lr 0.000590	time 0.8764 (0.8892)	loss 0.4165 (0.4812)	grad_norm 1.9818 (2.7843)	mem 20675MB
[2025-04-02 19:46:22 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][128/234]	eta 0:01:34 lr 0.000589	time 0.8781 (0.8891)	loss 0.3222 (0.4802)	grad_norm 3.0647 (2.7833)	mem 20675MB
[2025-04-02 19:46:24 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][130/234]	eta 0:01:32 lr 0.000589	time 0.8775 (0.8889)	loss 0.4832 (0.4796)	grad_norm 3.2367 (2.7826)	mem 20675MB
[2025-04-02 19:46:26 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][132/234]	eta 0:01:30 lr 0.000588	time 0.8775 (0.8888)	loss 0.6136 (0.4803)	grad_norm 3.0197 (2.7822)	mem 20675MB
[2025-04-02 19:46:28 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][134/234]	eta 0:01:28 lr 0.000588	time 0.8777 (0.8886)	loss 0.5246 (0.4805)	grad_norm 2.2680 (2.7765)	mem 20675MB
[2025-04-02 19:46:29 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][136/234]	eta 0:01:27 lr 0.000587	time 0.8771 (0.8884)	loss 0.4711 (0.4804)	grad_norm 2.9207 (2.7771)	mem 20675MB
[2025-04-02 19:46:31 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][138/234]	eta 0:01:25 lr 0.000587	time 0.8777 (0.8883)	loss 0.4189 (0.4804)	grad_norm 2.5913 (2.7739)	mem 20675MB
[2025-04-02 19:46:33 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][140/234]	eta 0:01:23 lr 0.000586	time 0.8789 (0.8882)	loss 0.5189 (0.4806)	grad_norm 2.9802 (2.7774)	mem 20675MB
[2025-04-02 19:46:35 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][142/234]	eta 0:01:21 lr 0.000585	time 0.8790 (0.8881)	loss 0.4828 (0.4805)	grad_norm 1.8467 (2.7668)	mem 20675MB
[2025-04-02 19:46:36 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][144/234]	eta 0:01:19 lr 0.000585	time 0.8786 (0.8880)	loss 0.4603 (0.4796)	grad_norm 3.3544 (2.7731)	mem 20675MB
[2025-04-02 19:46:38 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][146/234]	eta 0:01:18 lr 0.000584	time 0.8780 (0.8879)	loss 0.4410 (0.4796)	grad_norm 3.0486 (2.7661)	mem 20675MB
[2025-04-02 19:46:40 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][148/234]	eta 0:01:16 lr 0.000584	time 0.8782 (0.8878)	loss 0.3402 (0.4788)	grad_norm 2.6580 (2.7634)	mem 20675MB
[2025-04-02 19:46:42 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][150/234]	eta 0:01:14 lr 0.000583	time 0.8775 (0.8877)	loss 0.5035 (0.4790)	grad_norm 2.5411 (2.7616)	mem 20675MB
[2025-04-02 19:46:43 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][152/234]	eta 0:01:12 lr 0.000583	time 0.8785 (0.8876)	loss 0.4564 (0.4792)	grad_norm 2.3484 (2.7598)	mem 20675MB
[2025-04-02 19:46:45 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][154/234]	eta 0:01:10 lr 0.000582	time 0.8780 (0.8875)	loss 0.5203 (0.4792)	grad_norm 2.5032 (2.7636)	mem 20675MB
[2025-04-02 19:46:47 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][156/234]	eta 0:01:09 lr 0.000582	time 0.8800 (0.8874)	loss 0.5024 (0.4794)	grad_norm 4.8455 (2.7809)	mem 20675MB
[2025-04-02 19:46:49 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][158/234]	eta 0:01:07 lr 0.000581	time 0.8892 (0.8874)	loss 0.5912 (0.4790)	grad_norm 2.8787 (2.7877)	mem 20675MB
[2025-04-02 19:46:51 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][160/234]	eta 0:01:05 lr 0.000580	time 0.8996 (0.8874)	loss 0.5089 (0.4789)	grad_norm 2.9327 (2.7856)	mem 20675MB
[2025-04-02 19:46:52 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][162/234]	eta 0:01:03 lr 0.000580	time 0.8870 (0.8874)	loss 0.4248 (0.4787)	grad_norm 3.5643 (2.7891)	mem 20675MB
[2025-04-02 19:46:54 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][164/234]	eta 0:01:02 lr 0.000579	time 0.8785 (0.8873)	loss 0.4722 (0.4789)	grad_norm 1.9709 (2.7844)	mem 20675MB
[2025-04-02 19:46:56 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][166/234]	eta 0:01:00 lr 0.000579	time 0.8786 (0.8872)	loss 0.3112 (0.4778)	grad_norm 4.2774 (2.7925)	mem 20675MB
[2025-04-02 19:46:58 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][168/234]	eta 0:00:58 lr 0.000578	time 0.8781 (0.8871)	loss 0.4440 (0.4777)	grad_norm 3.7303 (2.8160)	mem 20675MB
[2025-04-02 19:46:59 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][170/234]	eta 0:00:56 lr 0.000578	time 0.8837 (0.8871)	loss 0.4884 (0.4779)	grad_norm 3.3461 (2.8236)	mem 20675MB
[2025-04-02 19:47:01 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][172/234]	eta 0:00:54 lr 0.000577	time 0.8784 (0.8870)	loss 0.5283 (0.4788)	grad_norm 3.8365 (2.8405)	mem 20675MB
[2025-04-02 19:47:03 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][174/234]	eta 0:00:53 lr 0.000577	time 0.8871 (0.8870)	loss 0.5116 (0.4782)	grad_norm 2.8796 (2.8370)	mem 20675MB
[2025-04-02 19:47:05 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][176/234]	eta 0:00:51 lr 0.000576	time 0.8793 (0.8869)	loss 0.4665 (0.4780)	grad_norm 1.7947 (2.8354)	mem 20675MB
[2025-04-02 19:47:06 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][178/234]	eta 0:00:49 lr 0.000575	time 0.8785 (0.8869)	loss 0.5146 (0.4785)	grad_norm 2.1871 (2.8336)	mem 20675MB
[2025-04-02 19:47:08 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][180/234]	eta 0:00:47 lr 0.000575	time 0.8779 (0.8868)	loss 0.4051 (0.4787)	grad_norm 2.1863 (2.8265)	mem 20675MB
[2025-04-02 19:47:10 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][182/234]	eta 0:00:46 lr 0.000574	time 0.8819 (0.8867)	loss 0.3767 (0.4785)	grad_norm 2.0797 (2.8217)	mem 20675MB
[2025-04-02 19:47:12 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][184/234]	eta 0:00:44 lr 0.000574	time 0.8766 (0.8867)	loss 0.5951 (0.4790)	grad_norm 2.8632 (2.8189)	mem 20675MB
[2025-04-02 19:47:13 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][186/234]	eta 0:00:42 lr 0.000573	time 0.8771 (0.8866)	loss 0.3286 (0.4775)	grad_norm 3.9328 (2.8273)	mem 20675MB
[2025-04-02 19:47:15 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][188/234]	eta 0:00:40 lr 0.000573	time 0.8833 (0.8865)	loss 0.5664 (0.4781)	grad_norm 2.4734 (2.8236)	mem 20675MB
[2025-04-02 19:47:17 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][190/234]	eta 0:00:39 lr 0.000572	time 0.8797 (0.8865)	loss 0.5018 (0.4782)	grad_norm 3.0110 (2.8211)	mem 20675MB
[2025-04-02 19:47:19 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][192/234]	eta 0:00:37 lr 0.000571	time 0.8860 (0.8864)	loss 0.4511 (0.4780)	grad_norm 2.7093 (2.8206)	mem 20675MB
[2025-04-02 19:47:21 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][194/234]	eta 0:00:35 lr 0.000571	time 0.8777 (0.8864)	loss 0.4123 (0.4779)	grad_norm 4.1251 (2.8318)	mem 20675MB
[2025-04-02 19:47:22 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][196/234]	eta 0:00:33 lr 0.000570	time 0.8786 (0.8863)	loss 0.4540 (0.4772)	grad_norm 2.4209 (2.8328)	mem 20675MB
[2025-04-02 19:47:24 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][198/234]	eta 0:00:31 lr 0.000570	time 0.8806 (0.8863)	loss 0.6915 (0.4788)	grad_norm 3.7149 (2.8391)	mem 20675MB
[2025-04-02 19:47:26 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][200/234]	eta 0:00:30 lr 0.000569	time 0.8774 (0.8862)	loss 0.4867 (0.4784)	grad_norm 4.1278 (2.8434)	mem 20675MB
[2025-04-02 19:47:28 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][202/234]	eta 0:00:28 lr 0.000569	time 0.8776 (0.8861)	loss 0.5833 (0.4782)	grad_norm 2.4720 (2.8476)	mem 20675MB
[2025-04-02 19:47:29 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][204/234]	eta 0:00:26 lr 0.000568	time 0.8777 (0.8860)	loss 0.4977 (0.4785)	grad_norm 3.1365 (2.8434)	mem 20675MB
[2025-04-02 19:47:31 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][206/234]	eta 0:00:24 lr 0.000568	time 0.8770 (0.8860)	loss 0.5843 (0.4795)	grad_norm 3.0083 (2.8415)	mem 20675MB
[2025-04-02 19:47:33 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][208/234]	eta 0:00:23 lr 0.000567	time 0.8782 (0.8859)	loss 0.4957 (0.4798)	grad_norm 2.6504 (2.8402)	mem 20675MB
[2025-04-02 19:47:35 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][210/234]	eta 0:00:21 lr 0.000566	time 0.8785 (0.8858)	loss 0.4348 (0.4794)	grad_norm 2.7103 (2.8412)	mem 20675MB
[2025-04-02 19:47:36 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][212/234]	eta 0:00:19 lr 0.000566	time 0.8824 (0.8858)	loss 0.5550 (0.4799)	grad_norm 2.4519 (2.8355)	mem 20675MB
[2025-04-02 19:47:38 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][214/234]	eta 0:00:17 lr 0.000565	time 0.8778 (0.8857)	loss 0.4601 (0.4794)	grad_norm 1.7350 (2.8305)	mem 20675MB
[2025-04-02 19:47:40 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][216/234]	eta 0:00:15 lr 0.000565	time 0.8780 (0.8857)	loss 0.5417 (0.4797)	grad_norm 2.4993 (2.8284)	mem 20675MB
[2025-04-02 19:47:42 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][218/234]	eta 0:00:14 lr 0.000564	time 0.8799 (0.8856)	loss 0.5057 (0.4800)	grad_norm 2.2025 (2.8277)	mem 20675MB
[2025-04-02 19:47:43 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][220/234]	eta 0:00:12 lr 0.000564	time 0.8785 (0.8856)	loss 0.4120 (0.4795)	grad_norm 3.4667 (2.8270)	mem 20675MB
[2025-04-02 19:47:45 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][222/234]	eta 0:00:10 lr 0.000563	time 0.8783 (0.8855)	loss 0.4786 (0.4802)	grad_norm 4.5742 (2.8392)	mem 20675MB
[2025-04-02 19:47:47 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][224/234]	eta 0:00:08 lr 0.000563	time 0.8782 (0.8854)	loss 0.3259 (0.4796)	grad_norm 4.6212 (2.8447)	mem 20675MB
[2025-04-02 19:47:49 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][226/234]	eta 0:00:07 lr 0.000562	time 0.8779 (0.8854)	loss 0.5239 (0.4796)	grad_norm 1.8700 (2.8433)	mem 20675MB
[2025-04-02 19:47:50 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][228/234]	eta 0:00:05 lr 0.000561	time 0.8788 (0.8853)	loss 0.4863 (0.4797)	grad_norm 2.5314 (2.8413)	mem 20675MB
[2025-04-02 19:47:52 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][230/234]	eta 0:00:03 lr 0.000561	time 0.8773 (0.8853)	loss 0.5283 (0.4800)	grad_norm 2.6170 (2.8383)	mem 20675MB
[2025-04-02 19:47:54 simmim_finetune] (main_finetune.py 252): INFO Train: [15/30][232/234]	eta 0:00:01 lr 0.000560	time 0.8767 (0.8852)	loss 0.5345 (0.4806)	grad_norm 1.9101 (2.8347)	mem 20675MB
[2025-04-02 19:47:55 simmim_finetune] (main_finetune.py 260): INFO EPOCH 15 training takes 0:03:27
[2025-04-02 19:47:55 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt15.pth saving......
[2025-04-02 19:47:58 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt15.pth saved !!!
[2025-04-02 19:47:59 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.097 (1.097)	Loss 0.6691 (0.6691)	Acc@1 64.062 (64.062)	Mem 20675MB
[2025-04-02 19:47:59 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.271
[2025-04-02 19:47:59 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.3%
[2025-04-02 19:47:59 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 71.27%
[2025-04-02 19:47:59 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.2082530419599224e-06, 2.2082530419599224e-06, 3.3229990675498606e-06, 3.3229990675498606e-06, 5.037992953072842e-06, 5.037992953072842e-06, 7.67644508464666e-06, 7.67644508464666e-06, 1.1735602210144841e-05, 1.1735602210144841e-05, 1.798045932629589e-05, 1.798045932629589e-05, 2.7587931812682114e-05, 2.7587931812682114e-05, 4.236865871481476e-05, 4.236865871481476e-05, 6.510823856424963e-05, 6.510823856424963e-05, 0.00010009220756338018, 0.00010009220756338018, 0.0001539136983312733, 0.0001539136983312733, 0.00023671599182033967, 0.00023671599182033967, 0.00036410413564967256, 0.00036410413564967256, 0.0005600858953871078, 0.0005600858953871078]
[2025-04-02 19:48:01 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][0/234]	eta 0:08:23 lr 0.000560	time 2.1508 (2.1508)	loss 0.4311 (0.4311)	grad_norm 5.0343 (5.0343)	mem 20675MB
[2025-04-02 19:48:03 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][2/234]	eta 0:05:02 lr 0.000559	time 0.8763 (1.3020)	loss 0.4707 (0.4194)	grad_norm 2.0946 (3.1503)	mem 20675MB
[2025-04-02 19:48:05 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][4/234]	eta 0:04:20 lr 0.000559	time 0.8769 (1.1321)	loss 0.3164 (0.4139)	grad_norm 2.5110 (2.7160)	mem 20675MB
[2025-04-02 19:48:07 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][6/234]	eta 0:04:01 lr 0.000558	time 0.8770 (1.0594)	loss 0.3228 (0.4038)	grad_norm 2.5069 (2.6841)	mem 20675MB
[2025-04-02 19:48:09 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][8/234]	eta 0:03:50 lr 0.000558	time 0.8770 (1.0190)	loss 0.4804 (0.4164)	grad_norm 3.1970 (2.7528)	mem 20675MB
[2025-04-02 19:48:10 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][10/234]	eta 0:03:42 lr 0.000557	time 0.8777 (0.9934)	loss 0.4762 (0.4254)	grad_norm 3.0905 (2.7944)	mem 20675MB
[2025-04-02 19:48:12 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][12/234]	eta 0:03:36 lr 0.000556	time 0.8773 (0.9757)	loss 0.4828 (0.4368)	grad_norm 3.5855 (2.8471)	mem 20675MB
[2025-04-02 19:48:14 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][14/234]	eta 0:03:31 lr 0.000556	time 0.8771 (0.9626)	loss 0.5858 (0.4438)	grad_norm 2.0106 (2.8936)	mem 20675MB
[2025-04-02 19:48:16 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][16/234]	eta 0:03:27 lr 0.000555	time 0.8781 (0.9528)	loss 0.5870 (0.4562)	grad_norm 3.2193 (2.8887)	mem 20675MB
[2025-04-02 19:48:17 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][18/234]	eta 0:03:24 lr 0.000555	time 0.8785 (0.9450)	loss 0.5201 (0.4637)	grad_norm 2.4851 (2.8320)	mem 20675MB
[2025-04-02 19:48:19 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][20/234]	eta 0:03:20 lr 0.000554	time 0.8787 (0.9388)	loss 0.3670 (0.4629)	grad_norm 2.9314 (2.8842)	mem 20675MB
[2025-04-02 19:48:21 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][22/234]	eta 0:03:17 lr 0.000554	time 0.8785 (0.9336)	loss 0.5265 (0.4673)	grad_norm 2.9205 (2.8260)	mem 20675MB
[2025-04-02 19:48:23 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][24/234]	eta 0:03:15 lr 0.000553	time 0.8796 (0.9294)	loss 0.4257 (0.4692)	grad_norm 3.2269 (2.8256)	mem 20675MB
[2025-04-02 19:48:24 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][26/234]	eta 0:03:12 lr 0.000553	time 0.8783 (0.9256)	loss 0.5095 (0.4746)	grad_norm 1.6787 (2.7621)	mem 20675MB
[2025-04-02 19:48:26 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][28/234]	eta 0:03:10 lr 0.000552	time 0.8802 (0.9225)	loss 0.3311 (0.4711)	grad_norm 3.9820 (2.7836)	mem 20675MB
[2025-04-02 19:48:28 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][30/234]	eta 0:03:07 lr 0.000551	time 0.8793 (0.9198)	loss 0.5311 (0.4719)	grad_norm 2.4428 (2.7961)	mem 20675MB
[2025-04-02 19:48:30 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][32/234]	eta 0:03:05 lr 0.000551	time 0.8778 (0.9173)	loss 0.4978 (0.4737)	grad_norm 1.9850 (2.7457)	mem 20675MB
[2025-04-02 19:48:31 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][34/234]	eta 0:03:03 lr 0.000550	time 0.8790 (0.9151)	loss 0.5302 (0.4739)	grad_norm 4.1604 (2.8143)	mem 20675MB
[2025-04-02 19:48:33 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][36/234]	eta 0:03:00 lr 0.000550	time 0.8786 (0.9132)	loss 0.4286 (0.4696)	grad_norm 3.0220 (2.8359)	mem 20675MB
[2025-04-02 19:48:35 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][38/234]	eta 0:02:58 lr 0.000549	time 0.8779 (0.9114)	loss 0.5140 (0.4723)	grad_norm 2.5478 (2.8501)	mem 20675MB
[2025-04-02 19:48:37 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][40/234]	eta 0:02:56 lr 0.000549	time 0.8792 (0.9099)	loss 0.5074 (0.4744)	grad_norm 1.9919 (2.8168)	mem 20675MB
[2025-04-02 19:48:38 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][42/234]	eta 0:02:54 lr 0.000548	time 0.8782 (0.9089)	loss 0.4639 (0.4753)	grad_norm 3.0536 (2.8005)	mem 20675MB
[2025-04-02 19:48:40 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][44/234]	eta 0:02:52 lr 0.000548	time 0.8780 (0.9076)	loss 0.5678 (0.4768)	grad_norm 3.0914 (2.7857)	mem 20675MB
[2025-04-02 19:48:42 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][46/234]	eta 0:02:50 lr 0.000547	time 0.8792 (0.9064)	loss 0.3655 (0.4749)	grad_norm 2.6200 (2.7707)	mem 20675MB
[2025-04-02 19:48:44 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][48/234]	eta 0:02:48 lr 0.000546	time 0.8783 (0.9054)	loss 0.4276 (0.4739)	grad_norm 3.3644 (2.7905)	mem 20675MB
[2025-04-02 19:48:45 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][50/234]	eta 0:02:46 lr 0.000546	time 0.8801 (0.9044)	loss 0.4728 (0.4733)	grad_norm 3.5220 (2.8093)	mem 20675MB
[2025-04-02 19:48:47 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][52/234]	eta 0:02:44 lr 0.000545	time 0.8799 (0.9036)	loss 0.4920 (0.4742)	grad_norm 2.4211 (2.8075)	mem 20675MB
[2025-04-02 19:48:49 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][54/234]	eta 0:02:42 lr 0.000545	time 0.9133 (0.9043)	loss 0.5357 (0.4749)	grad_norm 1.6240 (2.7675)	mem 20675MB
[2025-04-02 19:48:51 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][56/234]	eta 0:02:40 lr 0.000544	time 0.8838 (0.9038)	loss 0.4354 (0.4729)	grad_norm 2.9325 (2.7808)	mem 20675MB
[2025-04-02 19:48:53 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][58/234]	eta 0:02:38 lr 0.000544	time 0.8808 (0.9030)	loss 0.5819 (0.4747)	grad_norm 3.1337 (2.7877)	mem 20675MB
[2025-04-02 19:48:54 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][60/234]	eta 0:02:36 lr 0.000543	time 0.8799 (0.9023)	loss 0.3736 (0.4725)	grad_norm 3.4630 (2.8045)	mem 20675MB
[2025-04-02 19:48:56 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][62/234]	eta 0:02:35 lr 0.000543	time 0.8784 (0.9015)	loss 0.5104 (0.4735)	grad_norm 2.3745 (2.7855)	mem 20675MB
[2025-04-02 19:48:58 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][64/234]	eta 0:02:33 lr 0.000542	time 0.8782 (0.9009)	loss 0.4968 (0.4740)	grad_norm 2.3593 (2.7902)	mem 20675MB
[2025-04-02 19:49:00 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][66/234]	eta 0:02:31 lr 0.000541	time 0.8789 (0.9002)	loss 0.5588 (0.4747)	grad_norm 3.3413 (2.7854)	mem 20675MB
[2025-04-02 19:49:01 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][68/234]	eta 0:02:29 lr 0.000541	time 0.8779 (0.8997)	loss 0.3365 (0.4716)	grad_norm 5.2298 (2.8229)	mem 20675MB
[2025-04-02 19:49:03 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][70/234]	eta 0:02:27 lr 0.000540	time 0.8778 (0.8991)	loss 0.3668 (0.4686)	grad_norm 4.5006 (2.8520)	mem 20675MB
[2025-04-02 19:49:05 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][72/234]	eta 0:02:25 lr 0.000540	time 0.8774 (0.8985)	loss 0.6034 (0.4707)	grad_norm 3.6889 (2.8560)	mem 20675MB
[2025-04-02 19:49:07 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][74/234]	eta 0:02:23 lr 0.000539	time 0.8787 (0.8980)	loss 0.5426 (0.4716)	grad_norm 3.1533 (2.8473)	mem 20675MB
[2025-04-02 19:49:08 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][76/234]	eta 0:02:21 lr 0.000539	time 0.8780 (0.8975)	loss 0.5459 (0.4726)	grad_norm 2.7371 (2.8460)	mem 20675MB
[2025-04-02 19:49:10 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][78/234]	eta 0:02:19 lr 0.000538	time 0.8787 (0.8971)	loss 0.4545 (0.4716)	grad_norm 2.6944 (2.8535)	mem 20675MB
[2025-04-02 19:49:12 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][80/234]	eta 0:02:18 lr 0.000538	time 0.8795 (0.8967)	loss 0.5243 (0.4733)	grad_norm 2.6683 (2.8475)	mem 20675MB
[2025-04-02 19:49:14 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][82/234]	eta 0:02:16 lr 0.000537	time 0.8782 (0.8963)	loss 0.5240 (0.4740)	grad_norm 2.2231 (2.8429)	mem 20675MB
[2025-04-02 19:49:15 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][84/234]	eta 0:02:14 lr 0.000536	time 0.8783 (0.8959)	loss 0.4719 (0.4747)	grad_norm 2.8988 (2.8415)	mem 20675MB
[2025-04-02 19:49:17 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][86/234]	eta 0:02:12 lr 0.000536	time 0.8845 (0.8955)	loss 0.3502 (0.4746)	grad_norm 3.7336 (2.8557)	mem 20675MB
[2025-04-02 19:49:19 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][88/234]	eta 0:02:10 lr 0.000535	time 0.8837 (0.8952)	loss 0.5171 (0.4754)	grad_norm 1.7066 (2.8782)	mem 20675MB
[2025-04-02 19:49:21 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][90/234]	eta 0:02:08 lr 0.000535	time 0.8828 (0.8950)	loss 0.4959 (0.4762)	grad_norm 2.0868 (2.8635)	mem 20675MB
[2025-04-02 19:49:23 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][92/234]	eta 0:02:07 lr 0.000534	time 0.8783 (0.8947)	loss 0.5005 (0.4750)	grad_norm 2.0332 (2.8624)	mem 20675MB
[2025-04-02 19:49:24 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][94/234]	eta 0:02:05 lr 0.000534	time 0.8782 (0.8945)	loss 0.5132 (0.4740)	grad_norm 2.6000 (2.8618)	mem 20675MB
[2025-04-02 19:49:26 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][96/234]	eta 0:02:03 lr 0.000533	time 0.8798 (0.8942)	loss 0.4298 (0.4735)	grad_norm 2.8114 (2.8590)	mem 20675MB
[2025-04-02 19:49:28 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][98/234]	eta 0:02:01 lr 0.000533	time 0.8768 (0.8939)	loss 0.6011 (0.4739)	grad_norm 2.8063 (2.8589)	mem 20675MB
[2025-04-02 19:49:30 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][100/234]	eta 0:01:59 lr 0.000532	time 0.8781 (0.8936)	loss 0.5537 (0.4748)	grad_norm 2.1662 (2.8473)	mem 20675MB
[2025-04-02 19:49:31 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][102/234]	eta 0:01:57 lr 0.000532	time 0.8806 (0.8934)	loss 0.5459 (0.4746)	grad_norm 2.9320 (2.8487)	mem 20675MB
[2025-04-02 19:49:33 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][104/234]	eta 0:01:56 lr 0.000531	time 0.8795 (0.8932)	loss 0.4901 (0.4753)	grad_norm 1.9071 (2.8321)	mem 20675MB
[2025-04-02 19:49:35 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][106/234]	eta 0:01:54 lr 0.000530	time 0.8808 (0.8930)	loss 0.4800 (0.4765)	grad_norm 3.0872 (2.8292)	mem 20675MB
[2025-04-02 19:49:37 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][108/234]	eta 0:01:52 lr 0.000530	time 0.8788 (0.8928)	loss 0.6110 (0.4780)	grad_norm 3.9756 (2.8348)	mem 20675MB
[2025-04-02 19:49:38 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][110/234]	eta 0:01:50 lr 0.000529	time 0.8780 (0.8925)	loss 0.3148 (0.4762)	grad_norm 3.2176 (2.8495)	mem 20675MB
[2025-04-02 19:49:40 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][112/234]	eta 0:01:48 lr 0.000529	time 0.8801 (0.8923)	loss 0.3473 (0.4750)	grad_norm 5.8701 (2.8732)	mem 20675MB
[2025-04-02 19:49:42 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][114/234]	eta 0:01:47 lr 0.000528	time 0.8790 (0.8921)	loss 0.5569 (0.4757)	grad_norm 2.6125 (2.8758)	mem 20675MB
[2025-04-02 19:49:44 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][116/234]	eta 0:01:45 lr 0.000528	time 0.8940 (0.8920)	loss 0.3634 (0.4739)	grad_norm 3.6439 (2.8794)	mem 20675MB
[2025-04-02 19:49:45 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][118/234]	eta 0:01:43 lr 0.000527	time 0.8786 (0.8920)	loss 0.5510 (0.4746)	grad_norm 2.4037 (2.8782)	mem 20675MB
[2025-04-02 19:49:47 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][120/234]	eta 0:01:41 lr 0.000527	time 0.8794 (0.8918)	loss 0.4198 (0.4744)	grad_norm 3.0682 (2.8765)	mem 20675MB
[2025-04-02 19:49:49 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][122/234]	eta 0:01:39 lr 0.000526	time 0.8788 (0.8916)	loss 0.4479 (0.4733)	grad_norm 1.8878 (2.8686)	mem 20675MB
[2025-04-02 19:49:51 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][124/234]	eta 0:01:38 lr 0.000525	time 0.8859 (0.8915)	loss 0.4512 (0.4731)	grad_norm 1.9532 (2.8691)	mem 20675MB
[2025-04-02 19:49:53 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][126/234]	eta 0:01:36 lr 0.000525	time 0.8780 (0.8913)	loss 0.5014 (0.4735)	grad_norm 2.1134 (2.8581)	mem 20675MB
[2025-04-02 19:49:54 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][128/234]	eta 0:01:34 lr 0.000524	time 0.8784 (0.8911)	loss 0.3058 (0.4720)	grad_norm 2.9508 (2.8573)	mem 20675MB
[2025-04-02 19:49:56 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][130/234]	eta 0:01:32 lr 0.000524	time 0.8800 (0.8910)	loss 0.5122 (0.4724)	grad_norm 2.4363 (2.8573)	mem 20675MB
[2025-04-02 19:49:58 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][132/234]	eta 0:01:30 lr 0.000523	time 0.8786 (0.8908)	loss 0.3086 (0.4712)	grad_norm 2.9751 (2.8572)	mem 20675MB
[2025-04-02 19:50:00 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][134/234]	eta 0:01:29 lr 0.000523	time 0.8797 (0.8907)	loss 0.4601 (0.4701)	grad_norm 2.4290 (2.8779)	mem 20675MB
[2025-04-02 19:50:01 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][136/234]	eta 0:01:27 lr 0.000522	time 0.8792 (0.8905)	loss 0.5137 (0.4700)	grad_norm 3.2125 (2.8839)	mem 20675MB
[2025-04-02 19:50:03 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][138/234]	eta 0:01:25 lr 0.000522	time 0.8798 (0.8904)	loss 0.3783 (0.4696)	grad_norm 4.4395 (2.8912)	mem 20675MB
[2025-04-02 19:50:05 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][140/234]	eta 0:01:23 lr 0.000521	time 0.8796 (0.8903)	loss 0.4869 (0.4705)	grad_norm 2.1959 (2.8892)	mem 20675MB
[2025-04-02 19:50:07 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][142/234]	eta 0:01:21 lr 0.000520	time 0.8818 (0.8902)	loss 0.4734 (0.4706)	grad_norm 2.9816 (2.8903)	mem 20675MB
[2025-04-02 19:50:08 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][144/234]	eta 0:01:20 lr 0.000520	time 0.8841 (0.8901)	loss 0.4777 (0.4699)	grad_norm 1.9796 (2.8813)	mem 20675MB
[2025-04-02 19:50:10 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][146/234]	eta 0:01:18 lr 0.000519	time 0.8787 (0.8900)	loss 0.5194 (0.4701)	grad_norm 3.0294 (2.8841)	mem 20675MB
[2025-04-02 19:50:12 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][148/234]	eta 0:01:16 lr 0.000519	time 0.8833 (0.8899)	loss 0.5210 (0.4698)	grad_norm 1.4678 (2.8712)	mem 20675MB
[2025-04-02 19:50:14 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][150/234]	eta 0:01:14 lr 0.000518	time 0.8781 (0.8898)	loss 0.5329 (0.4704)	grad_norm 2.0292 (2.8601)	mem 20675MB
[2025-04-02 19:50:15 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][152/234]	eta 0:01:12 lr 0.000518	time 0.8783 (0.8896)	loss 0.5256 (0.4706)	grad_norm 1.7810 (2.8538)	mem 20675MB
[2025-04-02 19:50:17 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][154/234]	eta 0:01:11 lr 0.000517	time 0.8823 (0.8896)	loss 0.3652 (0.4698)	grad_norm 3.0006 (2.8527)	mem 20675MB
[2025-04-02 19:50:19 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][156/234]	eta 0:01:09 lr 0.000517	time 0.8833 (0.8896)	loss 0.5015 (0.4693)	grad_norm 1.5915 (2.8490)	mem 20675MB
[2025-04-02 19:50:21 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][158/234]	eta 0:01:07 lr 0.000516	time 0.8781 (0.8895)	loss 0.5406 (0.4690)	grad_norm 2.4524 (2.8539)	mem 20675MB
[2025-04-02 19:50:23 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][160/234]	eta 0:01:05 lr 0.000516	time 0.8782 (0.8894)	loss 0.4425 (0.4688)	grad_norm 2.3718 (2.8478)	mem 20675MB
[2025-04-02 19:50:24 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][162/234]	eta 0:01:04 lr 0.000515	time 0.8780 (0.8892)	loss 0.4181 (0.4685)	grad_norm 2.4903 (2.8487)	mem 20675MB
[2025-04-02 19:50:26 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][164/234]	eta 0:01:02 lr 0.000514	time 0.8782 (0.8891)	loss 0.4155 (0.4682)	grad_norm 2.5304 (2.8478)	mem 20675MB
[2025-04-02 19:50:28 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][166/234]	eta 0:01:00 lr 0.000514	time 0.8782 (0.8890)	loss 0.5553 (0.4685)	grad_norm 2.9219 (2.8466)	mem 20675MB
[2025-04-02 19:50:30 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][168/234]	eta 0:00:58 lr 0.000513	time 0.8779 (0.8889)	loss 0.5448 (0.4682)	grad_norm 4.6929 (2.8590)	mem 20675MB
[2025-04-02 19:50:31 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][170/234]	eta 0:00:56 lr 0.000513	time 0.8776 (0.8887)	loss 0.4936 (0.4681)	grad_norm 1.9994 (2.8518)	mem 20675MB
[2025-04-02 19:50:33 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][172/234]	eta 0:00:55 lr 0.000512	time 0.8764 (0.8886)	loss 0.4998 (0.4690)	grad_norm 2.6723 (2.8521)	mem 20675MB
[2025-04-02 19:50:35 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][174/234]	eta 0:00:53 lr 0.000512	time 0.8782 (0.8885)	loss 0.3569 (0.4688)	grad_norm 2.2664 (2.8529)	mem 20675MB
[2025-04-02 19:50:37 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][176/234]	eta 0:00:51 lr 0.000511	time 0.8771 (0.8884)	loss 0.5383 (0.4688)	grad_norm 2.3848 (2.8596)	mem 20675MB
[2025-04-02 19:50:38 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][178/234]	eta 0:00:49 lr 0.000511	time 0.8766 (0.8883)	loss 0.5198 (0.4687)	grad_norm 2.8340 (2.8560)	mem 20675MB
[2025-04-02 19:50:40 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][180/234]	eta 0:00:47 lr 0.000510	time 0.8785 (0.8881)	loss 0.4641 (0.4688)	grad_norm 3.0423 (2.8539)	mem 20675MB
[2025-04-02 19:50:42 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][182/234]	eta 0:00:46 lr 0.000509	time 0.8834 (0.8881)	loss 0.5173 (0.4683)	grad_norm 2.4814 (2.8611)	mem 20675MB
[2025-04-02 19:50:44 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][184/234]	eta 0:00:44 lr 0.000509	time 0.8774 (0.8880)	loss 0.5568 (0.4688)	grad_norm 2.6831 (2.8563)	mem 20675MB
[2025-04-02 19:50:45 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][186/234]	eta 0:00:42 lr 0.000508	time 0.8773 (0.8879)	loss 0.4039 (0.4679)	grad_norm 2.9811 (2.8650)	mem 20675MB
[2025-04-02 19:50:47 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][188/234]	eta 0:00:40 lr 0.000508	time 0.8774 (0.8878)	loss 0.5065 (0.4683)	grad_norm 1.8331 (2.8567)	mem 20675MB
[2025-04-02 19:50:49 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][190/234]	eta 0:00:39 lr 0.000507	time 0.8791 (0.8877)	loss 0.4002 (0.4685)	grad_norm 2.1036 (2.8521)	mem 20675MB
[2025-04-02 19:50:51 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][192/234]	eta 0:00:37 lr 0.000507	time 0.8813 (0.8876)	loss 0.3382 (0.4674)	grad_norm 3.3528 (2.8520)	mem 20675MB
[2025-04-02 19:50:52 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][194/234]	eta 0:00:35 lr 0.000506	time 0.8865 (0.8876)	loss 0.3776 (0.4670)	grad_norm 2.3721 (2.8470)	mem 20675MB
[2025-04-02 19:50:54 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][196/234]	eta 0:00:33 lr 0.000506	time 0.8781 (0.8875)	loss 0.4803 (0.4667)	grad_norm 2.2953 (2.8518)	mem 20675MB
[2025-04-02 19:50:56 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][198/234]	eta 0:00:31 lr 0.000505	time 0.8774 (0.8874)	loss 0.4990 (0.4666)	grad_norm 1.9374 (2.8472)	mem 20675MB
[2025-04-02 19:50:58 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][200/234]	eta 0:00:30 lr 0.000505	time 0.8777 (0.8873)	loss 0.4853 (0.4670)	grad_norm 1.9597 (2.8441)	mem 20675MB
[2025-04-02 19:50:59 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][202/234]	eta 0:00:28 lr 0.000504	time 0.8773 (0.8872)	loss 0.3339 (0.4667)	grad_norm 6.8194 (2.8666)	mem 20675MB
[2025-04-02 19:51:01 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][204/234]	eta 0:00:26 lr 0.000503	time 0.8801 (0.8872)	loss 0.4647 (0.4661)	grad_norm 2.7235 (2.8685)	mem 20675MB
[2025-04-02 19:51:03 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][206/234]	eta 0:00:24 lr 0.000503	time 0.8777 (0.8872)	loss 0.4604 (0.4661)	grad_norm 2.4402 (2.8644)	mem 20675MB
[2025-04-02 19:51:05 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][208/234]	eta 0:00:23 lr 0.000502	time 0.8776 (0.8871)	loss 0.5637 (0.4661)	grad_norm 3.3771 (2.8716)	mem 20675MB
[2025-04-02 19:51:06 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][210/234]	eta 0:00:21 lr 0.000502	time 0.8780 (0.8870)	loss 0.4636 (0.4664)	grad_norm 2.6490 (2.8801)	mem 20675MB
[2025-04-02 19:51:08 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][212/234]	eta 0:00:19 lr 0.000501	time 0.8784 (0.8870)	loss 0.6349 (0.4676)	grad_norm 3.6110 (2.8812)	mem 20675MB
[2025-04-02 19:51:10 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][214/234]	eta 0:00:17 lr 0.000501	time 0.8769 (0.8869)	loss 0.4693 (0.4675)	grad_norm 4.6077 (2.8893)	mem 20675MB
[2025-04-02 19:51:12 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][216/234]	eta 0:00:15 lr 0.000500	time 0.8771 (0.8868)	loss 0.4757 (0.4673)	grad_norm 2.0742 (2.8845)	mem 20675MB
[2025-04-02 19:51:14 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][218/234]	eta 0:00:14 lr 0.000500	time 0.8770 (0.8867)	loss 0.4864 (0.4676)	grad_norm 2.5170 (2.8824)	mem 20675MB
[2025-04-02 19:51:15 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][220/234]	eta 0:00:12 lr 0.000499	time 0.8763 (0.8866)	loss 0.5575 (0.4682)	grad_norm 2.5568 (2.8753)	mem 20675MB
[2025-04-02 19:51:17 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][222/234]	eta 0:00:10 lr 0.000498	time 0.8764 (0.8866)	loss 0.4624 (0.4684)	grad_norm 2.9611 (2.8733)	mem 20675MB
[2025-04-02 19:51:19 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][224/234]	eta 0:00:08 lr 0.000498	time 0.8764 (0.8865)	loss 0.5668 (0.4689)	grad_norm 2.3176 (2.8665)	mem 20675MB
[2025-04-02 19:51:21 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][226/234]	eta 0:00:07 lr 0.000497	time 0.8770 (0.8864)	loss 0.5795 (0.4697)	grad_norm 2.6024 (2.8622)	mem 20675MB
[2025-04-02 19:51:22 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][228/234]	eta 0:00:05 lr 0.000497	time 0.8768 (0.8863)	loss 0.5419 (0.4699)	grad_norm 1.8682 (2.8553)	mem 20675MB
[2025-04-02 19:51:24 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][230/234]	eta 0:00:03 lr 0.000496	time 0.8765 (0.8862)	loss 0.4667 (0.4696)	grad_norm 2.4150 (2.8544)	mem 20675MB
[2025-04-02 19:51:26 simmim_finetune] (main_finetune.py 252): INFO Train: [16/30][232/234]	eta 0:00:01 lr 0.000496	time 0.8774 (0.8862)	loss 0.5649 (0.4701)	grad_norm 2.5330 (2.8560)	mem 20675MB
[2025-04-02 19:51:27 simmim_finetune] (main_finetune.py 260): INFO EPOCH 16 training takes 0:03:27
[2025-04-02 19:51:28 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.080 (1.080)	Loss 0.7589 (0.7589)	Acc@1 61.719 (61.719)	Mem 20675MB
[2025-04-02 19:51:28 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 70.166
[2025-04-02 19:51:28 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 70.2%
[2025-04-02 19:51:28 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 71.27%
[2025-04-02 19:51:28 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [1.9822667774950066e-06, 1.9822667774950066e-06, 2.9683689124577926e-06, 2.9683689124577926e-06, 4.485449120092847e-06, 4.485449120092847e-06, 6.819418670300624e-06, 6.819418670300624e-06, 1.0410141055235663e-05, 1.0410141055235663e-05, 1.593432933975111e-05, 1.593432933975111e-05, 2.4433080546697948e-05, 2.4433080546697948e-05, 3.7508082403539234e-05, 3.7508082403539234e-05, 5.7623469875602755e-05, 5.7623469875602755e-05, 8.857021983262358e-05, 8.857021983262358e-05, 0.00013618060438188635, 0.00013618060438188635, 0.00020942734984229062, 0.00020942734984229062, 0.00032211465055060484, 0.00032211465055060484, 0.0004954797285633961, 0.0004954797285633961]
[2025-04-02 19:51:30 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][0/234]	eta 0:08:37 lr 0.000495	time 2.2132 (2.2132)	loss 0.6083 (0.6083)	grad_norm 2.0122 (2.0122)	mem 20675MB
[2025-04-02 19:51:32 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][2/234]	eta 0:05:06 lr 0.000495	time 0.8772 (1.3232)	loss 0.3542 (0.4933)	grad_norm 3.1031 (2.7153)	mem 20675MB
[2025-04-02 19:51:34 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][4/234]	eta 0:04:23 lr 0.000494	time 0.8765 (1.1449)	loss 0.5106 (0.4858)	grad_norm 3.1146 (2.7610)	mem 20675MB
[2025-04-02 19:51:36 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][6/234]	eta 0:04:03 lr 0.000494	time 0.8765 (1.0686)	loss 0.3187 (0.4645)	grad_norm 3.5532 (2.8751)	mem 20675MB
[2025-04-02 19:51:37 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][8/234]	eta 0:03:51 lr 0.000493	time 0.8771 (1.0261)	loss 0.3789 (0.4679)	grad_norm 3.0363 (2.9767)	mem 20675MB
[2025-04-02 19:51:39 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][10/234]	eta 0:03:43 lr 0.000492	time 0.8767 (0.9991)	loss 0.5001 (0.4724)	grad_norm 2.1904 (2.8578)	mem 20675MB
[2025-04-02 19:51:41 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][12/234]	eta 0:03:37 lr 0.000492	time 0.8768 (0.9803)	loss 0.5585 (0.4824)	grad_norm 3.5442 (2.9379)	mem 20675MB
[2025-04-02 19:51:43 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][14/234]	eta 0:03:32 lr 0.000491	time 0.8763 (0.9666)	loss 0.5424 (0.4776)	grad_norm 1.9343 (2.8584)	mem 20675MB
[2025-04-02 19:51:44 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][16/234]	eta 0:03:28 lr 0.000491	time 0.8767 (0.9561)	loss 0.3228 (0.4742)	grad_norm 3.4732 (2.8969)	mem 20675MB
[2025-04-02 19:51:46 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][18/234]	eta 0:03:24 lr 0.000490	time 0.8763 (0.9478)	loss 0.4683 (0.4695)	grad_norm 4.3488 (2.9765)	mem 20675MB
[2025-04-02 19:51:48 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][20/234]	eta 0:03:21 lr 0.000490	time 0.8763 (0.9411)	loss 0.5650 (0.4661)	grad_norm 3.3980 (3.0361)	mem 20675MB
[2025-04-02 19:51:50 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][22/234]	eta 0:03:18 lr 0.000489	time 0.8766 (0.9355)	loss 0.5936 (0.4681)	grad_norm 3.8592 (3.0634)	mem 20675MB
[2025-04-02 19:51:51 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][24/234]	eta 0:03:15 lr 0.000489	time 0.8768 (0.9309)	loss 0.3288 (0.4696)	grad_norm 3.5068 (3.1023)	mem 20675MB
[2025-04-02 19:51:53 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][26/234]	eta 0:03:12 lr 0.000488	time 0.8768 (0.9269)	loss 0.4754 (0.4731)	grad_norm 2.8182 (3.0806)	mem 20675MB
[2025-04-02 19:51:55 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][28/234]	eta 0:03:10 lr 0.000488	time 0.8769 (0.9235)	loss 0.2998 (0.4640)	grad_norm 2.7547 (3.0883)	mem 20675MB
[2025-04-02 19:51:57 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][30/234]	eta 0:03:07 lr 0.000487	time 0.8766 (0.9206)	loss 0.4086 (0.4615)	grad_norm 3.4571 (3.0855)	mem 20675MB
[2025-04-02 19:51:58 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][32/234]	eta 0:03:05 lr 0.000486	time 0.8771 (0.9180)	loss 0.5197 (0.4638)	grad_norm 2.1933 (3.0425)	mem 20675MB
[2025-04-02 19:52:00 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][34/234]	eta 0:03:03 lr 0.000486	time 0.8771 (0.9157)	loss 0.5786 (0.4654)	grad_norm 1.9545 (3.0133)	mem 20675MB
[2025-04-02 19:52:02 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][36/234]	eta 0:03:00 lr 0.000485	time 0.8764 (0.9136)	loss 0.4090 (0.4652)	grad_norm 4.5338 (3.0313)	mem 20675MB
[2025-04-02 19:52:04 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][38/234]	eta 0:02:58 lr 0.000485	time 0.8766 (0.9118)	loss 0.5668 (0.4691)	grad_norm 2.1901 (2.9901)	mem 20675MB
[2025-04-02 19:52:05 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][40/234]	eta 0:02:56 lr 0.000484	time 0.8764 (0.9101)	loss 0.5289 (0.4715)	grad_norm 2.8323 (2.9643)	mem 20675MB
[2025-04-02 19:52:07 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][42/234]	eta 0:02:54 lr 0.000484	time 0.8763 (0.9085)	loss 0.4257 (0.4712)	grad_norm 2.5458 (2.9377)	mem 20675MB
[2025-04-02 19:52:09 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][44/234]	eta 0:02:52 lr 0.000483	time 0.8765 (0.9071)	loss 0.5353 (0.4716)	grad_norm 1.8719 (2.8916)	mem 20675MB
[2025-04-02 19:52:11 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][46/234]	eta 0:02:50 lr 0.000483	time 0.8767 (0.9059)	loss 0.5340 (0.4726)	grad_norm 2.2786 (2.8884)	mem 20675MB
[2025-04-02 19:52:12 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][48/234]	eta 0:02:48 lr 0.000482	time 0.8768 (0.9047)	loss 0.3456 (0.4703)	grad_norm 2.7264 (2.8893)	mem 20675MB
[2025-04-02 19:52:14 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][50/234]	eta 0:02:46 lr 0.000482	time 0.8770 (0.9037)	loss 0.4854 (0.4702)	grad_norm 2.1861 (2.8843)	mem 20675MB
[2025-04-02 19:52:16 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][52/234]	eta 0:02:44 lr 0.000481	time 0.8786 (0.9028)	loss 0.4322 (0.4688)	grad_norm 2.2300 (2.8998)	mem 20675MB
[2025-04-02 19:52:18 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][54/234]	eta 0:02:42 lr 0.000480	time 0.8770 (0.9019)	loss 0.5368 (0.4681)	grad_norm 2.8004 (2.9138)	mem 20675MB
[2025-04-02 19:52:19 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][56/234]	eta 0:02:40 lr 0.000480	time 0.8766 (0.9010)	loss 0.4783 (0.4677)	grad_norm 2.8910 (2.9225)	mem 20675MB
[2025-04-02 19:52:21 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][58/234]	eta 0:02:38 lr 0.000479	time 0.8783 (0.9003)	loss 0.5202 (0.4699)	grad_norm 2.4929 (2.9156)	mem 20675MB
[2025-04-02 19:52:23 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][60/234]	eta 0:02:36 lr 0.000479	time 0.8772 (0.8996)	loss 0.5073 (0.4720)	grad_norm 2.1881 (2.9020)	mem 20675MB
[2025-04-02 19:52:25 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][62/234]	eta 0:02:34 lr 0.000478	time 0.8770 (0.8989)	loss 0.4764 (0.4728)	grad_norm 2.2113 (2.8874)	mem 20675MB
[2025-04-02 19:52:27 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][64/234]	eta 0:02:32 lr 0.000478	time 0.8773 (0.8984)	loss 0.5918 (0.4740)	grad_norm 2.0243 (2.8720)	mem 20675MB
[2025-04-02 19:52:28 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][66/234]	eta 0:02:30 lr 0.000477	time 0.8782 (0.8978)	loss 0.5216 (0.4751)	grad_norm 2.2863 (2.8479)	mem 20675MB
[2025-04-02 19:52:30 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][68/234]	eta 0:02:28 lr 0.000477	time 0.8766 (0.8973)	loss 0.5028 (0.4756)	grad_norm 1.8185 (2.8282)	mem 20675MB
[2025-04-02 19:52:32 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][70/234]	eta 0:02:27 lr 0.000476	time 0.8775 (0.8968)	loss 0.4788 (0.4762)	grad_norm 2.8901 (2.8170)	mem 20675MB
[2025-04-02 19:52:34 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][72/234]	eta 0:02:25 lr 0.000476	time 0.8774 (0.8962)	loss 0.4949 (0.4766)	grad_norm 2.3793 (2.7966)	mem 20675MB
[2025-04-02 19:52:35 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][74/234]	eta 0:02:23 lr 0.000475	time 0.8770 (0.8958)	loss 0.5539 (0.4769)	grad_norm 2.1239 (2.7862)	mem 20675MB
[2025-04-02 19:52:37 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][76/234]	eta 0:02:21 lr 0.000474	time 0.8789 (0.8953)	loss 0.5406 (0.4768)	grad_norm 2.1540 (2.8028)	mem 20675MB
[2025-04-02 19:52:39 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][78/234]	eta 0:02:19 lr 0.000474	time 0.8770 (0.8949)	loss 0.4764 (0.4781)	grad_norm 1.8917 (2.7878)	mem 20675MB
[2025-04-02 19:52:41 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][80/234]	eta 0:02:17 lr 0.000473	time 0.8870 (0.8946)	loss 0.4191 (0.4762)	grad_norm 2.4703 (2.7954)	mem 20675MB
[2025-04-02 19:52:42 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][82/234]	eta 0:02:15 lr 0.000473	time 0.8780 (0.8944)	loss 0.4688 (0.4752)	grad_norm 3.9542 (2.8076)	mem 20675MB
[2025-04-02 19:52:44 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][84/234]	eta 0:02:14 lr 0.000472	time 0.8779 (0.8940)	loss 0.5120 (0.4763)	grad_norm 2.9508 (2.8067)	mem 20675MB
[2025-04-02 19:52:46 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][86/234]	eta 0:02:12 lr 0.000472	time 0.8780 (0.8937)	loss 0.3333 (0.4731)	grad_norm 4.8065 (2.8254)	mem 20675MB
[2025-04-02 19:52:48 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][88/234]	eta 0:02:10 lr 0.000471	time 0.8786 (0.8934)	loss 0.3318 (0.4702)	grad_norm 2.6172 (2.8393)	mem 20675MB
[2025-04-02 19:52:49 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][90/234]	eta 0:02:08 lr 0.000471	time 0.8772 (0.8931)	loss 0.4181 (0.4697)	grad_norm 2.7266 (2.8407)	mem 20675MB
[2025-04-02 19:52:51 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][92/234]	eta 0:02:06 lr 0.000470	time 0.8765 (0.8927)	loss 0.4058 (0.4680)	grad_norm 3.2121 (2.8433)	mem 20675MB
[2025-04-02 19:52:53 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][94/234]	eta 0:02:04 lr 0.000470	time 0.8802 (0.8925)	loss 0.5042 (0.4693)	grad_norm 2.5729 (2.8450)	mem 20675MB
[2025-04-02 19:52:55 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][96/234]	eta 0:02:03 lr 0.000469	time 0.8766 (0.8922)	loss 0.5645 (0.4697)	grad_norm 2.2304 (2.8384)	mem 20675MB
[2025-04-02 19:52:56 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][98/234]	eta 0:02:01 lr 0.000469	time 0.8778 (0.8919)	loss 0.5818 (0.4695)	grad_norm 2.7188 (2.8416)	mem 20675MB
[2025-04-02 19:52:58 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][100/234]	eta 0:01:59 lr 0.000468	time 0.8856 (0.8917)	loss 0.3611 (0.4681)	grad_norm 3.0851 (2.8535)	mem 20675MB
[2025-04-02 19:53:00 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][102/234]	eta 0:01:57 lr 0.000467	time 0.9015 (0.8917)	loss 0.4729 (0.4679)	grad_norm 2.3494 (2.8451)	mem 20675MB
[2025-04-02 19:53:02 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][104/234]	eta 0:01:55 lr 0.000467	time 0.8797 (0.8916)	loss 0.3011 (0.4666)	grad_norm 3.1466 (2.8487)	mem 20675MB
[2025-04-02 19:53:03 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][106/234]	eta 0:01:54 lr 0.000466	time 0.8777 (0.8914)	loss 0.3551 (0.4644)	grad_norm 3.9230 (2.8642)	mem 20675MB
[2025-04-02 19:53:05 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][108/234]	eta 0:01:52 lr 0.000466	time 0.8910 (0.8914)	loss 0.5026 (0.4648)	grad_norm 2.5482 (2.8526)	mem 20675MB
[2025-04-02 19:53:07 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][110/234]	eta 0:01:50 lr 0.000465	time 0.8785 (0.8912)	loss 0.4528 (0.4650)	grad_norm 2.5815 (2.8488)	mem 20675MB
[2025-04-02 19:53:09 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][112/234]	eta 0:01:48 lr 0.000465	time 0.8782 (0.8910)	loss 0.5523 (0.4649)	grad_norm 3.4934 (2.8570)	mem 20675MB
[2025-04-02 19:53:11 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][114/234]	eta 0:01:46 lr 0.000464	time 0.8788 (0.8908)	loss 0.5082 (0.4658)	grad_norm 4.6615 (2.8716)	mem 20675MB
[2025-04-02 19:53:12 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][116/234]	eta 0:01:45 lr 0.000464	time 0.8778 (0.8907)	loss 0.5016 (0.4663)	grad_norm 3.7716 (2.8707)	mem 20675MB
[2025-04-02 19:53:14 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][118/234]	eta 0:01:43 lr 0.000463	time 0.8796 (0.8905)	loss 0.3711 (0.4658)	grad_norm 4.0960 (2.8816)	mem 20675MB
[2025-04-02 19:53:16 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][120/234]	eta 0:01:41 lr 0.000463	time 0.8851 (0.8904)	loss 0.3688 (0.4647)	grad_norm 5.2559 (2.9011)	mem 20675MB
[2025-04-02 19:53:18 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][122/234]	eta 0:01:39 lr 0.000462	time 0.8989 (0.8904)	loss 0.5086 (0.4646)	grad_norm 2.8150 (2.8990)	mem 20675MB
[2025-04-02 19:53:19 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][124/234]	eta 0:01:37 lr 0.000462	time 0.8779 (0.8902)	loss 0.3185 (0.4626)	grad_norm 3.2109 (2.9019)	mem 20675MB
[2025-04-02 19:53:21 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][126/234]	eta 0:01:36 lr 0.000461	time 0.8799 (0.8900)	loss 0.3102 (0.4622)	grad_norm 4.1384 (2.9062)	mem 20675MB
[2025-04-02 19:53:23 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][128/234]	eta 0:01:34 lr 0.000460	time 0.8829 (0.8899)	loss 0.5088 (0.4630)	grad_norm 2.2077 (2.9273)	mem 20675MB
[2025-04-02 19:53:25 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][130/234]	eta 0:01:32 lr 0.000460	time 0.8804 (0.8898)	loss 0.4629 (0.4630)	grad_norm 3.4921 (2.9323)	mem 20675MB
[2025-04-02 19:53:26 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][132/234]	eta 0:01:30 lr 0.000459	time 0.8797 (0.8896)	loss 0.5551 (0.4643)	grad_norm 2.4483 (2.9258)	mem 20675MB
[2025-04-02 19:53:28 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][134/234]	eta 0:01:28 lr 0.000459	time 0.8767 (0.8895)	loss 0.4643 (0.4644)	grad_norm 2.6615 (2.9259)	mem 20675MB
[2025-04-02 19:53:30 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][136/234]	eta 0:01:27 lr 0.000458	time 0.8787 (0.8895)	loss 0.4583 (0.4645)	grad_norm 1.6038 (2.9166)	mem 20675MB
[2025-04-02 19:53:32 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][138/234]	eta 0:01:25 lr 0.000458	time 0.8782 (0.8893)	loss 0.4697 (0.4640)	grad_norm 2.8949 (2.9174)	mem 20675MB
[2025-04-02 19:53:33 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][140/234]	eta 0:01:23 lr 0.000457	time 0.8794 (0.8892)	loss 0.4982 (0.4641)	grad_norm 2.0139 (2.9101)	mem 20675MB
[2025-04-02 19:53:35 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][142/234]	eta 0:01:21 lr 0.000457	time 0.8786 (0.8891)	loss 0.4410 (0.4625)	grad_norm 3.0218 (2.9101)	mem 20675MB
[2025-04-02 19:53:37 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][144/234]	eta 0:01:20 lr 0.000456	time 0.8790 (0.8889)	loss 0.4519 (0.4633)	grad_norm 2.5968 (2.9042)	mem 20675MB
[2025-04-02 19:53:39 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][146/234]	eta 0:01:18 lr 0.000456	time 0.8793 (0.8888)	loss 0.4880 (0.4636)	grad_norm 1.7254 (2.8933)	mem 20675MB
[2025-04-02 19:53:41 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][148/234]	eta 0:01:16 lr 0.000455	time 0.8830 (0.8887)	loss 0.5550 (0.4639)	grad_norm 3.3037 (2.8950)	mem 20675MB
[2025-04-02 19:53:42 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][150/234]	eta 0:01:14 lr 0.000454	time 0.8837 (0.8886)	loss 0.5033 (0.4643)	grad_norm 2.3897 (2.8892)	mem 20675MB
[2025-04-02 19:53:44 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][152/234]	eta 0:01:12 lr 0.000454	time 0.8790 (0.8885)	loss 0.4264 (0.4638)	grad_norm 2.9550 (2.8906)	mem 20675MB
[2025-04-02 19:53:46 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][154/234]	eta 0:01:11 lr 0.000453	time 0.8774 (0.8884)	loss 0.3635 (0.4633)	grad_norm 2.3489 (2.8799)	mem 20675MB
[2025-04-02 19:53:48 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][156/234]	eta 0:01:09 lr 0.000453	time 0.8794 (0.8883)	loss 0.4956 (0.4624)	grad_norm 2.4020 (2.8912)	mem 20675MB
[2025-04-02 19:53:49 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][158/234]	eta 0:01:07 lr 0.000452	time 0.8782 (0.8882)	loss 0.4654 (0.4621)	grad_norm 2.2559 (2.8867)	mem 20675MB
[2025-04-02 19:53:51 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][160/234]	eta 0:01:05 lr 0.000452	time 0.8801 (0.8882)	loss 0.6250 (0.4634)	grad_norm 3.2938 (2.8870)	mem 20675MB
[2025-04-02 19:53:53 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][162/234]	eta 0:01:03 lr 0.000451	time 0.8785 (0.8881)	loss 0.3694 (0.4634)	grad_norm 4.1434 (2.8957)	mem 20675MB
[2025-04-02 19:53:55 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][164/234]	eta 0:01:02 lr 0.000451	time 0.8788 (0.8880)	loss 0.5890 (0.4639)	grad_norm 4.0509 (2.9056)	mem 20675MB
[2025-04-02 19:53:56 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][166/234]	eta 0:01:00 lr 0.000450	time 0.8860 (0.8879)	loss 0.4273 (0.4626)	grad_norm 2.4180 (2.9086)	mem 20675MB
[2025-04-02 19:53:58 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][168/234]	eta 0:00:58 lr 0.000450	time 0.8865 (0.8879)	loss 0.4239 (0.4614)	grad_norm 3.0545 (2.9139)	mem 20675MB
[2025-04-02 19:54:00 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][170/234]	eta 0:00:56 lr 0.000449	time 0.8815 (0.8879)	loss 0.5471 (0.4621)	grad_norm 2.9460 (2.9104)	mem 20675MB
[2025-04-02 19:54:02 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][172/234]	eta 0:00:55 lr 0.000449	time 0.8782 (0.8879)	loss 0.5641 (0.4630)	grad_norm 1.6683 (2.8991)	mem 20675MB
[2025-04-02 19:54:03 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][174/234]	eta 0:00:53 lr 0.000448	time 0.8797 (0.8879)	loss 0.5181 (0.4634)	grad_norm 2.3704 (2.8989)	mem 20675MB
[2025-04-02 19:54:05 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][176/234]	eta 0:00:51 lr 0.000448	time 0.8797 (0.8878)	loss 0.5886 (0.4634)	grad_norm 2.8540 (2.8935)	mem 20675MB
[2025-04-02 19:54:07 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][178/234]	eta 0:00:49 lr 0.000447	time 0.8910 (0.8878)	loss 0.5709 (0.4635)	grad_norm 3.0374 (2.8952)	mem 20675MB
[2025-04-02 19:54:09 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][180/234]	eta 0:00:47 lr 0.000446	time 0.8801 (0.8877)	loss 0.5182 (0.4633)	grad_norm 2.7638 (2.8904)	mem 20675MB
[2025-04-02 19:54:11 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][182/234]	eta 0:00:46 lr 0.000446	time 0.8788 (0.8876)	loss 0.2721 (0.4625)	grad_norm 2.3862 (2.8836)	mem 20675MB
[2025-04-02 19:54:12 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][184/234]	eta 0:00:44 lr 0.000445	time 0.8782 (0.8876)	loss 0.5102 (0.4624)	grad_norm 2.3550 (2.8850)	mem 20675MB
[2025-04-02 19:54:14 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][186/234]	eta 0:00:42 lr 0.000445	time 0.8798 (0.8875)	loss 0.5274 (0.4630)	grad_norm 2.9364 (2.8819)	mem 20675MB
[2025-04-02 19:54:16 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][188/234]	eta 0:00:40 lr 0.000444	time 0.8797 (0.8874)	loss 0.4714 (0.4634)	grad_norm 2.6431 (2.8747)	mem 20675MB
[2025-04-02 19:54:18 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][190/234]	eta 0:00:39 lr 0.000444	time 0.8802 (0.8874)	loss 0.4453 (0.4628)	grad_norm 3.3906 (2.8770)	mem 20675MB
[2025-04-02 19:54:19 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][192/234]	eta 0:00:37 lr 0.000443	time 0.8806 (0.8873)	loss 0.5838 (0.4635)	grad_norm 3.3272 (2.8823)	mem 20675MB
[2025-04-02 19:54:21 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][194/234]	eta 0:00:35 lr 0.000443	time 0.8782 (0.8873)	loss 0.4675 (0.4640)	grad_norm 2.6676 (2.8880)	mem 20675MB
[2025-04-02 19:54:23 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][196/234]	eta 0:00:33 lr 0.000442	time 0.8777 (0.8872)	loss 0.4079 (0.4642)	grad_norm 3.1138 (2.8841)	mem 20675MB
[2025-04-02 19:54:25 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][198/234]	eta 0:00:31 lr 0.000442	time 0.8790 (0.8871)	loss 0.5579 (0.4647)	grad_norm 2.6254 (2.8852)	mem 20675MB
[2025-04-02 19:54:26 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][200/234]	eta 0:00:30 lr 0.000441	time 0.8782 (0.8871)	loss 0.4667 (0.4649)	grad_norm 2.5810 (2.8829)	mem 20675MB
[2025-04-02 19:54:28 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][202/234]	eta 0:00:28 lr 0.000441	time 0.8825 (0.8870)	loss 0.5595 (0.4653)	grad_norm 2.4680 (2.8802)	mem 20675MB
[2025-04-02 19:54:30 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][204/234]	eta 0:00:26 lr 0.000440	time 0.8782 (0.8869)	loss 0.4533 (0.4657)	grad_norm 2.8751 (2.8778)	mem 20675MB
[2025-04-02 19:54:32 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][206/234]	eta 0:00:24 lr 0.000439	time 0.8795 (0.8869)	loss 0.5091 (0.4661)	grad_norm 2.2601 (2.8739)	mem 20675MB
[2025-04-02 19:54:33 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][208/234]	eta 0:00:23 lr 0.000439	time 0.8794 (0.8869)	loss 0.3692 (0.4649)	grad_norm 2.8776 (2.8843)	mem 20675MB
[2025-04-02 19:54:35 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][210/234]	eta 0:00:21 lr 0.000438	time 0.8773 (0.8868)	loss 0.5708 (0.4652)	grad_norm 2.4684 (2.8801)	mem 20675MB
[2025-04-02 19:54:37 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][212/234]	eta 0:00:19 lr 0.000438	time 0.8783 (0.8867)	loss 0.5303 (0.4653)	grad_norm 2.8699 (2.8871)	mem 20675MB
[2025-04-02 19:54:39 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][214/234]	eta 0:00:17 lr 0.000437	time 0.9249 (0.8869)	loss 0.3592 (0.4644)	grad_norm 3.6149 (2.8967)	mem 20675MB
[2025-04-02 19:54:41 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][216/234]	eta 0:00:15 lr 0.000437	time 0.8956 (0.8870)	loss 0.5337 (0.4646)	grad_norm 2.8681 (2.8938)	mem 20675MB
[2025-04-02 19:54:42 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][218/234]	eta 0:00:14 lr 0.000436	time 0.8785 (0.8870)	loss 0.5689 (0.4658)	grad_norm 3.2285 (2.8972)	mem 20675MB
[2025-04-02 19:54:44 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][220/234]	eta 0:00:12 lr 0.000436	time 0.8913 (0.8869)	loss 0.4998 (0.4652)	grad_norm 2.1036 (2.8934)	mem 20675MB
[2025-04-02 19:54:46 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][222/234]	eta 0:00:10 lr 0.000435	time 0.8778 (0.8869)	loss 0.4707 (0.4651)	grad_norm 2.9530 (2.8918)	mem 20675MB
[2025-04-02 19:54:48 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][224/234]	eta 0:00:08 lr 0.000435	time 0.8782 (0.8868)	loss 0.5402 (0.4655)	grad_norm 2.1754 (2.8902)	mem 20675MB
[2025-04-02 19:54:49 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][226/234]	eta 0:00:07 lr 0.000434	time 0.8782 (0.8868)	loss 0.5189 (0.4656)	grad_norm 2.3525 (2.8913)	mem 20675MB
[2025-04-02 19:54:51 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][228/234]	eta 0:00:05 lr 0.000434	time 0.8833 (0.8868)	loss 0.4986 (0.4661)	grad_norm 2.8040 (2.8866)	mem 20675MB
[2025-04-02 19:54:53 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][230/234]	eta 0:00:03 lr 0.000433	time 0.8783 (0.8867)	loss 0.5741 (0.4662)	grad_norm 2.0547 (2.8803)	mem 20675MB
[2025-04-02 19:54:55 simmim_finetune] (main_finetune.py 252): INFO Train: [17/30][232/234]	eta 0:00:01 lr 0.000433	time 0.8772 (0.8866)	loss 0.5437 (0.4667)	grad_norm 2.2888 (2.8797)	mem 20675MB
[2025-04-02 19:54:56 simmim_finetune] (main_finetune.py 260): INFO EPOCH 17 training takes 0:03:27
[2025-04-02 19:54:58 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.962 (1.962)	Loss 0.6921 (0.6921)	Acc@1 66.406 (66.406)	Mem 20675MB
[2025-04-02 19:54:58 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 72.928
[2025-04-02 19:54:58 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 72.9%
[2025-04-02 19:54:58 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 72.93%
[2025-04-02 19:54:58 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [1.761249010452843e-06, 1.761249010452843e-06, 2.6215355985400186e-06, 2.6215355985400186e-06, 3.945053426366442e-06, 3.945053426366442e-06, 5.981234699945556e-06, 5.981234699945556e-06, 9.11382127468265e-06, 9.11382127468265e-06, 1.3933185235816647e-05, 1.3933185235816647e-05, 2.1347591329868945e-05, 2.1347591329868945e-05, 3.275436993610325e-05, 3.275436993610325e-05, 5.0303260099540636e-05, 5.0303260099540636e-05, 7.73015526586751e-05, 7.73015526586751e-05, 0.00011883738736503579, 0.00011883738736503579, 0.00018273867152866763, 0.00018273867152866763, 0.0002810483394727166, 0.0002810483394727166, 0.0004322939824635611, 0.0004322939824635611]
[2025-04-02 19:55:01 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][0/234]	eta 0:13:24 lr 0.000432	time 3.4373 (3.4373)	loss 0.3834 (0.3834)	grad_norm 3.5482 (3.5482)	mem 20675MB
[2025-04-02 19:55:03 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][2/234]	eta 0:06:42 lr 0.000431	time 0.8802 (1.7364)	loss 0.5250 (0.4745)	grad_norm 2.6951 (2.8341)	mem 20675MB
[2025-04-02 19:55:05 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][4/234]	eta 0:05:21 lr 0.000431	time 0.8792 (1.3964)	loss 0.4766 (0.4946)	grad_norm 1.9423 (2.5583)	mem 20675MB
[2025-04-02 19:55:07 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][6/234]	eta 0:04:44 lr 0.000430	time 0.8771 (1.2485)	loss 0.4897 (0.4808)	grad_norm 2.1131 (2.4571)	mem 20675MB
[2025-04-02 19:55:08 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][8/234]	eta 0:04:23 lr 0.000430	time 0.8777 (1.1667)	loss 0.5506 (0.4889)	grad_norm 3.0711 (2.4869)	mem 20675MB
[2025-04-02 19:55:10 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][10/234]	eta 0:04:09 lr 0.000429	time 0.8774 (1.1143)	loss 0.5470 (0.4827)	grad_norm 2.0689 (2.4865)	mem 20675MB
[2025-04-02 19:55:12 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][12/234]	eta 0:03:59 lr 0.000429	time 0.8945 (1.0797)	loss 0.6117 (0.4823)	grad_norm 3.3819 (2.9164)	mem 20675MB
[2025-04-02 19:55:14 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][14/234]	eta 0:03:51 lr 0.000428	time 0.8883 (1.0536)	loss 0.4956 (0.4759)	grad_norm 2.4808 (2.8442)	mem 20675MB
[2025-04-02 19:55:15 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][16/234]	eta 0:03:45 lr 0.000428	time 0.8825 (1.0333)	loss 0.3019 (0.4569)	grad_norm 4.2603 (2.9840)	mem 20675MB
[2025-04-02 19:55:17 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][18/234]	eta 0:03:39 lr 0.000427	time 0.9017 (1.0183)	loss 0.4612 (0.4632)	grad_norm 3.7093 (2.9928)	mem 20675MB
[2025-04-02 19:55:19 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][20/234]	eta 0:03:35 lr 0.000427	time 0.8769 (1.0050)	loss 0.5316 (0.4635)	grad_norm 3.1787 (2.9647)	mem 20675MB
[2025-04-02 19:55:21 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][22/234]	eta 0:03:30 lr 0.000426	time 0.8791 (0.9945)	loss 0.4477 (0.4644)	grad_norm 2.5789 (2.9331)	mem 20675MB
[2025-04-02 19:55:23 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][24/234]	eta 0:03:26 lr 0.000426	time 0.8777 (0.9853)	loss 0.4748 (0.4603)	grad_norm 2.7771 (2.9438)	mem 20675MB
[2025-04-02 19:55:24 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][26/234]	eta 0:03:23 lr 0.000425	time 0.8770 (0.9774)	loss 0.5187 (0.4613)	grad_norm 1.7659 (2.8687)	mem 20675MB
[2025-04-02 19:55:26 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][28/234]	eta 0:03:19 lr 0.000425	time 0.8780 (0.9708)	loss 0.5279 (0.4661)	grad_norm 2.7354 (2.8434)	mem 20675MB
[2025-04-02 19:55:28 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][30/234]	eta 0:03:16 lr 0.000424	time 0.8885 (0.9652)	loss 0.3459 (0.4654)	grad_norm 2.6406 (2.8279)	mem 20675MB
[2025-04-02 19:55:30 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][32/234]	eta 0:03:14 lr 0.000424	time 0.8797 (0.9604)	loss 0.3313 (0.4635)	grad_norm 2.8081 (2.8385)	mem 20675MB
[2025-04-02 19:55:31 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][34/234]	eta 0:03:11 lr 0.000423	time 0.8806 (0.9560)	loss 0.5859 (0.4651)	grad_norm 3.0457 (2.8440)	mem 20675MB
[2025-04-02 19:55:33 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][36/234]	eta 0:03:08 lr 0.000422	time 0.9094 (0.9530)	loss 0.4998 (0.4673)	grad_norm 2.6602 (2.8193)	mem 20675MB
[2025-04-02 19:55:35 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][38/234]	eta 0:03:06 lr 0.000422	time 0.8819 (0.9493)	loss 0.5048 (0.4681)	grad_norm 3.3636 (2.8297)	mem 20675MB
[2025-04-02 19:55:37 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][40/234]	eta 0:03:03 lr 0.000421	time 0.8801 (0.9461)	loss 0.4222 (0.4668)	grad_norm 2.7284 (2.8436)	mem 20675MB
[2025-04-02 19:55:38 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][42/234]	eta 0:03:01 lr 0.000421	time 0.8814 (0.9430)	loss 0.4009 (0.4666)	grad_norm 2.7351 (2.8327)	mem 20675MB
[2025-04-02 19:55:40 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][44/234]	eta 0:02:58 lr 0.000420	time 0.8848 (0.9407)	loss 0.5076 (0.4685)	grad_norm 2.5911 (2.8167)	mem 20675MB
[2025-04-02 19:55:42 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][46/234]	eta 0:02:56 lr 0.000420	time 0.8905 (0.9385)	loss 0.4668 (0.4695)	grad_norm 2.6228 (2.8063)	mem 20675MB
[2025-04-02 19:55:44 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][48/234]	eta 0:02:54 lr 0.000419	time 0.8775 (0.9360)	loss 0.2995 (0.4675)	grad_norm 2.4167 (2.7752)	mem 20675MB
[2025-04-02 19:55:46 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][50/234]	eta 0:02:51 lr 0.000419	time 0.8828 (0.9341)	loss 0.4105 (0.4649)	grad_norm 3.0353 (2.7865)	mem 20675MB
[2025-04-02 19:55:47 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][52/234]	eta 0:02:49 lr 0.000418	time 0.8768 (0.9319)	loss 0.5181 (0.4623)	grad_norm 4.1687 (2.8151)	mem 20675MB
[2025-04-02 19:55:49 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][54/234]	eta 0:02:47 lr 0.000418	time 0.8801 (0.9302)	loss 0.4539 (0.4623)	grad_norm 3.5042 (2.8234)	mem 20675MB
[2025-04-02 19:55:51 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][56/234]	eta 0:02:45 lr 0.000417	time 0.8838 (0.9285)	loss 0.2868 (0.4593)	grad_norm 2.4805 (2.8136)	mem 20675MB
[2025-04-02 19:55:53 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][58/234]	eta 0:02:43 lr 0.000417	time 0.8793 (0.9269)	loss 0.3623 (0.4600)	grad_norm 2.8616 (2.8459)	mem 20675MB
[2025-04-02 19:55:54 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][60/234]	eta 0:02:41 lr 0.000416	time 0.8811 (0.9254)	loss 0.4507 (0.4606)	grad_norm 2.3289 (2.8317)	mem 20675MB
[2025-04-02 19:55:56 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][62/234]	eta 0:02:38 lr 0.000416	time 0.8791 (0.9243)	loss 0.4829 (0.4620)	grad_norm 2.1656 (2.8163)	mem 20675MB
[2025-04-02 19:55:58 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][64/234]	eta 0:02:36 lr 0.000415	time 0.8786 (0.9231)	loss 0.3295 (0.4603)	grad_norm 3.7146 (2.8340)	mem 20675MB
[2025-04-02 19:56:00 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][66/234]	eta 0:02:34 lr 0.000415	time 0.8808 (0.9218)	loss 0.5423 (0.4617)	grad_norm 3.7042 (2.8437)	mem 20675MB
[2025-04-02 19:56:01 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][68/234]	eta 0:02:32 lr 0.000414	time 0.8783 (0.9206)	loss 0.4481 (0.4618)	grad_norm 2.7584 (2.8313)	mem 20675MB
[2025-04-02 19:56:03 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][70/234]	eta 0:02:30 lr 0.000414	time 0.8785 (0.9195)	loss 0.3157 (0.4615)	grad_norm 3.2382 (2.8234)	mem 20675MB
[2025-04-02 19:56:05 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][72/234]	eta 0:02:28 lr 0.000413	time 0.8843 (0.9185)	loss 0.5121 (0.4605)	grad_norm 2.3960 (2.8141)	mem 20675MB
[2025-04-02 19:56:07 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][74/234]	eta 0:02:26 lr 0.000412	time 0.8817 (0.9176)	loss 0.5555 (0.4620)	grad_norm 3.0501 (2.8423)	mem 20675MB
[2025-04-02 19:56:08 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][76/234]	eta 0:02:24 lr 0.000412	time 0.8856 (0.9167)	loss 0.5186 (0.4623)	grad_norm 2.1217 (2.8293)	mem 20675MB
[2025-04-02 19:56:10 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][78/234]	eta 0:02:22 lr 0.000411	time 0.8805 (0.9160)	loss 0.4850 (0.4617)	grad_norm 2.5183 (2.8387)	mem 20675MB
[2025-04-02 19:56:12 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][80/234]	eta 0:02:20 lr 0.000411	time 0.8785 (0.9151)	loss 0.3411 (0.4609)	grad_norm 2.2820 (2.8214)	mem 20675MB
[2025-04-02 19:56:14 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][82/234]	eta 0:02:18 lr 0.000410	time 0.8809 (0.9142)	loss 0.5652 (0.4625)	grad_norm 2.9318 (2.8174)	mem 20675MB
[2025-04-02 19:56:16 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][84/234]	eta 0:02:17 lr 0.000410	time 0.8782 (0.9135)	loss 0.4888 (0.4624)	grad_norm 2.6885 (2.8108)	mem 20675MB
[2025-04-02 19:56:17 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][86/234]	eta 0:02:15 lr 0.000409	time 0.8855 (0.9129)	loss 0.3526 (0.4607)	grad_norm 3.7287 (2.8299)	mem 20675MB
[2025-04-02 19:56:19 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][88/234]	eta 0:02:13 lr 0.000409	time 0.8799 (0.9122)	loss 0.3607 (0.4604)	grad_norm 5.1295 (2.8553)	mem 20675MB
[2025-04-02 19:56:21 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][90/234]	eta 0:02:11 lr 0.000408	time 0.8905 (0.9116)	loss 0.3804 (0.4587)	grad_norm 2.8594 (2.8616)	mem 20675MB
[2025-04-02 19:56:23 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][92/234]	eta 0:02:09 lr 0.000408	time 0.8801 (0.9110)	loss 0.4276 (0.4580)	grad_norm 3.8387 (2.8604)	mem 20675MB
[2025-04-02 19:56:24 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][94/234]	eta 0:02:07 lr 0.000407	time 0.8807 (0.9103)	loss 0.4244 (0.4571)	grad_norm 2.5056 (2.8688)	mem 20675MB
[2025-04-02 19:56:26 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][96/234]	eta 0:02:05 lr 0.000407	time 0.8819 (0.9097)	loss 0.5565 (0.4594)	grad_norm 3.1077 (2.8722)	mem 20675MB
[2025-04-02 19:56:28 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][98/234]	eta 0:02:03 lr 0.000406	time 0.8774 (0.9092)	loss 0.5567 (0.4610)	grad_norm 4.4810 (2.8841)	mem 20675MB
[2025-04-02 19:56:30 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][100/234]	eta 0:02:01 lr 0.000406	time 0.8785 (0.9087)	loss 0.5347 (0.4626)	grad_norm 2.7287 (2.8887)	mem 20675MB
[2025-04-02 19:56:31 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][102/234]	eta 0:01:59 lr 0.000405	time 0.8812 (0.9083)	loss 0.5097 (0.4616)	grad_norm 3.3500 (2.8922)	mem 20675MB
[2025-04-02 19:56:33 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][104/234]	eta 0:01:58 lr 0.000405	time 0.8796 (0.9078)	loss 0.5766 (0.4627)	grad_norm 1.7001 (2.8761)	mem 20675MB
[2025-04-02 19:56:35 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][106/234]	eta 0:01:56 lr 0.000404	time 0.8790 (0.9073)	loss 0.5118 (0.4638)	grad_norm 2.7429 (2.8664)	mem 20675MB
[2025-04-02 19:56:37 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][108/234]	eta 0:01:54 lr 0.000404	time 0.8838 (0.9069)	loss 0.5441 (0.4643)	grad_norm 4.0030 (2.8745)	mem 20675MB
[2025-04-02 19:56:39 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][110/234]	eta 0:01:52 lr 0.000403	time 0.9013 (0.9066)	loss 0.4731 (0.4647)	grad_norm 2.8649 (2.8717)	mem 20675MB
[2025-04-02 19:56:40 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][112/234]	eta 0:01:50 lr 0.000402	time 0.8785 (0.9062)	loss 0.5301 (0.4651)	grad_norm 2.2852 (2.8589)	mem 20675MB
[2025-04-02 19:56:42 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][114/234]	eta 0:01:48 lr 0.000402	time 0.8815 (0.9058)	loss 0.3265 (0.4640)	grad_norm 4.0240 (2.8630)	mem 20675MB
[2025-04-02 19:56:44 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][116/234]	eta 0:01:46 lr 0.000401	time 0.8800 (0.9054)	loss 0.3872 (0.4623)	grad_norm 2.1554 (2.8613)	mem 20675MB
[2025-04-02 19:56:46 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][118/234]	eta 0:01:44 lr 0.000401	time 0.8798 (0.9050)	loss 0.4278 (0.4627)	grad_norm 2.7469 (2.8721)	mem 20675MB
[2025-04-02 19:56:47 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][120/234]	eta 0:01:43 lr 0.000400	time 0.8803 (0.9047)	loss 0.4923 (0.4635)	grad_norm 2.9605 (2.8749)	mem 20675MB
[2025-04-02 19:56:49 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][122/234]	eta 0:01:41 lr 0.000400	time 0.8798 (0.9044)	loss 0.4958 (0.4628)	grad_norm 3.1910 (2.8856)	mem 20675MB
[2025-04-02 19:56:51 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][124/234]	eta 0:01:39 lr 0.000399	time 0.8797 (0.9040)	loss 0.3389 (0.4629)	grad_norm 4.0452 (2.8988)	mem 20675MB
[2025-04-02 19:56:53 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][126/234]	eta 0:01:37 lr 0.000399	time 0.8934 (0.9039)	loss 0.4349 (0.4615)	grad_norm 2.9517 (2.9023)	mem 20675MB
[2025-04-02 19:56:54 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][128/234]	eta 0:01:35 lr 0.000398	time 0.8789 (0.9035)	loss 0.6124 (0.4635)	grad_norm 3.2289 (2.9106)	mem 20675MB
[2025-04-02 19:56:56 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][130/234]	eta 0:01:33 lr 0.000398	time 0.8850 (0.9032)	loss 0.3719 (0.4629)	grad_norm 4.7917 (2.9208)	mem 20675MB
[2025-04-02 19:56:58 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][132/234]	eta 0:01:32 lr 0.000397	time 0.8871 (0.9031)	loss 0.4318 (0.4624)	grad_norm 2.8360 (2.9366)	mem 20675MB
[2025-04-02 19:57:00 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][134/234]	eta 0:01:30 lr 0.000397	time 0.8797 (0.9027)	loss 0.4328 (0.4624)	grad_norm 2.2509 (2.9242)	mem 20675MB
[2025-04-02 19:57:02 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][136/234]	eta 0:01:28 lr 0.000396	time 0.8894 (0.9025)	loss 0.5022 (0.4618)	grad_norm 1.7434 (2.9278)	mem 20675MB
[2025-04-02 19:57:03 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][138/234]	eta 0:01:26 lr 0.000396	time 0.8785 (0.9022)	loss 0.4940 (0.4618)	grad_norm 2.7087 (2.9423)	mem 20675MB
[2025-04-02 19:57:05 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][140/234]	eta 0:01:24 lr 0.000395	time 0.8798 (0.9020)	loss 0.5608 (0.4627)	grad_norm 3.0905 (2.9383)	mem 20675MB
[2025-04-02 19:57:07 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][142/234]	eta 0:01:22 lr 0.000395	time 0.8809 (0.9017)	loss 0.5145 (0.4631)	grad_norm 2.6758 (2.9298)	mem 20675MB
[2025-04-02 19:57:09 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][144/234]	eta 0:01:21 lr 0.000394	time 0.8790 (0.9014)	loss 0.4924 (0.4633)	grad_norm 2.6318 (2.9267)	mem 20675MB
[2025-04-02 19:57:10 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][146/234]	eta 0:01:19 lr 0.000394	time 0.8801 (0.9012)	loss 0.4800 (0.4638)	grad_norm 2.7752 (2.9235)	mem 20675MB
[2025-04-02 19:57:12 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][148/234]	eta 0:01:17 lr 0.000393	time 0.8783 (0.9012)	loss 0.4203 (0.4637)	grad_norm 3.1619 (2.9179)	mem 20675MB
[2025-04-02 19:57:14 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][150/234]	eta 0:01:15 lr 0.000393	time 0.8789 (0.9009)	loss 0.3837 (0.4631)	grad_norm 3.2325 (2.9208)	mem 20675MB
[2025-04-02 19:57:16 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][152/234]	eta 0:01:13 lr 0.000392	time 0.8779 (0.9006)	loss 0.5034 (0.4627)	grad_norm 3.1920 (2.9301)	mem 20675MB
[2025-04-02 19:57:17 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][154/234]	eta 0:01:12 lr 0.000392	time 0.8784 (0.9004)	loss 0.4689 (0.4631)	grad_norm 2.6067 (2.9393)	mem 20675MB
[2025-04-02 19:57:19 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][156/234]	eta 0:01:10 lr 0.000391	time 0.8773 (0.9001)	loss 0.4743 (0.4631)	grad_norm 3.3721 (2.9421)	mem 20675MB
[2025-04-02 19:57:21 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][158/234]	eta 0:01:08 lr 0.000391	time 0.8783 (0.8999)	loss 0.5549 (0.4640)	grad_norm 3.0992 (2.9436)	mem 20675MB
[2025-04-02 19:57:23 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][160/234]	eta 0:01:06 lr 0.000390	time 0.8867 (0.8997)	loss 0.5036 (0.4648)	grad_norm 2.6163 (2.9450)	mem 20675MB
[2025-04-02 19:57:25 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][162/234]	eta 0:01:04 lr 0.000389	time 0.8885 (0.8995)	loss 0.4761 (0.4651)	grad_norm 2.0526 (2.9432)	mem 20675MB
[2025-04-02 19:57:26 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][164/234]	eta 0:01:02 lr 0.000389	time 0.8845 (0.8993)	loss 0.3873 (0.4648)	grad_norm 2.5785 (2.9413)	mem 20675MB
[2025-04-02 19:57:28 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][166/234]	eta 0:01:01 lr 0.000388	time 0.8926 (0.8992)	loss 0.3401 (0.4633)	grad_norm 3.8487 (2.9496)	mem 20675MB
[2025-04-02 19:57:30 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][168/234]	eta 0:00:59 lr 0.000388	time 0.8783 (0.8990)	loss 0.5451 (0.4635)	grad_norm 2.8410 (2.9475)	mem 20675MB
[2025-04-02 19:57:32 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][170/234]	eta 0:00:57 lr 0.000387	time 0.8788 (0.8988)	loss 0.4892 (0.4628)	grad_norm 3.4371 (2.9576)	mem 20675MB
[2025-04-02 19:57:33 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][172/234]	eta 0:00:55 lr 0.000387	time 0.8799 (0.8986)	loss 0.5898 (0.4637)	grad_norm 3.0150 (2.9593)	mem 20675MB
[2025-04-02 19:57:35 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][174/234]	eta 0:00:53 lr 0.000386	time 0.8854 (0.8984)	loss 0.4531 (0.4633)	grad_norm 2.3861 (2.9621)	mem 20675MB
[2025-04-02 19:57:37 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][176/234]	eta 0:00:52 lr 0.000386	time 0.8784 (0.8982)	loss 0.4772 (0.4632)	grad_norm 2.3407 (2.9589)	mem 20675MB
[2025-04-02 19:57:39 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][178/234]	eta 0:00:50 lr 0.000385	time 0.8783 (0.8981)	loss 0.5699 (0.4644)	grad_norm 3.6387 (2.9708)	mem 20675MB
[2025-04-02 19:57:40 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][180/234]	eta 0:00:48 lr 0.000385	time 0.8787 (0.8980)	loss 0.4798 (0.4647)	grad_norm 2.8507 (2.9653)	mem 20675MB
[2025-04-02 19:57:42 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][182/234]	eta 0:00:46 lr 0.000384	time 0.8782 (0.8978)	loss 0.5390 (0.4649)	grad_norm 2.3608 (2.9631)	mem 20675MB
[2025-04-02 19:57:44 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][184/234]	eta 0:00:44 lr 0.000384	time 0.8894 (0.8977)	loss 0.4321 (0.4649)	grad_norm 3.0151 (2.9675)	mem 20675MB
[2025-04-02 19:57:46 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][186/234]	eta 0:00:43 lr 0.000383	time 0.9087 (0.8977)	loss 0.6102 (0.4661)	grad_norm 4.3009 (2.9704)	mem 20675MB
[2025-04-02 19:57:48 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][188/234]	eta 0:00:41 lr 0.000383	time 0.8770 (0.8975)	loss 0.5686 (0.4667)	grad_norm 3.5883 (2.9680)	mem 20675MB
[2025-04-02 19:57:49 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][190/234]	eta 0:00:39 lr 0.000382	time 0.8810 (0.8974)	loss 0.4859 (0.4668)	grad_norm 2.2238 (2.9597)	mem 20675MB
[2025-04-02 19:57:51 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][192/234]	eta 0:00:37 lr 0.000382	time 0.8842 (0.8972)	loss 0.4625 (0.4669)	grad_norm 3.1569 (2.9599)	mem 20675MB
[2025-04-02 19:57:53 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][194/234]	eta 0:00:35 lr 0.000381	time 0.8940 (0.8972)	loss 0.5031 (0.4669)	grad_norm 2.1033 (2.9542)	mem 20675MB
[2025-04-02 19:57:55 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][196/234]	eta 0:00:34 lr 0.000381	time 0.8772 (0.8970)	loss 0.5461 (0.4671)	grad_norm 2.1130 (2.9438)	mem 20675MB
[2025-04-02 19:57:56 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][198/234]	eta 0:00:32 lr 0.000380	time 0.8782 (0.8968)	loss 0.4680 (0.4666)	grad_norm 1.4658 (2.9318)	mem 20675MB
[2025-04-02 19:57:58 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][200/234]	eta 0:00:30 lr 0.000380	time 0.8880 (0.8967)	loss 0.4291 (0.4660)	grad_norm 3.2077 (2.9346)	mem 20675MB
[2025-04-02 19:58:00 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][202/234]	eta 0:00:28 lr 0.000379	time 0.8785 (0.8967)	loss 0.3563 (0.4656)	grad_norm 3.2034 (2.9443)	mem 20675MB
[2025-04-02 19:58:02 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][204/234]	eta 0:00:26 lr 0.000379	time 0.9050 (0.8967)	loss 0.5496 (0.4659)	grad_norm 2.1644 (2.9445)	mem 20675MB
[2025-04-02 19:58:03 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][206/234]	eta 0:00:25 lr 0.000378	time 0.8772 (0.8965)	loss 0.4606 (0.4652)	grad_norm 3.6717 (2.9500)	mem 20675MB
[2025-04-02 19:58:05 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][208/234]	eta 0:00:23 lr 0.000378	time 0.8776 (0.8963)	loss 0.5247 (0.4656)	grad_norm 3.1926 (2.9478)	mem 20675MB
[2025-04-02 19:58:07 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][210/234]	eta 0:00:21 lr 0.000377	time 0.8844 (0.8962)	loss 0.5479 (0.4663)	grad_norm 3.3056 (2.9507)	mem 20675MB
[2025-04-02 19:58:09 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][212/234]	eta 0:00:19 lr 0.000377	time 0.8800 (0.8961)	loss 0.4759 (0.4660)	grad_norm 3.5493 (2.9581)	mem 20675MB
[2025-04-02 19:58:11 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][214/234]	eta 0:00:17 lr 0.000376	time 0.8789 (0.8960)	loss 0.4251 (0.4654)	grad_norm 3.4334 (2.9623)	mem 20675MB
[2025-04-02 19:58:12 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][216/234]	eta 0:00:16 lr 0.000376	time 0.8880 (0.8959)	loss 0.5506 (0.4651)	grad_norm 3.3939 (2.9627)	mem 20675MB
[2025-04-02 19:58:14 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][218/234]	eta 0:00:14 lr 0.000375	time 0.8776 (0.8957)	loss 0.5601 (0.4652)	grad_norm 3.3569 (2.9646)	mem 20675MB
[2025-04-02 19:58:16 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][220/234]	eta 0:00:12 lr 0.000375	time 0.8900 (0.8956)	loss 0.5196 (0.4655)	grad_norm 4.1243 (2.9757)	mem 20675MB
[2025-04-02 19:58:18 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][222/234]	eta 0:00:10 lr 0.000374	time 0.8768 (0.8955)	loss 0.3278 (0.4653)	grad_norm 2.8120 (2.9734)	mem 20675MB
[2025-04-02 19:58:19 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][224/234]	eta 0:00:08 lr 0.000374	time 0.8770 (0.8953)	loss 0.5908 (0.4656)	grad_norm 2.1056 (2.9689)	mem 20675MB
[2025-04-02 19:58:21 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][226/234]	eta 0:00:07 lr 0.000373	time 0.8769 (0.8952)	loss 0.5481 (0.4662)	grad_norm 2.6189 (2.9643)	mem 20675MB
[2025-04-02 19:58:23 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][228/234]	eta 0:00:05 lr 0.000372	time 0.8774 (0.8951)	loss 0.5015 (0.4665)	grad_norm 3.2685 (2.9626)	mem 20675MB
[2025-04-02 19:58:25 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][230/234]	eta 0:00:03 lr 0.000372	time 0.8771 (0.8949)	loss 0.5891 (0.4666)	grad_norm 3.3690 (2.9666)	mem 20675MB
[2025-04-02 19:58:26 simmim_finetune] (main_finetune.py 252): INFO Train: [18/30][232/234]	eta 0:00:01 lr 0.000371	time 0.8778 (0.8948)	loss 0.4886 (0.4668)	grad_norm 3.2327 (2.9644)	mem 20675MB
[2025-04-02 19:58:27 simmim_finetune] (main_finetune.py 260): INFO EPOCH 18 training takes 0:03:29
[2025-04-02 19:58:29 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.185 (1.185)	Loss 0.7147 (0.7147)	Acc@1 67.969 (67.969)	Mem 20675MB
[2025-04-02 19:58:29 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 74.033
[2025-04-02 19:58:29 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 74.0%
[2025-04-02 19:58:29 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 19:58:29 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [1.5476212577400848e-06, 1.5476212577400848e-06, 2.286299104163357e-06, 2.286299104163357e-06, 3.42272656019916e-06, 3.42272656019916e-06, 5.171076492561934e-06, 5.171076492561934e-06, 7.860845619273893e-06, 7.860845619273893e-06, 1.1998951968061522e-05, 1.1998951968061522e-05, 1.8365269427734796e-05, 1.8365269427734796e-05, 2.8159603981078292e-05, 2.8159603981078292e-05, 4.322781098622214e-05, 4.322781098622214e-05, 6.640966791721269e-05, 6.640966791721269e-05, 0.00010207406319565966, 0.00010207406319565966, 0.0001569423636240396, 0.0001569423636240396, 0.00024135513351385493, 0.00024135513351385493, 0.00037122093334434, 0.00037122093334434]
[2025-04-02 19:58:31 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][0/234]	eta 0:09:06 lr 0.000371	time 2.3347 (2.3347)	loss 0.4195 (0.4195)	grad_norm 2.4759 (2.4759)	mem 20675MB
[2025-04-02 19:58:33 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][2/234]	eta 0:05:16 lr 0.000370	time 0.8779 (1.3643)	loss 0.5221 (0.4783)	grad_norm 3.0322 (2.5445)	mem 20675MB
[2025-04-02 19:58:35 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][4/234]	eta 0:04:29 lr 0.000370	time 0.8780 (1.1701)	loss 0.3546 (0.4550)	grad_norm 3.9351 (2.9158)	mem 20675MB
[2025-04-02 19:58:36 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][6/234]	eta 0:04:07 lr 0.000369	time 0.8775 (1.0867)	loss 0.6271 (0.4705)	grad_norm 3.6315 (2.9252)	mem 20675MB
[2025-04-02 19:58:38 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][8/234]	eta 0:03:55 lr 0.000369	time 0.8770 (1.0404)	loss 0.4757 (0.4643)	grad_norm 2.7609 (3.1699)	mem 20675MB
[2025-04-02 19:58:40 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][10/234]	eta 0:03:46 lr 0.000368	time 0.8775 (1.0110)	loss 0.3758 (0.4434)	grad_norm 3.6625 (3.2645)	mem 20675MB
[2025-04-02 19:58:42 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][12/234]	eta 0:03:39 lr 0.000368	time 0.8793 (0.9908)	loss 0.4402 (0.4453)	grad_norm 2.4382 (3.1942)	mem 20675MB
[2025-04-02 19:58:43 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][14/234]	eta 0:03:34 lr 0.000367	time 0.8765 (0.9757)	loss 0.3474 (0.4437)	grad_norm 3.7780 (3.1646)	mem 20675MB
[2025-04-02 19:58:45 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][16/234]	eta 0:03:30 lr 0.000367	time 0.8771 (0.9642)	loss 0.5267 (0.4530)	grad_norm 2.1562 (3.0949)	mem 20675MB
[2025-04-02 19:58:47 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][18/234]	eta 0:03:26 lr 0.000366	time 0.8806 (0.9553)	loss 0.5244 (0.4638)	grad_norm 1.5211 (2.9852)	mem 20675MB
[2025-04-02 19:58:49 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][20/234]	eta 0:03:22 lr 0.000366	time 0.8774 (0.9480)	loss 0.6116 (0.4761)	grad_norm 4.3532 (3.0190)	mem 20675MB
[2025-04-02 19:58:50 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][22/234]	eta 0:03:19 lr 0.000365	time 0.8784 (0.9419)	loss 0.4616 (0.4762)	grad_norm 3.1832 (2.9652)	mem 20675MB
[2025-04-02 19:58:52 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][24/234]	eta 0:03:16 lr 0.000365	time 0.8779 (0.9369)	loss 0.5061 (0.4776)	grad_norm 2.8994 (2.9465)	mem 20675MB
[2025-04-02 19:58:54 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][26/234]	eta 0:03:13 lr 0.000364	time 0.8784 (0.9326)	loss 0.5713 (0.4808)	grad_norm 1.7487 (2.8651)	mem 20675MB
[2025-04-02 19:58:56 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][28/234]	eta 0:03:11 lr 0.000364	time 0.8781 (0.9289)	loss 0.3639 (0.4771)	grad_norm 2.5998 (2.8407)	mem 20675MB
[2025-04-02 19:58:57 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][30/234]	eta 0:03:08 lr 0.000363	time 0.8775 (0.9256)	loss 0.4848 (0.4736)	grad_norm 2.0019 (2.8234)	mem 20675MB
[2025-04-02 19:58:59 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][32/234]	eta 0:03:06 lr 0.000363	time 0.8773 (0.9228)	loss 0.4213 (0.4710)	grad_norm 2.6934 (2.8095)	mem 20675MB
[2025-04-02 19:59:01 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][34/234]	eta 0:03:04 lr 0.000362	time 0.8773 (0.9202)	loss 0.5057 (0.4738)	grad_norm 2.5289 (2.8305)	mem 20675MB
[2025-04-02 19:59:03 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][36/234]	eta 0:03:01 lr 0.000362	time 0.8779 (0.9180)	loss 0.5246 (0.4763)	grad_norm 2.3695 (2.8258)	mem 20675MB
[2025-04-02 19:59:04 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][38/234]	eta 0:02:59 lr 0.000361	time 0.8783 (0.9160)	loss 0.4414 (0.4746)	grad_norm 3.1929 (2.8159)	mem 20675MB
[2025-04-02 19:59:06 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][40/234]	eta 0:02:57 lr 0.000361	time 0.8789 (0.9142)	loss 0.3018 (0.4672)	grad_norm 4.7083 (2.8480)	mem 20675MB
[2025-04-02 19:59:08 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][42/234]	eta 0:02:55 lr 0.000360	time 0.8782 (0.9126)	loss 0.3820 (0.4644)	grad_norm 5.2188 (2.9250)	mem 20675MB
[2025-04-02 19:59:10 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][44/234]	eta 0:02:53 lr 0.000360	time 0.8787 (0.9111)	loss 0.3140 (0.4590)	grad_norm 3.7601 (2.9958)	mem 20675MB
[2025-04-02 19:59:12 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][46/234]	eta 0:02:51 lr 0.000359	time 0.8767 (0.9097)	loss 0.5384 (0.4601)	grad_norm 4.0399 (3.0382)	mem 20675MB
[2025-04-02 19:59:13 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][48/234]	eta 0:02:48 lr 0.000359	time 0.8780 (0.9084)	loss 0.5023 (0.4635)	grad_norm 2.4225 (3.0132)	mem 20675MB
[2025-04-02 19:59:15 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][50/234]	eta 0:02:46 lr 0.000358	time 0.8776 (0.9072)	loss 0.4047 (0.4628)	grad_norm 2.7213 (2.9861)	mem 20675MB
[2025-04-02 19:59:17 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][52/234]	eta 0:02:44 lr 0.000358	time 0.8782 (0.9062)	loss 0.4559 (0.4650)	grad_norm 2.8790 (2.9786)	mem 20675MB
[2025-04-02 19:59:19 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][54/234]	eta 0:02:42 lr 0.000357	time 0.8786 (0.9052)	loss 0.4923 (0.4631)	grad_norm 1.8473 (2.9699)	mem 20675MB
[2025-04-02 19:59:20 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][56/234]	eta 0:02:40 lr 0.000357	time 0.8783 (0.9042)	loss 0.5814 (0.4650)	grad_norm 1.7918 (2.9406)	mem 20675MB
[2025-04-02 19:59:22 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][58/234]	eta 0:02:38 lr 0.000356	time 0.8771 (0.9034)	loss 0.5411 (0.4642)	grad_norm 2.3312 (2.9274)	mem 20675MB
[2025-04-02 19:59:24 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][60/234]	eta 0:02:37 lr 0.000356	time 0.8770 (0.9025)	loss 0.5175 (0.4653)	grad_norm 3.9725 (2.9370)	mem 20675MB
[2025-04-02 19:59:26 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][62/234]	eta 0:02:35 lr 0.000355	time 0.8769 (0.9018)	loss 0.5378 (0.4660)	grad_norm 2.2060 (2.9099)	mem 20675MB
[2025-04-02 19:59:27 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][64/234]	eta 0:02:33 lr 0.000355	time 0.8770 (0.9010)	loss 0.5002 (0.4670)	grad_norm 2.9756 (2.9050)	mem 20675MB
[2025-04-02 19:59:29 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][66/234]	eta 0:02:31 lr 0.000354	time 0.8765 (0.9003)	loss 0.5138 (0.4658)	grad_norm 1.9761 (2.8935)	mem 20675MB
[2025-04-02 19:59:31 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][68/234]	eta 0:02:29 lr 0.000354	time 0.8770 (0.8997)	loss 0.3951 (0.4643)	grad_norm 4.0185 (2.9108)	mem 20675MB
[2025-04-02 19:59:33 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][70/234]	eta 0:02:27 lr 0.000353	time 0.8775 (0.8990)	loss 0.4352 (0.4637)	grad_norm 2.3683 (2.9143)	mem 20675MB
[2025-04-02 19:59:34 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][72/234]	eta 0:02:25 lr 0.000353	time 0.8776 (0.8985)	loss 0.5287 (0.4657)	grad_norm 3.9404 (2.9401)	mem 20675MB
[2025-04-02 19:59:36 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][74/234]	eta 0:02:23 lr 0.000352	time 0.8771 (0.8979)	loss 0.5968 (0.4655)	grad_norm 4.3480 (2.9501)	mem 20675MB
[2025-04-02 19:59:38 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][76/234]	eta 0:02:21 lr 0.000352	time 0.8776 (0.8974)	loss 0.4257 (0.4642)	grad_norm 1.9727 (2.9328)	mem 20675MB
[2025-04-02 19:59:40 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][78/234]	eta 0:02:19 lr 0.000351	time 0.8764 (0.8969)	loss 0.3421 (0.4639)	grad_norm 6.3950 (2.9798)	mem 20675MB
[2025-04-02 19:59:41 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][80/234]	eta 0:02:18 lr 0.000351	time 0.8770 (0.8964)	loss 0.4920 (0.4655)	grad_norm 2.5707 (2.9760)	mem 20675MB
[2025-04-02 19:59:43 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][82/234]	eta 0:02:16 lr 0.000350	time 0.8767 (0.8960)	loss 0.4957 (0.4666)	grad_norm 2.0704 (2.9571)	mem 20675MB
[2025-04-02 19:59:45 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][84/234]	eta 0:02:14 lr 0.000350	time 0.8777 (0.8956)	loss 0.3504 (0.4655)	grad_norm 2.7549 (2.9467)	mem 20675MB
[2025-04-02 19:59:47 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][86/234]	eta 0:02:12 lr 0.000349	time 0.8772 (0.8951)	loss 0.3877 (0.4654)	grad_norm 2.4042 (2.9352)	mem 20675MB
[2025-04-02 19:59:48 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][88/234]	eta 0:02:10 lr 0.000349	time 0.8772 (0.8948)	loss 0.5717 (0.4669)	grad_norm 2.6220 (2.9237)	mem 20675MB
[2025-04-02 19:59:50 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][90/234]	eta 0:02:08 lr 0.000348	time 0.8771 (0.8944)	loss 0.3682 (0.4651)	grad_norm 2.2210 (2.9173)	mem 20675MB
[2025-04-02 19:59:52 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][92/234]	eta 0:02:06 lr 0.000348	time 0.8780 (0.8940)	loss 0.3446 (0.4642)	grad_norm 3.1573 (2.9158)	mem 20675MB
[2025-04-02 19:59:54 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][94/234]	eta 0:02:05 lr 0.000347	time 0.8765 (0.8937)	loss 0.5083 (0.4652)	grad_norm 1.4649 (2.8922)	mem 20675MB
[2025-04-02 19:59:55 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][96/234]	eta 0:02:03 lr 0.000347	time 0.8770 (0.8934)	loss 0.4897 (0.4655)	grad_norm 2.1680 (2.9022)	mem 20675MB
[2025-04-02 19:59:57 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][98/234]	eta 0:02:01 lr 0.000346	time 0.8763 (0.8930)	loss 0.3736 (0.4654)	grad_norm 8.4163 (2.9503)	mem 20675MB
[2025-04-02 19:59:59 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][100/234]	eta 0:01:59 lr 0.000346	time 0.8768 (0.8927)	loss 0.5945 (0.4672)	grad_norm 3.2109 (2.9424)	mem 20675MB
[2025-04-02 20:00:01 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][102/234]	eta 0:01:57 lr 0.000345	time 0.8780 (0.8925)	loss 0.5639 (0.4672)	grad_norm 2.1172 (2.9331)	mem 20675MB
[2025-04-02 20:00:02 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][104/234]	eta 0:01:55 lr 0.000345	time 0.8774 (0.8922)	loss 0.4790 (0.4669)	grad_norm 2.0427 (2.9145)	mem 20675MB
[2025-04-02 20:00:04 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][106/234]	eta 0:01:54 lr 0.000344	time 0.8764 (0.8919)	loss 0.5031 (0.4667)	grad_norm 2.6434 (2.9321)	mem 20675MB
[2025-04-02 20:00:06 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][108/234]	eta 0:01:52 lr 0.000344	time 0.8782 (0.8917)	loss 0.5603 (0.4680)	grad_norm 2.1319 (2.9304)	mem 20675MB
[2025-04-02 20:00:08 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][110/234]	eta 0:01:50 lr 0.000343	time 0.8775 (0.8914)	loss 0.4951 (0.4676)	grad_norm 1.9553 (2.9178)	mem 20675MB
[2025-04-02 20:00:09 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][112/234]	eta 0:01:48 lr 0.000343	time 0.8770 (0.8912)	loss 0.4945 (0.4685)	grad_norm 2.3101 (2.9164)	mem 20675MB
[2025-04-02 20:00:11 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][114/234]	eta 0:01:46 lr 0.000342	time 0.8773 (0.8909)	loss 0.4662 (0.4683)	grad_norm 2.4354 (2.9194)	mem 20675MB
[2025-04-02 20:00:13 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][116/234]	eta 0:01:45 lr 0.000342	time 0.8782 (0.8907)	loss 0.5598 (0.4699)	grad_norm 2.4194 (2.9089)	mem 20675MB
[2025-04-02 20:00:15 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][118/234]	eta 0:01:43 lr 0.000341	time 0.8781 (0.8905)	loss 0.4301 (0.4705)	grad_norm 3.8392 (2.9190)	mem 20675MB
[2025-04-02 20:00:16 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][120/234]	eta 0:01:41 lr 0.000341	time 0.8774 (0.8904)	loss 0.2754 (0.4690)	grad_norm 2.7070 (2.9135)	mem 20675MB
[2025-04-02 20:00:18 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][122/234]	eta 0:01:39 lr 0.000340	time 0.8780 (0.8902)	loss 0.5825 (0.4701)	grad_norm 2.4287 (2.9133)	mem 20675MB
[2025-04-02 20:00:20 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][124/234]	eta 0:01:37 lr 0.000340	time 0.8776 (0.8900)	loss 0.3288 (0.4693)	grad_norm 1.6841 (2.8963)	mem 20675MB
[2025-04-02 20:00:22 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][126/234]	eta 0:01:36 lr 0.000339	time 0.8790 (0.8898)	loss 0.4645 (0.4702)	grad_norm 2.5077 (2.8900)	mem 20675MB
[2025-04-02 20:00:24 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][128/234]	eta 0:01:34 lr 0.000339	time 0.8787 (0.8896)	loss 0.3492 (0.4691)	grad_norm 2.2207 (2.8782)	mem 20675MB
[2025-04-02 20:00:25 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][130/234]	eta 0:01:32 lr 0.000338	time 0.8778 (0.8895)	loss 0.5848 (0.4702)	grad_norm 2.2707 (2.8725)	mem 20675MB
[2025-04-02 20:00:27 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][132/234]	eta 0:01:30 lr 0.000338	time 0.8772 (0.8893)	loss 0.5496 (0.4706)	grad_norm 2.3297 (2.8697)	mem 20675MB
[2025-04-02 20:00:29 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][134/234]	eta 0:01:28 lr 0.000337	time 0.8772 (0.8891)	loss 0.4410 (0.4708)	grad_norm 2.1421 (2.8603)	mem 20675MB
[2025-04-02 20:00:31 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][136/234]	eta 0:01:27 lr 0.000337	time 0.8771 (0.8890)	loss 0.5230 (0.4718)	grad_norm 3.8060 (2.8649)	mem 20675MB
[2025-04-02 20:00:32 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][138/234]	eta 0:01:25 lr 0.000336	time 0.8789 (0.8888)	loss 0.5525 (0.4718)	grad_norm 2.7540 (2.8669)	mem 20675MB
[2025-04-02 20:00:34 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][140/234]	eta 0:01:23 lr 0.000336	time 0.8786 (0.8887)	loss 0.5707 (0.4728)	grad_norm 3.0968 (2.8717)	mem 20675MB
[2025-04-02 20:00:36 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][142/234]	eta 0:01:21 lr 0.000335	time 0.8792 (0.8886)	loss 0.4171 (0.4715)	grad_norm 3.8767 (2.8828)	mem 20675MB
[2025-04-02 20:00:38 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][144/234]	eta 0:01:19 lr 0.000335	time 0.8792 (0.8885)	loss 0.5321 (0.4726)	grad_norm 2.6060 (2.8834)	mem 20675MB
[2025-04-02 20:00:39 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][146/234]	eta 0:01:18 lr 0.000334	time 0.8785 (0.8883)	loss 0.3497 (0.4720)	grad_norm 2.8028 (2.8742)	mem 20675MB
[2025-04-02 20:00:41 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][148/234]	eta 0:01:16 lr 0.000334	time 0.8794 (0.8882)	loss 0.3691 (0.4711)	grad_norm 2.4157 (2.8648)	mem 20675MB
[2025-04-02 20:00:43 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][150/234]	eta 0:01:14 lr 0.000333	time 0.8787 (0.8881)	loss 0.5427 (0.4714)	grad_norm 3.3505 (2.8797)	mem 20675MB
[2025-04-02 20:00:45 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][152/234]	eta 0:01:12 lr 0.000333	time 0.8783 (0.8880)	loss 0.4882 (0.4718)	grad_norm 3.7298 (2.8833)	mem 20675MB
[2025-04-02 20:00:46 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][154/234]	eta 0:01:11 lr 0.000332	time 0.8790 (0.8879)	loss 0.4204 (0.4708)	grad_norm 2.7677 (2.8783)	mem 20675MB
[2025-04-02 20:00:48 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][156/234]	eta 0:01:09 lr 0.000332	time 0.8771 (0.8878)	loss 0.4907 (0.4709)	grad_norm 2.3110 (2.8791)	mem 20675MB
[2025-04-02 20:00:50 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][158/234]	eta 0:01:07 lr 0.000331	time 0.8781 (0.8877)	loss 0.4485 (0.4710)	grad_norm 4.5443 (2.8861)	mem 20675MB
[2025-04-02 20:00:52 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][160/234]	eta 0:01:05 lr 0.000331	time 0.8783 (0.8875)	loss 0.4099 (0.4700)	grad_norm 4.9652 (2.9085)	mem 20675MB
[2025-04-02 20:00:53 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][162/234]	eta 0:01:03 lr 0.000330	time 0.8786 (0.8874)	loss 0.4393 (0.4691)	grad_norm 3.0219 (2.9121)	mem 20675MB
[2025-04-02 20:00:55 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][164/234]	eta 0:01:02 lr 0.000330	time 0.8785 (0.8873)	loss 0.4261 (0.4696)	grad_norm 1.7879 (2.9007)	mem 20675MB
[2025-04-02 20:00:57 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][166/234]	eta 0:01:00 lr 0.000329	time 0.8783 (0.8872)	loss 0.5319 (0.4697)	grad_norm 1.8128 (2.8954)	mem 20675MB
[2025-04-02 20:00:59 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][168/234]	eta 0:00:58 lr 0.000329	time 0.8786 (0.8872)	loss 0.6214 (0.4709)	grad_norm 3.0257 (2.8933)	mem 20675MB
[2025-04-02 20:01:00 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][170/234]	eta 0:00:56 lr 0.000328	time 0.8788 (0.8871)	loss 0.5985 (0.4707)	grad_norm 2.4303 (2.8938)	mem 20675MB
[2025-04-02 20:01:02 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][172/234]	eta 0:00:54 lr 0.000328	time 0.8778 (0.8870)	loss 0.4012 (0.4706)	grad_norm 2.0698 (2.8844)	mem 20675MB
[2025-04-02 20:01:04 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][174/234]	eta 0:00:53 lr 0.000327	time 0.8774 (0.8869)	loss 0.3954 (0.4703)	grad_norm 2.9584 (2.8799)	mem 20675MB
[2025-04-02 20:01:06 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][176/234]	eta 0:00:51 lr 0.000327	time 0.8787 (0.8868)	loss 0.5695 (0.4708)	grad_norm 2.7564 (2.8794)	mem 20675MB
[2025-04-02 20:01:07 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][178/234]	eta 0:00:49 lr 0.000326	time 0.8793 (0.8867)	loss 0.3417 (0.4703)	grad_norm 3.3474 (2.8753)	mem 20675MB
[2025-04-02 20:01:09 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][180/234]	eta 0:00:47 lr 0.000326	time 0.8789 (0.8866)	loss 0.4904 (0.4704)	grad_norm 2.9090 (2.8736)	mem 20675MB
[2025-04-02 20:01:11 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][182/234]	eta 0:00:46 lr 0.000325	time 0.8787 (0.8866)	loss 0.5248 (0.4705)	grad_norm 2.8648 (2.8770)	mem 20675MB
[2025-04-02 20:01:13 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][184/234]	eta 0:00:44 lr 0.000325	time 0.8784 (0.8865)	loss 0.3485 (0.4701)	grad_norm 2.7690 (2.8755)	mem 20675MB
[2025-04-02 20:01:15 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][186/234]	eta 0:00:42 lr 0.000324	time 0.8788 (0.8864)	loss 0.4817 (0.4702)	grad_norm 2.1785 (2.8676)	mem 20675MB
[2025-04-02 20:01:16 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][188/234]	eta 0:00:40 lr 0.000324	time 0.8783 (0.8863)	loss 0.5061 (0.4708)	grad_norm 4.3662 (2.8759)	mem 20675MB
[2025-04-02 20:01:18 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][190/234]	eta 0:00:38 lr 0.000323	time 0.8793 (0.8863)	loss 0.4578 (0.4711)	grad_norm 2.0770 (2.8732)	mem 20675MB
[2025-04-02 20:01:20 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][192/234]	eta 0:00:37 lr 0.000323	time 0.8788 (0.8862)	loss 0.4078 (0.4708)	grad_norm 2.3165 (2.8671)	mem 20675MB
[2025-04-02 20:01:22 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][194/234]	eta 0:00:35 lr 0.000322	time 0.8797 (0.8861)	loss 0.5056 (0.4713)	grad_norm 2.8127 (2.8645)	mem 20675MB
[2025-04-02 20:01:23 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][196/234]	eta 0:00:33 lr 0.000322	time 0.8795 (0.8861)	loss 0.4678 (0.4716)	grad_norm 2.4213 (2.8581)	mem 20675MB
[2025-04-02 20:01:25 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][198/234]	eta 0:00:31 lr 0.000321	time 0.8793 (0.8860)	loss 0.3920 (0.4713)	grad_norm 4.4196 (2.8675)	mem 20675MB
[2025-04-02 20:01:27 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][200/234]	eta 0:00:30 lr 0.000321	time 0.8772 (0.8859)	loss 0.3892 (0.4712)	grad_norm 4.2508 (2.8712)	mem 20675MB
[2025-04-02 20:01:29 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][202/234]	eta 0:00:28 lr 0.000320	time 0.8791 (0.8859)	loss 0.3769 (0.4708)	grad_norm 2.1821 (2.8631)	mem 20675MB
[2025-04-02 20:01:30 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][204/234]	eta 0:00:26 lr 0.000320	time 0.8792 (0.8858)	loss 0.4697 (0.4713)	grad_norm 4.1862 (2.8669)	mem 20675MB
[2025-04-02 20:01:32 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][206/234]	eta 0:00:24 lr 0.000319	time 0.8793 (0.8858)	loss 0.4469 (0.4714)	grad_norm 3.4916 (2.8691)	mem 20675MB
[2025-04-02 20:01:34 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][208/234]	eta 0:00:23 lr 0.000319	time 0.8796 (0.8857)	loss 0.4627 (0.4714)	grad_norm 1.6111 (2.8578)	mem 20675MB
[2025-04-02 20:01:36 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][210/234]	eta 0:00:21 lr 0.000319	time 0.8803 (0.8857)	loss 0.5333 (0.4716)	grad_norm 1.4207 (2.8474)	mem 20675MB
[2025-04-02 20:01:37 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][212/234]	eta 0:00:19 lr 0.000318	time 0.8791 (0.8856)	loss 0.5049 (0.4720)	grad_norm 2.3610 (2.8417)	mem 20675MB
[2025-04-02 20:01:39 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][214/234]	eta 0:00:17 lr 0.000318	time 0.8790 (0.8856)	loss 0.5124 (0.4725)	grad_norm 1.9149 (2.8342)	mem 20675MB
[2025-04-02 20:01:41 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][216/234]	eta 0:00:15 lr 0.000317	time 0.8799 (0.8855)	loss 0.4925 (0.4728)	grad_norm 2.2673 (2.8281)	mem 20675MB
[2025-04-02 20:01:43 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][218/234]	eta 0:00:14 lr 0.000317	time 0.8795 (0.8855)	loss 0.5116 (0.4726)	grad_norm 2.3430 (2.8250)	mem 20675MB
[2025-04-02 20:01:44 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][220/234]	eta 0:00:12 lr 0.000316	time 0.8791 (0.8854)	loss 0.5499 (0.4736)	grad_norm 2.8065 (2.8223)	mem 20675MB
[2025-04-02 20:01:46 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][222/234]	eta 0:00:10 lr 0.000316	time 0.8795 (0.8854)	loss 0.4553 (0.4734)	grad_norm 2.2951 (2.8196)	mem 20675MB
[2025-04-02 20:01:48 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][224/234]	eta 0:00:08 lr 0.000315	time 0.8786 (0.8853)	loss 0.4811 (0.4738)	grad_norm 1.8704 (2.8152)	mem 20675MB
[2025-04-02 20:01:50 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][226/234]	eta 0:00:07 lr 0.000315	time 0.8785 (0.8853)	loss 0.3828 (0.4734)	grad_norm 2.1415 (2.8110)	mem 20675MB
[2025-04-02 20:01:51 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][228/234]	eta 0:00:05 lr 0.000314	time 0.8780 (0.8852)	loss 0.5301 (0.4739)	grad_norm 2.8828 (2.8106)	mem 20675MB
[2025-04-02 20:01:53 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][230/234]	eta 0:00:03 lr 0.000314	time 0.8773 (0.8852)	loss 0.5900 (0.4737)	grad_norm 2.5583 (2.8148)	mem 20675MB
[2025-04-02 20:01:55 simmim_finetune] (main_finetune.py 252): INFO Train: [19/30][232/234]	eta 0:00:01 lr 0.000313	time 0.8773 (0.8851)	loss 0.5403 (0.4731)	grad_norm 2.1027 (2.8096)	mem 20675MB
[2025-04-02 20:01:56 simmim_finetune] (main_finetune.py 260): INFO EPOCH 19 training takes 0:03:27
[2025-04-02 20:01:57 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.123 (1.123)	Loss 0.7836 (0.7836)	Acc@1 61.719 (61.719)	Mem 20675MB
[2025-04-02 20:01:57 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 69.613
[2025-04-02 20:01:57 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 69.6%
[2025-04-02 20:01:57 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:01:57 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [1.3437240697199356e-06, 1.3437240697199356e-06, 1.966332350512946e-06, 1.966332350512946e-06, 2.924191244040655e-06, 2.924191244040655e-06, 4.397820311006361e-06, 4.397820311006361e-06, 6.664941952492062e-06, 6.664941952492062e-06, 1.0152821400931602e-05, 1.0152821400931602e-05, 1.5518789783146278e-05, 1.5518789783146278e-05, 2.3774125755784234e-05, 2.3774125755784234e-05, 3.647464263676572e-05, 3.647464263676572e-05, 5.601389937673723e-05, 5.601389937673723e-05, 8.607429436130878e-05, 8.607429436130878e-05, 0.00013232105587603424, 0.00013232105587603424, 0.00020346991974484264, 0.00020346991974484264, 0.00031292971031224016, 0.00031292971031224016]
[2025-04-02 20:02:00 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][0/234]	eta 0:08:43 lr 0.000313	time 2.2382 (2.2382)	loss 0.4867 (0.4867)	grad_norm 2.4941 (2.4941)	mem 20675MB
[2025-04-02 20:02:01 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][2/234]	eta 0:05:09 lr 0.000312	time 0.8781 (1.3322)	loss 0.5136 (0.4683)	grad_norm 1.8140 (2.4986)	mem 20675MB
[2025-04-02 20:02:03 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][4/234]	eta 0:04:24 lr 0.000312	time 0.8778 (1.1511)	loss 0.3914 (0.4671)	grad_norm 3.6342 (2.7727)	mem 20675MB
[2025-04-02 20:02:05 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][6/234]	eta 0:04:04 lr 0.000311	time 0.8777 (1.0731)	loss 0.5858 (0.4832)	grad_norm 2.7471 (2.6720)	mem 20675MB
[2025-04-02 20:02:07 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][8/234]	eta 0:03:52 lr 0.000311	time 0.8783 (1.0301)	loss 0.4441 (0.4846)	grad_norm 2.5560 (2.6338)	mem 20675MB
[2025-04-02 20:02:08 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][10/234]	eta 0:03:44 lr 0.000310	time 0.8780 (1.0027)	loss 0.5041 (0.4845)	grad_norm 3.0121 (2.7056)	mem 20675MB
[2025-04-02 20:02:10 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][12/234]	eta 0:03:38 lr 0.000310	time 0.8773 (0.9836)	loss 0.5375 (0.4955)	grad_norm 2.7308 (2.6832)	mem 20675MB
[2025-04-02 20:02:12 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][14/234]	eta 0:03:33 lr 0.000309	time 0.8784 (0.9697)	loss 0.4341 (0.4862)	grad_norm 3.5067 (2.7057)	mem 20675MB
[2025-04-02 20:02:14 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][16/234]	eta 0:03:29 lr 0.000309	time 0.8780 (0.9590)	loss 0.5647 (0.4912)	grad_norm 1.8104 (2.6938)	mem 20675MB
[2025-04-02 20:02:15 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][18/234]	eta 0:03:25 lr 0.000308	time 0.8782 (0.9506)	loss 0.5091 (0.4970)	grad_norm 2.5024 (2.7122)	mem 20675MB
[2025-04-02 20:02:17 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][20/234]	eta 0:03:21 lr 0.000308	time 0.8781 (0.9437)	loss 0.5451 (0.4964)	grad_norm 2.9838 (2.7157)	mem 20675MB
[2025-04-02 20:02:19 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][22/234]	eta 0:03:18 lr 0.000307	time 0.8781 (0.9381)	loss 0.3822 (0.4941)	grad_norm 2.5536 (2.6715)	mem 20675MB
[2025-04-02 20:02:21 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][24/234]	eta 0:03:16 lr 0.000307	time 0.8785 (0.9334)	loss 0.4876 (0.4950)	grad_norm 3.7822 (2.7053)	mem 20675MB
[2025-04-02 20:02:22 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][26/234]	eta 0:03:13 lr 0.000306	time 0.8777 (0.9293)	loss 0.5674 (0.4978)	grad_norm 2.3998 (2.6764)	mem 20675MB
[2025-04-02 20:02:24 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][28/234]	eta 0:03:10 lr 0.000306	time 0.8780 (0.9259)	loss 0.3755 (0.4915)	grad_norm 3.2587 (2.6863)	mem 20675MB
[2025-04-02 20:02:26 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][30/234]	eta 0:03:08 lr 0.000305	time 0.8789 (0.9229)	loss 0.4073 (0.4841)	grad_norm 1.7016 (2.6570)	mem 20675MB
[2025-04-02 20:02:28 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][32/234]	eta 0:03:05 lr 0.000305	time 0.8799 (0.9204)	loss 0.4783 (0.4816)	grad_norm 1.9214 (2.6556)	mem 20675MB
[2025-04-02 20:02:30 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][34/234]	eta 0:03:03 lr 0.000304	time 0.8795 (0.9181)	loss 0.5016 (0.4814)	grad_norm 4.0021 (2.6964)	mem 20675MB
[2025-04-02 20:02:31 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][36/234]	eta 0:03:01 lr 0.000304	time 0.8785 (0.9160)	loss 0.4639 (0.4821)	grad_norm 3.6347 (2.7229)	mem 20675MB
[2025-04-02 20:02:33 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][38/234]	eta 0:02:59 lr 0.000304	time 0.8775 (0.9141)	loss 0.2947 (0.4768)	grad_norm 4.0858 (2.7734)	mem 20675MB
[2025-04-02 20:02:35 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][40/234]	eta 0:02:57 lr 0.000303	time 0.8788 (0.9124)	loss 0.5441 (0.4794)	grad_norm 3.0172 (2.7771)	mem 20675MB
[2025-04-02 20:02:37 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][42/234]	eta 0:02:54 lr 0.000303	time 0.8780 (0.9109)	loss 0.4629 (0.4812)	grad_norm 3.5321 (2.8159)	mem 20675MB
[2025-04-02 20:02:38 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][44/234]	eta 0:02:52 lr 0.000302	time 0.8867 (0.9097)	loss 0.4453 (0.4787)	grad_norm 3.0716 (2.8424)	mem 20675MB
[2025-04-02 20:02:40 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][46/234]	eta 0:02:50 lr 0.000302	time 0.8804 (0.9086)	loss 0.5376 (0.4827)	grad_norm 2.3726 (2.8305)	mem 20675MB
[2025-04-02 20:02:42 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][48/234]	eta 0:02:48 lr 0.000301	time 0.8860 (0.9076)	loss 0.3815 (0.4782)	grad_norm 3.7643 (2.8530)	mem 20675MB
[2025-04-02 20:02:44 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][50/234]	eta 0:02:46 lr 0.000301	time 0.8912 (0.9068)	loss 0.3457 (0.4750)	grad_norm 5.2020 (2.9248)	mem 20675MB
[2025-04-02 20:02:45 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][52/234]	eta 0:02:44 lr 0.000300	time 0.8786 (0.9058)	loss 0.4877 (0.4777)	grad_norm 3.5306 (2.9323)	mem 20675MB
[2025-04-02 20:02:47 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][54/234]	eta 0:02:42 lr 0.000300	time 0.8801 (0.9049)	loss 0.5314 (0.4781)	grad_norm 2.1195 (2.9119)	mem 20675MB
[2025-04-02 20:02:49 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][56/234]	eta 0:02:40 lr 0.000299	time 0.8793 (0.9041)	loss 0.5514 (0.4802)	grad_norm 2.2785 (2.8750)	mem 20675MB
[2025-04-02 20:02:51 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][58/234]	eta 0:02:38 lr 0.000299	time 0.8796 (0.9033)	loss 0.3529 (0.4785)	grad_norm 2.9806 (2.8564)	mem 20675MB
[2025-04-02 20:02:52 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][60/234]	eta 0:02:37 lr 0.000298	time 0.8785 (0.9026)	loss 0.5230 (0.4780)	grad_norm 2.2777 (2.8289)	mem 20675MB
[2025-04-02 20:02:54 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][62/234]	eta 0:02:35 lr 0.000298	time 0.8792 (0.9019)	loss 0.4945 (0.4785)	grad_norm 2.2961 (2.8043)	mem 20675MB
[2025-04-02 20:02:56 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][64/234]	eta 0:02:33 lr 0.000297	time 0.8790 (0.9012)	loss 0.3626 (0.4749)	grad_norm 2.6641 (2.7998)	mem 20675MB
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[2025-04-02 20:02:59 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][68/234]	eta 0:02:29 lr 0.000296	time 0.8817 (0.9001)	loss 0.5156 (0.4758)	grad_norm 3.6737 (2.8123)	mem 20675MB
[2025-04-02 20:03:01 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][70/234]	eta 0:02:27 lr 0.000296	time 0.8783 (0.8995)	loss 0.4305 (0.4756)	grad_norm 3.0018 (2.8152)	mem 20675MB
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[2025-04-02 20:03:15 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][86/234]	eta 0:02:12 lr 0.000292	time 0.8858 (0.8964)	loss 0.2959 (0.4706)	grad_norm 3.3336 (2.9223)	mem 20675MB
[2025-04-02 20:03:17 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][88/234]	eta 0:02:10 lr 0.000292	time 0.8805 (0.8961)	loss 0.5083 (0.4705)	grad_norm 2.2066 (2.9045)	mem 20675MB
[2025-04-02 20:03:19 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][90/234]	eta 0:02:08 lr 0.000291	time 0.8812 (0.8958)	loss 0.5280 (0.4703)	grad_norm 2.9918 (2.9101)	mem 20675MB
[2025-04-02 20:03:21 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][92/234]	eta 0:02:07 lr 0.000291	time 0.8792 (0.8956)	loss 0.3161 (0.4694)	grad_norm 3.3803 (2.9213)	mem 20675MB
[2025-04-02 20:03:22 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][94/234]	eta 0:02:05 lr 0.000290	time 0.8796 (0.8954)	loss 0.3898 (0.4688)	grad_norm 2.0051 (2.9130)	mem 20675MB
[2025-04-02 20:03:24 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][96/234]	eta 0:02:03 lr 0.000290	time 0.8795 (0.8951)	loss 0.5438 (0.4702)	grad_norm 2.4902 (2.9064)	mem 20675MB
[2025-04-02 20:03:26 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][98/234]	eta 0:02:01 lr 0.000289	time 0.8809 (0.8948)	loss 0.4938 (0.4702)	grad_norm 3.5156 (2.9104)	mem 20675MB
[2025-04-02 20:03:28 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][100/234]	eta 0:01:59 lr 0.000289	time 0.8872 (0.8946)	loss 0.5111 (0.4712)	grad_norm 3.8610 (2.9236)	mem 20675MB
[2025-04-02 20:03:29 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][102/234]	eta 0:01:58 lr 0.000288	time 0.8815 (0.8944)	loss 0.3298 (0.4706)	grad_norm 3.2421 (2.9204)	mem 20675MB
[2025-04-02 20:03:31 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][104/234]	eta 0:01:56 lr 0.000288	time 0.8806 (0.8941)	loss 0.3656 (0.4695)	grad_norm 3.6257 (2.9130)	mem 20675MB
[2025-04-02 20:03:33 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][106/234]	eta 0:01:54 lr 0.000287	time 0.8806 (0.8942)	loss 0.5441 (0.4706)	grad_norm 1.7619 (2.9019)	mem 20675MB
[2025-04-02 20:03:35 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][108/234]	eta 0:01:52 lr 0.000287	time 0.8899 (0.8941)	loss 0.4743 (0.4708)	grad_norm 3.1403 (2.9079)	mem 20675MB
[2025-04-02 20:03:37 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][110/234]	eta 0:01:50 lr 0.000286	time 0.8799 (0.8938)	loss 0.5659 (0.4721)	grad_norm 2.5378 (2.9046)	mem 20675MB
[2025-04-02 20:03:38 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][112/234]	eta 0:01:49 lr 0.000286	time 0.8787 (0.8936)	loss 0.5150 (0.4709)	grad_norm 3.1097 (2.9137)	mem 20675MB
[2025-04-02 20:03:40 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][114/234]	eta 0:01:47 lr 0.000285	time 0.8814 (0.8935)	loss 0.5295 (0.4718)	grad_norm 2.7700 (2.9176)	mem 20675MB
[2025-04-02 20:03:42 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][116/234]	eta 0:01:45 lr 0.000285	time 0.8792 (0.8932)	loss 0.4642 (0.4707)	grad_norm 1.7256 (2.9175)	mem 20675MB
[2025-04-02 20:03:44 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][118/234]	eta 0:01:43 lr 0.000285	time 0.8791 (0.8933)	loss 0.4717 (0.4709)	grad_norm 2.8830 (2.9135)	mem 20675MB
[2025-04-02 20:03:45 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][120/234]	eta 0:01:41 lr 0.000284	time 0.8805 (0.8930)	loss 0.5271 (0.4707)	grad_norm 3.1068 (2.9176)	mem 20675MB
[2025-04-02 20:03:47 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][122/234]	eta 0:01:40 lr 0.000284	time 0.8855 (0.8930)	loss 0.5417 (0.4712)	grad_norm 2.7734 (2.9067)	mem 20675MB
[2025-04-02 20:03:49 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][124/234]	eta 0:01:38 lr 0.000283	time 0.8802 (0.8928)	loss 0.5448 (0.4719)	grad_norm 2.1696 (2.9010)	mem 20675MB
[2025-04-02 20:03:51 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][126/234]	eta 0:01:36 lr 0.000283	time 0.8919 (0.8927)	loss 0.4147 (0.4712)	grad_norm 2.7329 (2.8983)	mem 20675MB
[2025-04-02 20:03:53 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][128/234]	eta 0:01:34 lr 0.000282	time 0.9029 (0.8927)	loss 0.4812 (0.4719)	grad_norm 2.9560 (2.8927)	mem 20675MB
[2025-04-02 20:03:54 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][130/234]	eta 0:01:32 lr 0.000282	time 0.9049 (0.8927)	loss 0.3932 (0.4711)	grad_norm 2.7606 (2.8925)	mem 20675MB
[2025-04-02 20:03:56 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][132/234]	eta 0:01:31 lr 0.000281	time 0.8817 (0.8926)	loss 0.4684 (0.4710)	grad_norm 2.0107 (2.8826)	mem 20675MB
[2025-04-02 20:03:58 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][134/234]	eta 0:01:29 lr 0.000281	time 0.8811 (0.8925)	loss 0.5075 (0.4711)	grad_norm 1.8673 (2.8718)	mem 20675MB
[2025-04-02 20:04:00 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][136/234]	eta 0:01:27 lr 0.000280	time 0.8894 (0.8925)	loss 0.5269 (0.4716)	grad_norm 2.3655 (2.8561)	mem 20675MB
[2025-04-02 20:04:01 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][138/234]	eta 0:01:25 lr 0.000280	time 0.8797 (0.8924)	loss 0.3135 (0.4707)	grad_norm 2.8012 (2.8516)	mem 20675MB
[2025-04-02 20:04:03 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][140/234]	eta 0:01:23 lr 0.000279	time 0.8802 (0.8923)	loss 0.3290 (0.4694)	grad_norm 2.6534 (2.8472)	mem 20675MB
[2025-04-02 20:04:05 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][142/234]	eta 0:01:22 lr 0.000279	time 0.8790 (0.8921)	loss 0.4019 (0.4679)	grad_norm 4.4931 (2.8839)	mem 20675MB
[2025-04-02 20:04:07 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][144/234]	eta 0:01:20 lr 0.000278	time 0.8816 (0.8919)	loss 0.4983 (0.4677)	grad_norm 3.4893 (2.8954)	mem 20675MB
[2025-04-02 20:04:08 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][146/234]	eta 0:01:18 lr 0.000278	time 0.8799 (0.8918)	loss 0.4125 (0.4683)	grad_norm 5.9006 (2.9213)	mem 20675MB
[2025-04-02 20:04:10 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][148/234]	eta 0:01:16 lr 0.000278	time 0.8796 (0.8916)	loss 0.5511 (0.4685)	grad_norm 3.3735 (2.9343)	mem 20675MB
[2025-04-02 20:04:12 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][150/234]	eta 0:01:14 lr 0.000277	time 0.8780 (0.8917)	loss 0.4897 (0.4685)	grad_norm 2.7297 (2.9396)	mem 20675MB
[2025-04-02 20:04:14 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][152/234]	eta 0:01:13 lr 0.000277	time 0.8789 (0.8915)	loss 0.5489 (0.4696)	grad_norm 2.7022 (2.9361)	mem 20675MB
[2025-04-02 20:04:16 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][154/234]	eta 0:01:11 lr 0.000276	time 0.8795 (0.8914)	loss 0.4583 (0.4695)	grad_norm 3.1062 (2.9344)	mem 20675MB
[2025-04-02 20:04:17 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][156/234]	eta 0:01:09 lr 0.000276	time 0.8857 (0.8914)	loss 0.5434 (0.4704)	grad_norm 1.9540 (2.9263)	mem 20675MB
[2025-04-02 20:04:19 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][158/234]	eta 0:01:07 lr 0.000275	time 0.8798 (0.8913)	loss 0.3622 (0.4697)	grad_norm 2.8183 (2.9225)	mem 20675MB
[2025-04-02 20:04:21 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][160/234]	eta 0:01:05 lr 0.000275	time 0.8822 (0.8912)	loss 0.4914 (0.4691)	grad_norm 2.2730 (2.9194)	mem 20675MB
[2025-04-02 20:04:23 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][162/234]	eta 0:01:04 lr 0.000274	time 0.8821 (0.8911)	loss 0.5215 (0.4699)	grad_norm 2.0534 (2.9066)	mem 20675MB
[2025-04-02 20:04:24 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][164/234]	eta 0:01:02 lr 0.000274	time 0.8842 (0.8910)	loss 0.5595 (0.4708)	grad_norm 2.3873 (2.9031)	mem 20675MB
[2025-04-02 20:04:26 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][166/234]	eta 0:01:00 lr 0.000273	time 0.8788 (0.8910)	loss 0.4624 (0.4699)	grad_norm 2.2921 (2.8966)	mem 20675MB
[2025-04-02 20:04:28 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][168/234]	eta 0:00:58 lr 0.000273	time 0.8850 (0.8909)	loss 0.3032 (0.4688)	grad_norm 3.0356 (2.9085)	mem 20675MB
[2025-04-02 20:04:30 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][170/234]	eta 0:00:57 lr 0.000272	time 0.8862 (0.8908)	loss 0.5077 (0.4691)	grad_norm 2.8229 (2.9052)	mem 20675MB
[2025-04-02 20:04:31 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][172/234]	eta 0:00:55 lr 0.000272	time 0.8823 (0.8907)	loss 0.5228 (0.4692)	grad_norm 3.0423 (2.9090)	mem 20675MB
[2025-04-02 20:04:33 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][174/234]	eta 0:00:53 lr 0.000272	time 0.8806 (0.8907)	loss 0.4615 (0.4694)	grad_norm 2.9057 (2.9038)	mem 20675MB
[2025-04-02 20:04:35 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][176/234]	eta 0:00:51 lr 0.000271	time 0.8790 (0.8906)	loss 0.4736 (0.4688)	grad_norm 4.0502 (2.9188)	mem 20675MB
[2025-04-02 20:04:37 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][178/234]	eta 0:00:49 lr 0.000271	time 0.8791 (0.8905)	loss 0.3884 (0.4684)	grad_norm 3.7964 (2.9241)	mem 20675MB
[2025-04-02 20:04:39 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][180/234]	eta 0:00:48 lr 0.000270	time 0.8777 (0.8904)	loss 0.4765 (0.4676)	grad_norm 3.3318 (2.9280)	mem 20675MB
[2025-04-02 20:04:40 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][182/234]	eta 0:00:46 lr 0.000270	time 0.8794 (0.8903)	loss 0.4060 (0.4665)	grad_norm 2.4949 (2.9380)	mem 20675MB
[2025-04-02 20:04:42 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][184/234]	eta 0:00:44 lr 0.000269	time 0.8839 (0.8902)	loss 0.4951 (0.4668)	grad_norm 2.7634 (2.9396)	mem 20675MB
[2025-04-02 20:04:44 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][186/234]	eta 0:00:42 lr 0.000269	time 0.8818 (0.8901)	loss 0.3731 (0.4658)	grad_norm 4.4310 (2.9471)	mem 20675MB
[2025-04-02 20:04:46 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][188/234]	eta 0:00:40 lr 0.000268	time 0.8813 (0.8900)	loss 0.5427 (0.4660)	grad_norm 2.7797 (2.9456)	mem 20675MB
[2025-04-02 20:04:47 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][190/234]	eta 0:00:39 lr 0.000268	time 0.8794 (0.8899)	loss 0.3315 (0.4653)	grad_norm 3.3426 (2.9453)	mem 20675MB
[2025-04-02 20:04:49 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][192/234]	eta 0:00:37 lr 0.000267	time 0.8794 (0.8899)	loss 0.5039 (0.4650)	grad_norm 2.1968 (2.9408)	mem 20675MB
[2025-04-02 20:04:51 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][194/234]	eta 0:00:35 lr 0.000267	time 0.8791 (0.8898)	loss 0.4615 (0.4651)	grad_norm 3.8389 (2.9419)	mem 20675MB
[2025-04-02 20:04:53 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][196/234]	eta 0:00:33 lr 0.000266	time 0.8799 (0.8897)	loss 0.4799 (0.4652)	grad_norm 2.4737 (2.9433)	mem 20675MB
[2025-04-02 20:04:54 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][198/234]	eta 0:00:32 lr 0.000266	time 0.8894 (0.8897)	loss 0.6001 (0.4655)	grad_norm 2.0954 (2.9370)	mem 20675MB
[2025-04-02 20:04:56 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][200/234]	eta 0:00:30 lr 0.000266	time 0.8797 (0.8896)	loss 0.5140 (0.4654)	grad_norm 3.6036 (2.9507)	mem 20675MB
[2025-04-02 20:04:58 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][202/234]	eta 0:00:28 lr 0.000265	time 0.8989 (0.8896)	loss 0.4752 (0.4663)	grad_norm 2.4248 (2.9528)	mem 20675MB
[2025-04-02 20:05:00 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][204/234]	eta 0:00:26 lr 0.000265	time 0.8789 (0.8895)	loss 0.4183 (0.4654)	grad_norm 1.9335 (2.9481)	mem 20675MB
[2025-04-02 20:05:01 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][206/234]	eta 0:00:24 lr 0.000264	time 0.8786 (0.8895)	loss 0.5252 (0.4659)	grad_norm 2.9675 (2.9438)	mem 20675MB
[2025-04-02 20:05:03 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][208/234]	eta 0:00:23 lr 0.000264	time 0.8788 (0.8895)	loss 0.4027 (0.4653)	grad_norm 2.4644 (2.9418)	mem 20675MB
[2025-04-02 20:05:05 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][210/234]	eta 0:00:21 lr 0.000263	time 0.8777 (0.8894)	loss 0.4895 (0.4654)	grad_norm 1.6810 (2.9330)	mem 20675MB
[2025-04-02 20:05:07 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][212/234]	eta 0:00:19 lr 0.000263	time 0.8793 (0.8893)	loss 0.4922 (0.4658)	grad_norm 1.9744 (2.9224)	mem 20675MB
[2025-04-02 20:05:09 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][214/234]	eta 0:00:17 lr 0.000262	time 0.8773 (0.8893)	loss 0.3445 (0.4650)	grad_norm 2.5587 (2.9150)	mem 20675MB
[2025-04-02 20:05:10 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][216/234]	eta 0:00:16 lr 0.000262	time 0.8789 (0.8892)	loss 0.3315 (0.4639)	grad_norm 3.8699 (2.9217)	mem 20675MB
[2025-04-02 20:05:12 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][218/234]	eta 0:00:14 lr 0.000261	time 0.8795 (0.8891)	loss 0.4878 (0.4634)	grad_norm 3.3539 (2.9253)	mem 20675MB
[2025-04-02 20:05:14 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][220/234]	eta 0:00:12 lr 0.000261	time 0.8827 (0.8890)	loss 0.3545 (0.4636)	grad_norm 5.5938 (2.9376)	mem 20675MB
[2025-04-02 20:05:16 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][222/234]	eta 0:00:10 lr 0.000261	time 0.8852 (0.8890)	loss 0.4722 (0.4642)	grad_norm 3.2371 (2.9388)	mem 20675MB
[2025-04-02 20:05:17 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][224/234]	eta 0:00:08 lr 0.000260	time 0.8780 (0.8889)	loss 0.4707 (0.4641)	grad_norm 3.3292 (2.9424)	mem 20675MB
[2025-04-02 20:05:19 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][226/234]	eta 0:00:07 lr 0.000260	time 0.8778 (0.8889)	loss 0.6161 (0.4646)	grad_norm 3.9443 (2.9423)	mem 20675MB
[2025-04-02 20:05:21 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][228/234]	eta 0:00:05 lr 0.000259	time 0.8763 (0.8888)	loss 0.3449 (0.4642)	grad_norm 4.1745 (2.9470)	mem 20675MB
[2025-04-02 20:05:23 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][230/234]	eta 0:00:03 lr 0.000259	time 0.8778 (0.8887)	loss 0.3432 (0.4638)	grad_norm 3.8764 (2.9535)	mem 20675MB
[2025-04-02 20:05:24 simmim_finetune] (main_finetune.py 252): INFO Train: [20/30][232/234]	eta 0:00:01 lr 0.000258	time 0.8769 (0.8886)	loss 0.4957 (0.4640)	grad_norm 2.4452 (2.9508)	mem 20675MB
[2025-04-02 20:05:26 simmim_finetune] (main_finetune.py 260): INFO EPOCH 20 training takes 0:03:28
[2025-04-02 20:05:26 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt20.pth saving......
[2025-04-02 20:05:30 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt20.pth saved !!!
[2025-04-02 20:05:33 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 2.131 (2.131)	Loss 0.6854 (0.6854)	Acc@1 65.625 (65.625)	Mem 20675MB
[2025-04-02 20:05:33 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 72.376
[2025-04-02 20:05:33 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 72.4%
[2025-04-02 20:05:33 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:05:33 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [1.1517913866525726e-06, 1.1517913866525726e-06, 1.665140960299127e-06, 1.665140960299127e-06, 2.4549095351399806e-06, 2.4549095351399806e-06, 3.669938111818216e-06, 3.669938111818216e-06, 5.5392128451693475e-06, 5.5392128451693475e-06, 8.415020127248011e-06, 8.415020127248011e-06, 1.2839339022753647e-05, 1.2839339022753647e-05, 1.9645983477377698e-05, 1.9645983477377698e-05, 3.0117744176799322e-05, 3.0117744176799322e-05, 4.6228145252832594e-05, 4.6228145252832594e-05, 7.101337767749916e-05, 7.101337767749916e-05, 0.00010914450448467848, 0.00010914450448467848, 0.00016780777649572358, 0.00016780777649572358, 0.00025805896420502374, 0.00025805896420502374]
[2025-04-02 20:05:36 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][0/234]	eta 0:13:17 lr 0.000258	time 3.4071 (3.4071)	loss 0.5740 (0.5740)	grad_norm 3.7900 (3.7900)	mem 20675MB
[2025-04-02 20:05:38 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][2/234]	eta 0:06:42 lr 0.000257	time 0.9203 (1.7361)	loss 0.5178 (0.5409)	grad_norm 1.8130 (2.9824)	mem 20675MB
[2025-04-02 20:05:40 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][4/234]	eta 0:05:20 lr 0.000257	time 0.8779 (1.3933)	loss 0.4427 (0.4971)	grad_norm 2.7057 (2.8528)	mem 20675MB
[2025-04-02 20:05:42 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][6/234]	eta 0:04:44 lr 0.000256	time 0.8947 (1.2499)	loss 0.4053 (0.4805)	grad_norm 2.4624 (2.6804)	mem 20675MB
[2025-04-02 20:05:44 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][8/234]	eta 0:04:23 lr 0.000256	time 0.8778 (1.1676)	loss 0.3985 (0.4745)	grad_norm 3.1960 (2.7320)	mem 20675MB
[2025-04-02 20:05:45 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][10/234]	eta 0:04:10 lr 0.000256	time 0.8807 (1.1162)	loss 0.4473 (0.4666)	grad_norm 2.5741 (2.6781)	mem 20675MB
[2025-04-02 20:05:47 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][12/234]	eta 0:03:59 lr 0.000255	time 0.8771 (1.0797)	loss 0.4595 (0.4660)	grad_norm 1.8313 (2.7821)	mem 20675MB
[2025-04-02 20:05:49 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][14/234]	eta 0:03:51 lr 0.000255	time 0.8970 (1.0540)	loss 0.5269 (0.4612)	grad_norm 2.8970 (2.9440)	mem 20675MB
[2025-04-02 20:05:51 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][16/234]	eta 0:03:45 lr 0.000254	time 0.8843 (1.0337)	loss 0.5378 (0.4725)	grad_norm 1.9119 (2.8693)	mem 20675MB
[2025-04-02 20:05:52 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][18/234]	eta 0:03:39 lr 0.000254	time 0.8781 (1.0175)	loss 0.5221 (0.4787)	grad_norm 2.2708 (2.7971)	mem 20675MB
[2025-04-02 20:05:54 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][20/234]	eta 0:03:35 lr 0.000253	time 0.8788 (1.0052)	loss 0.5144 (0.4792)	grad_norm 3.0708 (2.7874)	mem 20675MB
[2025-04-02 20:05:56 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][22/234]	eta 0:03:30 lr 0.000253	time 0.8772 (0.9944)	loss 0.4093 (0.4798)	grad_norm 3.0344 (2.7718)	mem 20675MB
[2025-04-02 20:05:58 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][24/234]	eta 0:03:26 lr 0.000252	time 0.8785 (0.9856)	loss 0.4978 (0.4792)	grad_norm 3.0335 (2.7527)	mem 20675MB
[2025-04-02 20:05:59 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][26/234]	eta 0:03:23 lr 0.000252	time 0.8771 (0.9777)	loss 0.5145 (0.4762)	grad_norm 2.5375 (2.7388)	mem 20675MB
[2025-04-02 20:06:01 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][28/234]	eta 0:03:20 lr 0.000252	time 0.8839 (0.9711)	loss 0.4804 (0.4760)	grad_norm 2.9944 (2.7286)	mem 20675MB
[2025-04-02 20:06:03 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][30/234]	eta 0:03:16 lr 0.000251	time 0.8772 (0.9651)	loss 0.5490 (0.4787)	grad_norm 3.5578 (2.7714)	mem 20675MB
[2025-04-02 20:06:05 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][32/234]	eta 0:03:13 lr 0.000251	time 0.8838 (0.9601)	loss 0.3006 (0.4763)	grad_norm 4.1794 (2.8337)	mem 20675MB
[2025-04-02 20:06:06 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][34/234]	eta 0:03:11 lr 0.000250	time 0.8770 (0.9556)	loss 0.3154 (0.4748)	grad_norm 5.0976 (2.8873)	mem 20675MB
[2025-04-02 20:06:08 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][36/234]	eta 0:03:08 lr 0.000250	time 0.8768 (0.9514)	loss 0.4300 (0.4751)	grad_norm 2.5463 (2.8742)	mem 20675MB
[2025-04-02 20:06:10 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][38/234]	eta 0:03:05 lr 0.000249	time 0.8770 (0.9477)	loss 0.5650 (0.4792)	grad_norm 3.4554 (2.9117)	mem 20675MB
[2025-04-02 20:06:12 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][40/234]	eta 0:03:03 lr 0.000249	time 0.8779 (0.9443)	loss 0.3507 (0.4779)	grad_norm 4.1503 (2.9683)	mem 20675MB
[2025-04-02 20:06:13 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][42/234]	eta 0:03:00 lr 0.000248	time 0.8767 (0.9412)	loss 0.4565 (0.4762)	grad_norm 3.6264 (3.0153)	mem 20675MB
[2025-04-02 20:06:15 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][44/234]	eta 0:02:58 lr 0.000248	time 0.8774 (0.9386)	loss 0.5072 (0.4786)	grad_norm 3.5282 (3.0279)	mem 20675MB
[2025-04-02 20:06:17 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][46/234]	eta 0:02:56 lr 0.000248	time 0.8835 (0.9362)	loss 0.4213 (0.4771)	grad_norm 5.2687 (3.0913)	mem 20675MB
[2025-04-02 20:06:19 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][48/234]	eta 0:02:53 lr 0.000247	time 0.8833 (0.9340)	loss 0.3870 (0.4721)	grad_norm 2.8838 (3.1331)	mem 20675MB
[2025-04-02 20:06:21 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][50/234]	eta 0:02:51 lr 0.000247	time 0.8808 (0.9320)	loss 0.4497 (0.4715)	grad_norm 2.9901 (3.1187)	mem 20675MB
[2025-04-02 20:06:22 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][52/234]	eta 0:02:49 lr 0.000246	time 0.8780 (0.9301)	loss 0.3541 (0.4694)	grad_norm 2.0062 (3.0784)	mem 20675MB
[2025-04-02 20:06:24 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][54/234]	eta 0:02:47 lr 0.000246	time 0.8905 (0.9285)	loss 0.4981 (0.4678)	grad_norm 2.6802 (3.0568)	mem 20675MB
[2025-04-02 20:06:26 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][56/234]	eta 0:02:45 lr 0.000245	time 0.8802 (0.9270)	loss 0.5319 (0.4680)	grad_norm 1.7596 (3.0421)	mem 20675MB
[2025-04-02 20:06:28 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][58/234]	eta 0:02:42 lr 0.000245	time 0.8802 (0.9254)	loss 0.5478 (0.4712)	grad_norm 2.7781 (3.0622)	mem 20675MB
[2025-04-02 20:06:29 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][60/234]	eta 0:02:40 lr 0.000244	time 0.8775 (0.9238)	loss 0.4985 (0.4729)	grad_norm 2.9722 (3.0533)	mem 20675MB
[2025-04-02 20:06:31 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][62/234]	eta 0:02:38 lr 0.000244	time 0.8808 (0.9224)	loss 0.5448 (0.4742)	grad_norm 2.3366 (3.0320)	mem 20675MB
[2025-04-02 20:06:33 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][64/234]	eta 0:02:36 lr 0.000244	time 0.8907 (0.9214)	loss 0.4884 (0.4745)	grad_norm 3.0010 (3.0168)	mem 20675MB
[2025-04-02 20:06:35 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][66/234]	eta 0:02:34 lr 0.000243	time 0.8774 (0.9203)	loss 0.5114 (0.4745)	grad_norm 2.6394 (3.0005)	mem 20675MB
[2025-04-02 20:06:36 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][68/234]	eta 0:02:32 lr 0.000243	time 0.8770 (0.9192)	loss 0.5518 (0.4756)	grad_norm 2.7413 (2.9909)	mem 20675MB
[2025-04-02 20:06:38 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][70/234]	eta 0:02:30 lr 0.000242	time 0.8776 (0.9182)	loss 0.4926 (0.4766)	grad_norm 2.3717 (2.9679)	mem 20675MB
[2025-04-02 20:06:40 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][72/234]	eta 0:02:28 lr 0.000242	time 0.8771 (0.9171)	loss 0.5570 (0.4768)	grad_norm 2.7754 (2.9599)	mem 20675MB
[2025-04-02 20:06:42 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][74/234]	eta 0:02:26 lr 0.000241	time 0.8773 (0.9161)	loss 0.3838 (0.4762)	grad_norm 4.6737 (2.9802)	mem 20675MB
[2025-04-02 20:06:43 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][76/234]	eta 0:02:24 lr 0.000241	time 0.8766 (0.9151)	loss 0.4404 (0.4755)	grad_norm 4.5009 (3.0210)	mem 20675MB
[2025-04-02 20:06:45 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][78/234]	eta 0:02:22 lr 0.000240	time 0.8790 (0.9142)	loss 0.4242 (0.4751)	grad_norm 2.2006 (3.0021)	mem 20675MB
[2025-04-02 20:06:47 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][80/234]	eta 0:02:20 lr 0.000240	time 0.8772 (0.9133)	loss 0.4935 (0.4737)	grad_norm 2.9991 (2.9960)	mem 20675MB
[2025-04-02 20:06:49 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][82/234]	eta 0:02:18 lr 0.000240	time 0.8768 (0.9124)	loss 0.4851 (0.4747)	grad_norm 1.7651 (2.9790)	mem 20675MB
[2025-04-02 20:06:50 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][84/234]	eta 0:02:16 lr 0.000239	time 0.8859 (0.9117)	loss 0.5722 (0.4759)	grad_norm 2.3713 (2.9654)	mem 20675MB
[2025-04-02 20:06:52 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][86/234]	eta 0:02:14 lr 0.000239	time 0.8765 (0.9110)	loss 0.4647 (0.4767)	grad_norm 2.3333 (2.9550)	mem 20675MB
[2025-04-02 20:06:54 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][88/234]	eta 0:02:12 lr 0.000238	time 0.8777 (0.9102)	loss 0.3876 (0.4755)	grad_norm 3.0679 (2.9462)	mem 20675MB
[2025-04-02 20:06:56 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][90/234]	eta 0:02:10 lr 0.000238	time 0.8845 (0.9096)	loss 0.5099 (0.4758)	grad_norm 3.2051 (2.9500)	mem 20675MB
[2025-04-02 20:06:58 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][92/234]	eta 0:02:09 lr 0.000237	time 0.8779 (0.9089)	loss 0.4336 (0.4742)	grad_norm 3.2660 (2.9549)	mem 20675MB
[2025-04-02 20:06:59 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][94/234]	eta 0:02:07 lr 0.000237	time 0.8766 (0.9084)	loss 0.4925 (0.4735)	grad_norm 4.1067 (2.9671)	mem 20675MB
[2025-04-02 20:07:01 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][96/234]	eta 0:02:05 lr 0.000236	time 0.8781 (0.9078)	loss 0.4974 (0.4746)	grad_norm 2.3466 (2.9611)	mem 20675MB
[2025-04-02 20:07:03 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][98/234]	eta 0:02:03 lr 0.000236	time 0.8862 (0.9073)	loss 0.5694 (0.4757)	grad_norm 3.2744 (2.9563)	mem 20675MB
[2025-04-02 20:07:05 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][100/234]	eta 0:02:01 lr 0.000236	time 0.8794 (0.9068)	loss 0.5149 (0.4752)	grad_norm 2.6149 (2.9587)	mem 20675MB
[2025-04-02 20:07:06 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][102/234]	eta 0:01:59 lr 0.000235	time 0.8782 (0.9064)	loss 0.5050 (0.4764)	grad_norm 1.9548 (2.9505)	mem 20675MB
[2025-04-02 20:07:08 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][104/234]	eta 0:01:57 lr 0.000235	time 0.8781 (0.9058)	loss 0.5492 (0.4773)	grad_norm 2.4106 (2.9474)	mem 20675MB
[2025-04-02 20:07:10 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][106/234]	eta 0:01:55 lr 0.000234	time 0.9041 (0.9056)	loss 0.3594 (0.4748)	grad_norm 4.9571 (2.9636)	mem 20675MB
[2025-04-02 20:07:12 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][108/234]	eta 0:01:54 lr 0.000234	time 0.8774 (0.9051)	loss 0.4471 (0.4734)	grad_norm 2.4383 (2.9527)	mem 20675MB
[2025-04-02 20:07:13 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][110/234]	eta 0:01:52 lr 0.000233	time 0.8770 (0.9046)	loss 0.5393 (0.4743)	grad_norm 3.0345 (2.9475)	mem 20675MB
[2025-04-02 20:07:15 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][112/234]	eta 0:01:50 lr 0.000233	time 0.8786 (0.9042)	loss 0.4463 (0.4742)	grad_norm 2.9618 (2.9461)	mem 20675MB
[2025-04-02 20:07:17 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][114/234]	eta 0:01:48 lr 0.000233	time 0.8780 (0.9037)	loss 0.4000 (0.4723)	grad_norm 3.3474 (2.9525)	mem 20675MB
[2025-04-02 20:07:19 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][116/234]	eta 0:01:46 lr 0.000232	time 0.8785 (0.9033)	loss 0.4041 (0.4724)	grad_norm 2.7709 (2.9467)	mem 20675MB
[2025-04-02 20:07:20 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][118/234]	eta 0:01:44 lr 0.000232	time 0.8776 (0.9029)	loss 0.4147 (0.4721)	grad_norm 3.6031 (2.9430)	mem 20675MB
[2025-04-02 20:07:22 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][120/234]	eta 0:01:42 lr 0.000231	time 0.8776 (0.9025)	loss 0.2972 (0.4707)	grad_norm 2.3190 (2.9302)	mem 20675MB
[2025-04-02 20:07:24 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][122/234]	eta 0:01:41 lr 0.000231	time 0.8772 (0.9021)	loss 0.4275 (0.4705)	grad_norm 1.5656 (2.9189)	mem 20675MB
[2025-04-02 20:07:26 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][124/234]	eta 0:01:39 lr 0.000230	time 0.8784 (0.9017)	loss 0.4925 (0.4705)	grad_norm 2.2430 (2.9124)	mem 20675MB
[2025-04-02 20:07:27 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][126/234]	eta 0:01:37 lr 0.000230	time 0.8799 (0.9014)	loss 0.4904 (0.4713)	grad_norm 2.1714 (2.9091)	mem 20675MB
[2025-04-02 20:07:29 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][128/234]	eta 0:01:35 lr 0.000229	time 0.8782 (0.9011)	loss 0.4048 (0.4708)	grad_norm 3.3261 (2.9093)	mem 20675MB
[2025-04-02 20:07:31 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][130/234]	eta 0:01:33 lr 0.000229	time 0.8788 (0.9007)	loss 0.4695 (0.4709)	grad_norm 2.9162 (2.9162)	mem 20675MB
[2025-04-02 20:07:33 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][132/234]	eta 0:01:31 lr 0.000229	time 0.8783 (0.9004)	loss 0.4185 (0.4707)	grad_norm 3.4303 (2.9216)	mem 20675MB
[2025-04-02 20:07:35 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][134/234]	eta 0:01:30 lr 0.000228	time 0.8787 (0.9002)	loss 0.5508 (0.4717)	grad_norm 2.4851 (2.9187)	mem 20675MB
[2025-04-02 20:07:36 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][136/234]	eta 0:01:28 lr 0.000228	time 0.8793 (0.8999)	loss 0.2695 (0.4709)	grad_norm 2.6190 (2.9124)	mem 20675MB
[2025-04-02 20:07:38 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][138/234]	eta 0:01:26 lr 0.000227	time 0.8782 (0.8996)	loss 0.4464 (0.4697)	grad_norm 2.6543 (2.9151)	mem 20675MB
[2025-04-02 20:07:40 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][140/234]	eta 0:01:24 lr 0.000227	time 0.8793 (0.8994)	loss 0.4907 (0.4701)	grad_norm 2.7220 (2.9139)	mem 20675MB
[2025-04-02 20:07:42 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][142/234]	eta 0:01:22 lr 0.000226	time 0.8779 (0.8991)	loss 0.4893 (0.4703)	grad_norm 2.5144 (2.9083)	mem 20675MB
[2025-04-02 20:07:43 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][144/234]	eta 0:01:20 lr 0.000226	time 0.8767 (0.8988)	loss 0.4661 (0.4705)	grad_norm 3.1635 (2.9083)	mem 20675MB
[2025-04-02 20:07:45 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][146/234]	eta 0:01:19 lr 0.000226	time 0.8771 (0.8985)	loss 0.4750 (0.4706)	grad_norm 3.9560 (2.9091)	mem 20675MB
[2025-04-02 20:07:47 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][148/234]	eta 0:01:17 lr 0.000225	time 0.8782 (0.8983)	loss 0.4158 (0.4696)	grad_norm 3.2952 (2.9235)	mem 20675MB
[2025-04-02 20:07:49 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][150/234]	eta 0:01:15 lr 0.000225	time 0.8787 (0.8980)	loss 0.4832 (0.4698)	grad_norm 3.0519 (2.9210)	mem 20675MB
[2025-04-02 20:07:50 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][152/234]	eta 0:01:13 lr 0.000224	time 0.8782 (0.8978)	loss 0.5621 (0.4704)	grad_norm 1.9012 (2.9097)	mem 20675MB
[2025-04-02 20:07:52 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][154/234]	eta 0:01:11 lr 0.000224	time 0.8787 (0.8975)	loss 0.4445 (0.4696)	grad_norm 2.0698 (2.9008)	mem 20675MB
[2025-04-02 20:07:54 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][156/234]	eta 0:01:09 lr 0.000223	time 0.8791 (0.8973)	loss 0.3004 (0.4678)	grad_norm 3.1317 (2.9069)	mem 20675MB
[2025-04-02 20:07:56 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][158/234]	eta 0:01:08 lr 0.000223	time 0.8775 (0.8971)	loss 0.3843 (0.4675)	grad_norm 3.1349 (2.9014)	mem 20675MB
[2025-04-02 20:07:57 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][160/234]	eta 0:01:06 lr 0.000223	time 0.8858 (0.8969)	loss 0.4582 (0.4672)	grad_norm 1.9991 (2.8971)	mem 20675MB
[2025-04-02 20:07:59 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][162/234]	eta 0:01:04 lr 0.000222	time 0.8775 (0.8967)	loss 0.3482 (0.4668)	grad_norm 2.3983 (2.8898)	mem 20675MB
[2025-04-02 20:08:01 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][164/234]	eta 0:01:02 lr 0.000222	time 0.8924 (0.8966)	loss 0.3389 (0.4669)	grad_norm 2.5567 (2.8866)	mem 20675MB
[2025-04-02 20:08:03 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][166/234]	eta 0:01:00 lr 0.000221	time 0.8772 (0.8964)	loss 0.5193 (0.4676)	grad_norm 2.1667 (2.8882)	mem 20675MB
[2025-04-02 20:08:04 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][168/234]	eta 0:00:59 lr 0.000221	time 0.8774 (0.8962)	loss 0.5735 (0.4682)	grad_norm 2.5657 (2.8896)	mem 20675MB
[2025-04-02 20:08:06 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][170/234]	eta 0:00:57 lr 0.000220	time 0.8767 (0.8960)	loss 0.3367 (0.4667)	grad_norm 2.5866 (2.8870)	mem 20675MB
[2025-04-02 20:08:08 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][172/234]	eta 0:00:55 lr 0.000220	time 0.8781 (0.8958)	loss 0.5852 (0.4671)	grad_norm 3.2043 (2.8879)	mem 20675MB
[2025-04-02 20:08:10 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][174/234]	eta 0:00:53 lr 0.000220	time 0.8792 (0.8956)	loss 0.4551 (0.4672)	grad_norm 2.5763 (2.8847)	mem 20675MB
[2025-04-02 20:08:11 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][176/234]	eta 0:00:51 lr 0.000219	time 0.8785 (0.8955)	loss 0.5236 (0.4673)	grad_norm 2.4661 (2.8846)	mem 20675MB
[2025-04-02 20:08:13 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][178/234]	eta 0:00:50 lr 0.000219	time 0.8785 (0.8953)	loss 0.3470 (0.4662)	grad_norm 3.3374 (2.8902)	mem 20675MB
[2025-04-02 20:08:15 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][180/234]	eta 0:00:48 lr 0.000218	time 0.8777 (0.8952)	loss 0.4799 (0.4657)	grad_norm 3.1255 (2.8951)	mem 20675MB
[2025-04-02 20:08:17 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][182/234]	eta 0:00:46 lr 0.000218	time 0.8779 (0.8950)	loss 0.5739 (0.4664)	grad_norm 3.6915 (2.8955)	mem 20675MB
[2025-04-02 20:08:19 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][184/234]	eta 0:00:44 lr 0.000217	time 0.8782 (0.8948)	loss 0.3992 (0.4656)	grad_norm 2.4508 (2.8960)	mem 20675MB
[2025-04-02 20:08:20 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][186/234]	eta 0:00:42 lr 0.000217	time 0.8811 (0.8947)	loss 0.4191 (0.4655)	grad_norm 3.5256 (2.8936)	mem 20675MB
[2025-04-02 20:08:22 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][188/234]	eta 0:00:41 lr 0.000217	time 0.8778 (0.8945)	loss 0.5371 (0.4662)	grad_norm 2.1440 (2.8898)	mem 20675MB
[2025-04-02 20:08:24 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][190/234]	eta 0:00:39 lr 0.000216	time 0.8782 (0.8943)	loss 0.5366 (0.4671)	grad_norm 2.6886 (2.8854)	mem 20675MB
[2025-04-02 20:08:26 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][192/234]	eta 0:00:37 lr 0.000216	time 0.8773 (0.8942)	loss 0.4103 (0.4662)	grad_norm 2.5927 (2.8810)	mem 20675MB
[2025-04-02 20:08:27 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][194/234]	eta 0:00:35 lr 0.000215	time 0.8785 (0.8940)	loss 0.4173 (0.4663)	grad_norm 2.3905 (2.8761)	mem 20675MB
[2025-04-02 20:08:29 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][196/234]	eta 0:00:33 lr 0.000215	time 0.8805 (0.8939)	loss 0.3234 (0.4652)	grad_norm 3.8109 (2.8804)	mem 20675MB
[2025-04-02 20:08:31 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][198/234]	eta 0:00:32 lr 0.000215	time 0.8784 (0.8937)	loss 0.4875 (0.4656)	grad_norm 2.3042 (2.8766)	mem 20675MB
[2025-04-02 20:08:33 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][200/234]	eta 0:00:30 lr 0.000214	time 0.8789 (0.8936)	loss 0.4211 (0.4656)	grad_norm 2.5494 (2.8704)	mem 20675MB
[2025-04-02 20:08:34 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][202/234]	eta 0:00:28 lr 0.000214	time 0.8892 (0.8937)	loss 0.3338 (0.4648)	grad_norm 2.1282 (2.8730)	mem 20675MB
[2025-04-02 20:08:36 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][204/234]	eta 0:00:26 lr 0.000213	time 0.8855 (0.8936)	loss 0.3539 (0.4638)	grad_norm 2.5872 (2.8706)	mem 20675MB
[2025-04-02 20:08:38 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][206/234]	eta 0:00:25 lr 0.000213	time 0.8803 (0.8936)	loss 0.4485 (0.4639)	grad_norm 2.3598 (2.8697)	mem 20675MB
[2025-04-02 20:08:40 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][208/234]	eta 0:00:23 lr 0.000212	time 0.8795 (0.8935)	loss 0.4239 (0.4639)	grad_norm 2.7016 (2.8693)	mem 20675MB
[2025-04-02 20:08:41 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][210/234]	eta 0:00:21 lr 0.000212	time 0.8797 (0.8934)	loss 0.6450 (0.4644)	grad_norm 3.7374 (2.8751)	mem 20675MB
[2025-04-02 20:08:43 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][212/234]	eta 0:00:19 lr 0.000212	time 0.8778 (0.8932)	loss 0.4877 (0.4650)	grad_norm 3.7021 (2.8832)	mem 20675MB
[2025-04-02 20:08:45 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][214/234]	eta 0:00:17 lr 0.000211	time 0.8779 (0.8931)	loss 0.4730 (0.4648)	grad_norm 1.7276 (2.8764)	mem 20675MB
[2025-04-02 20:08:47 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][216/234]	eta 0:00:16 lr 0.000211	time 0.8787 (0.8930)	loss 0.4507 (0.4651)	grad_norm 2.0757 (2.8770)	mem 20675MB
[2025-04-02 20:08:49 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][218/234]	eta 0:00:14 lr 0.000210	time 0.8782 (0.8928)	loss 0.4600 (0.4649)	grad_norm 2.9674 (2.8749)	mem 20675MB
[2025-04-02 20:08:50 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][220/234]	eta 0:00:12 lr 0.000210	time 0.8788 (0.8927)	loss 0.4935 (0.4650)	grad_norm 2.7148 (2.8738)	mem 20675MB
[2025-04-02 20:08:52 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][222/234]	eta 0:00:10 lr 0.000210	time 0.8782 (0.8926)	loss 0.5171 (0.4650)	grad_norm 3.2740 (2.8736)	mem 20675MB
[2025-04-02 20:08:54 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][224/234]	eta 0:00:08 lr 0.000209	time 0.8797 (0.8925)	loss 0.5123 (0.4652)	grad_norm 2.6210 (2.8708)	mem 20675MB
[2025-04-02 20:08:56 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][226/234]	eta 0:00:07 lr 0.000209	time 0.8771 (0.8924)	loss 0.5106 (0.4657)	grad_norm 2.3541 (2.8666)	mem 20675MB
[2025-04-02 20:08:57 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][228/234]	eta 0:00:05 lr 0.000208	time 0.8780 (0.8923)	loss 0.5030 (0.4660)	grad_norm 1.8941 (2.8592)	mem 20675MB
[2025-04-02 20:08:59 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][230/234]	eta 0:00:03 lr 0.000208	time 0.8785 (0.8921)	loss 0.5005 (0.4659)	grad_norm 2.3653 (2.8613)	mem 20675MB
[2025-04-02 20:09:01 simmim_finetune] (main_finetune.py 252): INFO Train: [21/30][232/234]	eta 0:00:01 lr 0.000207	time 0.8831 (0.8921)	loss 0.3681 (0.4653)	grad_norm 1.8653 (2.8601)	mem 20675MB
[2025-04-02 20:09:02 simmim_finetune] (main_finetune.py 260): INFO EPOCH 21 training takes 0:03:28
[2025-04-02 20:09:04 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.784 (1.784)	Loss 0.6695 (0.6695)	Acc@1 64.844 (64.844)	Mem 20675MB
[2025-04-02 20:09:04 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.823
[2025-04-02 20:09:04 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.8%
[2025-04-02 20:09:04 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:09:04 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [9.739260631781794e-07, 9.739260631781794e-07, 1.3860248494214344e-06, 1.3860248494214344e-06, 2.0200229821033654e-06, 2.0200229821033654e-06, 2.9954047246909514e-06, 2.9954047246909514e-06, 4.4959920209795446e-06, 4.4959920209795446e-06, 6.804587861423535e-06, 6.804587861423535e-06, 1.0356273769798903e-05, 1.0356273769798903e-05, 1.5820405936530237e-05, 1.5820405936530237e-05, 2.4226763116116906e-05, 2.4226763116116906e-05, 3.715962031548102e-05, 3.715962031548102e-05, 5.7056323699118105e-05, 5.7056323699118105e-05, 8.766663659702132e-05, 8.766663659702132e-05, 0.0001347594256707186, 0.0001347594256707186, 0.00020720987039948358, 0.00020720987039948358]
[2025-04-02 20:09:07 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][0/234]	eta 0:11:26 lr 0.000207	time 2.9340 (2.9340)	loss 0.5278 (0.5278)	grad_norm 2.4354 (2.4354)	mem 20675MB
[2025-04-02 20:09:09 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][2/234]	eta 0:06:02 lr 0.000207	time 0.8778 (1.5638)	loss 0.4440 (0.4848)	grad_norm 1.7403 (2.1361)	mem 20675MB
[2025-04-02 20:09:10 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][4/234]	eta 0:04:56 lr 0.000206	time 0.8791 (1.2904)	loss 0.3629 (0.4699)	grad_norm 3.6086 (2.6139)	mem 20675MB
[2025-04-02 20:09:12 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][6/234]	eta 0:04:27 lr 0.000206	time 0.8791 (1.1732)	loss 0.4408 (0.4685)	grad_norm 2.0047 (2.5308)	mem 20675MB
[2025-04-02 20:09:14 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][8/234]	eta 0:04:10 lr 0.000205	time 0.8790 (1.1081)	loss 0.4883 (0.4745)	grad_norm 1.6291 (2.4959)	mem 20675MB
[2025-04-02 20:09:16 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][10/234]	eta 0:03:58 lr 0.000205	time 0.8780 (1.0665)	loss 0.4649 (0.4765)	grad_norm 3.5430 (2.6105)	mem 20675MB
[2025-04-02 20:09:18 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][12/234]	eta 0:03:50 lr 0.000205	time 0.8772 (1.0385)	loss 0.5052 (0.4712)	grad_norm 4.5628 (2.7910)	mem 20675MB
[2025-04-02 20:09:19 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][14/234]	eta 0:03:43 lr 0.000204	time 0.8808 (1.0175)	loss 0.5598 (0.4829)	grad_norm 4.2339 (2.8358)	mem 20675MB
[2025-04-02 20:09:21 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][16/234]	eta 0:03:38 lr 0.000204	time 0.8789 (1.0013)	loss 0.4865 (0.4857)	grad_norm 3.0525 (2.8097)	mem 20675MB
[2025-04-02 20:09:23 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][18/234]	eta 0:03:33 lr 0.000203	time 0.8785 (0.9884)	loss 0.4479 (0.4832)	grad_norm 2.1870 (2.7874)	mem 20675MB
[2025-04-02 20:09:25 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][20/234]	eta 0:03:29 lr 0.000203	time 0.8782 (0.9790)	loss 0.4479 (0.4745)	grad_norm 2.9411 (2.8124)	mem 20675MB
[2025-04-02 20:09:26 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][22/234]	eta 0:03:25 lr 0.000202	time 0.8775 (0.9702)	loss 0.3876 (0.4628)	grad_norm 3.9060 (2.9515)	mem 20675MB
[2025-04-02 20:09:28 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][24/234]	eta 0:03:22 lr 0.000202	time 0.8770 (0.9628)	loss 0.5292 (0.4669)	grad_norm 2.1972 (2.9077)	mem 20675MB
[2025-04-02 20:09:30 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][26/234]	eta 0:03:19 lr 0.000202	time 0.8806 (0.9568)	loss 0.4649 (0.4684)	grad_norm 3.2482 (2.9344)	mem 20675MB
[2025-04-02 20:09:32 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][28/234]	eta 0:03:15 lr 0.000201	time 0.8766 (0.9514)	loss 0.3696 (0.4673)	grad_norm 3.3833 (2.9155)	mem 20675MB
[2025-04-02 20:09:33 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][30/234]	eta 0:03:13 lr 0.000201	time 0.8781 (0.9469)	loss 0.5343 (0.4690)	grad_norm 2.1232 (2.8614)	mem 20675MB
[2025-04-02 20:09:35 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][32/234]	eta 0:03:10 lr 0.000200	time 0.8784 (0.9428)	loss 0.5678 (0.4742)	grad_norm 2.9203 (2.8597)	mem 20675MB
[2025-04-02 20:09:37 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][34/234]	eta 0:03:07 lr 0.000200	time 0.8779 (0.9392)	loss 0.4809 (0.4726)	grad_norm 1.6575 (2.8057)	mem 20675MB
[2025-04-02 20:09:39 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][36/234]	eta 0:03:05 lr 0.000200	time 0.8833 (0.9360)	loss 0.5648 (0.4734)	grad_norm 3.8037 (2.8393)	mem 20675MB
[2025-04-02 20:09:40 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][38/234]	eta 0:03:02 lr 0.000199	time 0.8774 (0.9331)	loss 0.3732 (0.4689)	grad_norm 1.8728 (2.8020)	mem 20675MB
[2025-04-02 20:09:42 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][40/234]	eta 0:03:00 lr 0.000199	time 0.8791 (0.9305)	loss 0.4836 (0.4663)	grad_norm 1.6357 (2.8157)	mem 20675MB
[2025-04-02 20:09:44 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][42/234]	eta 0:02:58 lr 0.000198	time 0.8764 (0.9281)	loss 0.4976 (0.4641)	grad_norm 2.7261 (2.8178)	mem 20675MB
[2025-04-02 20:09:46 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][44/234]	eta 0:02:55 lr 0.000198	time 0.8780 (0.9259)	loss 0.4337 (0.4650)	grad_norm 3.0894 (2.8087)	mem 20675MB
[2025-04-02 20:09:47 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][46/234]	eta 0:02:53 lr 0.000198	time 0.8786 (0.9239)	loss 0.3367 (0.4622)	grad_norm 3.9544 (2.8263)	mem 20675MB
[2025-04-02 20:09:49 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][48/234]	eta 0:02:51 lr 0.000197	time 0.8789 (0.9221)	loss 0.5649 (0.4658)	grad_norm 2.9606 (2.8165)	mem 20675MB
[2025-04-02 20:09:51 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][50/234]	eta 0:02:49 lr 0.000197	time 0.8788 (0.9204)	loss 0.3604 (0.4651)	grad_norm 2.9836 (2.8080)	mem 20675MB
[2025-04-02 20:09:53 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][52/234]	eta 0:02:47 lr 0.000196	time 0.8788 (0.9189)	loss 0.5546 (0.4674)	grad_norm 3.0303 (2.8165)	mem 20675MB
[2025-04-02 20:09:55 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][54/234]	eta 0:02:45 lr 0.000196	time 0.8849 (0.9175)	loss 0.5174 (0.4694)	grad_norm 2.4833 (2.8219)	mem 20675MB
[2025-04-02 20:09:56 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][56/234]	eta 0:02:43 lr 0.000195	time 0.8775 (0.9164)	loss 0.5224 (0.4676)	grad_norm 2.3007 (2.8106)	mem 20675MB
[2025-04-02 20:09:58 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][58/234]	eta 0:02:41 lr 0.000195	time 0.8793 (0.9152)	loss 0.5631 (0.4679)	grad_norm 1.9261 (2.8078)	mem 20675MB
[2025-04-02 20:10:00 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][60/234]	eta 0:02:39 lr 0.000195	time 0.8779 (0.9140)	loss 0.5176 (0.4700)	grad_norm 5.0584 (2.8421)	mem 20675MB
[2025-04-02 20:10:02 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][62/234]	eta 0:02:37 lr 0.000194	time 0.8840 (0.9130)	loss 0.4987 (0.4716)	grad_norm 2.6747 (2.8514)	mem 20675MB
[2025-04-02 20:10:03 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][64/234]	eta 0:02:35 lr 0.000194	time 0.8837 (0.9122)	loss 0.4753 (0.4692)	grad_norm 3.1993 (2.8645)	mem 20675MB
[2025-04-02 20:10:05 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][66/234]	eta 0:02:33 lr 0.000193	time 0.8787 (0.9112)	loss 0.5111 (0.4710)	grad_norm 2.5049 (2.8491)	mem 20675MB
[2025-04-02 20:10:07 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][68/234]	eta 0:02:31 lr 0.000193	time 0.8786 (0.9103)	loss 0.4913 (0.4722)	grad_norm 2.7533 (2.8325)	mem 20675MB
[2025-04-02 20:10:09 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][70/234]	eta 0:02:29 lr 0.000193	time 0.8795 (0.9094)	loss 0.5433 (0.4721)	grad_norm 2.7558 (2.8396)	mem 20675MB
[2025-04-02 20:10:10 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][72/234]	eta 0:02:27 lr 0.000192	time 0.8849 (0.9088)	loss 0.4655 (0.4725)	grad_norm 2.5109 (2.8411)	mem 20675MB
[2025-04-02 20:10:12 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][74/234]	eta 0:02:25 lr 0.000192	time 0.8794 (0.9083)	loss 0.4954 (0.4735)	grad_norm 2.9732 (2.8383)	mem 20675MB
[2025-04-02 20:10:14 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][76/234]	eta 0:02:23 lr 0.000191	time 0.8793 (0.9076)	loss 0.3882 (0.4723)	grad_norm 3.0597 (2.8279)	mem 20675MB
[2025-04-02 20:10:16 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][78/234]	eta 0:02:21 lr 0.000191	time 0.8788 (0.9069)	loss 0.2893 (0.4679)	grad_norm 4.7593 (2.8591)	mem 20675MB
[2025-04-02 20:10:17 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][80/234]	eta 0:02:19 lr 0.000191	time 0.8811 (0.9063)	loss 0.3653 (0.4672)	grad_norm 2.8268 (2.8550)	mem 20675MB
[2025-04-02 20:10:19 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][82/234]	eta 0:02:17 lr 0.000190	time 0.8793 (0.9056)	loss 0.4692 (0.4679)	grad_norm 2.3495 (2.8607)	mem 20675MB
[2025-04-02 20:10:21 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][84/234]	eta 0:02:15 lr 0.000190	time 0.8781 (0.9050)	loss 0.5338 (0.4696)	grad_norm 2.2262 (2.8608)	mem 20675MB
[2025-04-02 20:10:23 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][86/234]	eta 0:02:13 lr 0.000189	time 0.8784 (0.9046)	loss 0.5685 (0.4698)	grad_norm 3.2304 (2.8746)	mem 20675MB
[2025-04-02 20:10:25 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][88/234]	eta 0:02:11 lr 0.000189	time 0.8799 (0.9041)	loss 0.6042 (0.4714)	grad_norm 2.9255 (2.8719)	mem 20675MB
[2025-04-02 20:10:26 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][90/234]	eta 0:02:10 lr 0.000189	time 0.8784 (0.9036)	loss 0.5209 (0.4722)	grad_norm 2.3601 (2.8748)	mem 20675MB
[2025-04-02 20:10:28 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][92/234]	eta 0:02:08 lr 0.000188	time 0.8792 (0.9031)	loss 0.5369 (0.4730)	grad_norm 3.5276 (2.8748)	mem 20675MB
[2025-04-02 20:10:30 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][94/234]	eta 0:02:06 lr 0.000188	time 0.8787 (0.9026)	loss 0.4090 (0.4721)	grad_norm 3.7744 (2.8799)	mem 20675MB
[2025-04-02 20:10:32 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][96/234]	eta 0:02:04 lr 0.000187	time 0.8804 (0.9021)	loss 0.5227 (0.4731)	grad_norm 3.8116 (2.8852)	mem 20675MB
[2025-04-02 20:10:33 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][98/234]	eta 0:02:02 lr 0.000187	time 0.8843 (0.9018)	loss 0.4438 (0.4726)	grad_norm 3.9170 (2.8937)	mem 20675MB
[2025-04-02 20:10:35 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][100/234]	eta 0:02:00 lr 0.000187	time 0.8783 (0.9013)	loss 0.3066 (0.4699)	grad_norm 5.2352 (2.9205)	mem 20675MB
[2025-04-02 20:10:37 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][102/234]	eta 0:01:58 lr 0.000186	time 0.8783 (0.9009)	loss 0.5355 (0.4713)	grad_norm 2.2168 (2.9106)	mem 20675MB
[2025-04-02 20:10:39 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][104/234]	eta 0:01:57 lr 0.000186	time 0.8778 (0.9005)	loss 0.3900 (0.4710)	grad_norm 3.0839 (2.9061)	mem 20675MB
[2025-04-02 20:10:40 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][106/234]	eta 0:01:55 lr 0.000185	time 0.8774 (0.9001)	loss 0.5247 (0.4719)	grad_norm 2.2967 (2.9105)	mem 20675MB
[2025-04-02 20:10:42 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][108/234]	eta 0:01:53 lr 0.000185	time 0.8786 (0.8998)	loss 0.4585 (0.4707)	grad_norm 2.5745 (2.9533)	mem 20675MB
[2025-04-02 20:10:44 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][110/234]	eta 0:01:51 lr 0.000185	time 0.8771 (0.8994)	loss 0.4572 (0.4705)	grad_norm 1.5962 (2.9417)	mem 20675MB
[2025-04-02 20:10:46 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][112/234]	eta 0:01:49 lr 0.000184	time 0.8804 (0.8990)	loss 0.4849 (0.4697)	grad_norm 2.5406 (2.9350)	mem 20675MB
[2025-04-02 20:10:47 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][114/234]	eta 0:01:47 lr 0.000184	time 0.8836 (0.8987)	loss 0.3914 (0.4692)	grad_norm 3.7543 (2.9375)	mem 20675MB
[2025-04-02 20:10:49 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][116/234]	eta 0:01:46 lr 0.000183	time 0.8945 (0.8986)	loss 0.5397 (0.4685)	grad_norm 2.3236 (2.9339)	mem 20675MB
[2025-04-02 20:10:51 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][118/234]	eta 0:01:44 lr 0.000183	time 0.8787 (0.8983)	loss 0.5069 (0.4689)	grad_norm 2.0747 (2.9159)	mem 20675MB
[2025-04-02 20:10:53 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][120/234]	eta 0:01:42 lr 0.000183	time 0.8770 (0.8980)	loss 0.4229 (0.4676)	grad_norm 2.4031 (2.9117)	mem 20675MB
[2025-04-02 20:10:54 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][122/234]	eta 0:01:40 lr 0.000182	time 0.8763 (0.8977)	loss 0.4853 (0.4683)	grad_norm 2.2038 (2.9101)	mem 20675MB
[2025-04-02 20:10:56 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][124/234]	eta 0:01:38 lr 0.000182	time 0.8779 (0.8974)	loss 0.4352 (0.4679)	grad_norm 2.3458 (2.9001)	mem 20675MB
[2025-04-02 20:10:58 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][126/234]	eta 0:01:36 lr 0.000181	time 0.8798 (0.8971)	loss 0.4413 (0.4669)	grad_norm 3.8021 (2.9121)	mem 20675MB
[2025-04-02 20:11:00 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][128/234]	eta 0:01:35 lr 0.000181	time 0.8790 (0.8969)	loss 0.3410 (0.4670)	grad_norm 4.1038 (2.9184)	mem 20675MB
[2025-04-02 20:11:02 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][130/234]	eta 0:01:33 lr 0.000181	time 0.8833 (0.8966)	loss 0.5865 (0.4681)	grad_norm 3.6599 (2.9206)	mem 20675MB
[2025-04-02 20:11:03 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][132/234]	eta 0:01:31 lr 0.000180	time 0.8776 (0.8964)	loss 0.5863 (0.4695)	grad_norm 3.6350 (2.9233)	mem 20675MB
[2025-04-02 20:11:05 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][134/234]	eta 0:01:29 lr 0.000180	time 0.8777 (0.8961)	loss 0.4553 (0.4684)	grad_norm 2.4210 (2.9197)	mem 20675MB
[2025-04-02 20:11:07 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][136/234]	eta 0:01:27 lr 0.000180	time 0.8818 (0.8959)	loss 0.5411 (0.4695)	grad_norm 2.1876 (2.9097)	mem 20675MB
[2025-04-02 20:11:09 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][138/234]	eta 0:01:25 lr 0.000179	time 0.8783 (0.8957)	loss 0.5250 (0.4708)	grad_norm 2.0779 (2.9013)	mem 20675MB
[2025-04-02 20:11:10 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][140/234]	eta 0:01:24 lr 0.000179	time 0.8907 (0.8955)	loss 0.3824 (0.4701)	grad_norm 2.9313 (2.8953)	mem 20675MB
[2025-04-02 20:11:12 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][142/234]	eta 0:01:22 lr 0.000178	time 0.8793 (0.8953)	loss 0.4439 (0.4703)	grad_norm 2.1507 (2.8907)	mem 20675MB
[2025-04-02 20:11:14 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][144/234]	eta 0:01:20 lr 0.000178	time 0.8791 (0.8951)	loss 0.5335 (0.4697)	grad_norm 1.9451 (2.8937)	mem 20675MB
[2025-04-02 20:11:16 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][146/234]	eta 0:01:18 lr 0.000178	time 0.8780 (0.8949)	loss 0.3578 (0.4691)	grad_norm 3.9249 (2.9002)	mem 20675MB
[2025-04-02 20:11:17 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][148/234]	eta 0:01:16 lr 0.000177	time 0.8790 (0.8947)	loss 0.5070 (0.4702)	grad_norm 2.9212 (2.8979)	mem 20675MB
[2025-04-02 20:11:19 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][150/234]	eta 0:01:15 lr 0.000177	time 0.8789 (0.8945)	loss 0.4108 (0.4700)	grad_norm 3.3930 (2.8980)	mem 20675MB
[2025-04-02 20:11:21 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][152/234]	eta 0:01:13 lr 0.000176	time 0.8812 (0.8943)	loss 0.5298 (0.4707)	grad_norm 2.5858 (2.8922)	mem 20675MB
[2025-04-02 20:11:23 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][154/234]	eta 0:01:11 lr 0.000176	time 0.8784 (0.8941)	loss 0.6030 (0.4716)	grad_norm 2.8209 (2.8851)	mem 20675MB
[2025-04-02 20:11:24 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][156/234]	eta 0:01:09 lr 0.000176	time 0.8813 (0.8940)	loss 0.4562 (0.4715)	grad_norm 2.7851 (2.8825)	mem 20675MB
[2025-04-02 20:11:26 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][158/234]	eta 0:01:07 lr 0.000175	time 0.8783 (0.8938)	loss 0.5194 (0.4710)	grad_norm 2.9449 (2.8910)	mem 20675MB
[2025-04-02 20:11:28 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][160/234]	eta 0:01:06 lr 0.000175	time 0.8843 (0.8936)	loss 0.5144 (0.4715)	grad_norm 3.4318 (2.8898)	mem 20675MB
[2025-04-02 20:11:30 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][162/234]	eta 0:01:04 lr 0.000174	time 0.8808 (0.8935)	loss 0.4306 (0.4717)	grad_norm 2.9849 (2.8909)	mem 20675MB
[2025-04-02 20:11:31 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][164/234]	eta 0:01:02 lr 0.000174	time 0.8777 (0.8933)	loss 0.4012 (0.4702)	grad_norm 2.9025 (2.8973)	mem 20675MB
[2025-04-02 20:11:33 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][166/234]	eta 0:01:00 lr 0.000174	time 0.8788 (0.8932)	loss 0.5162 (0.4698)	grad_norm 4.3838 (2.9016)	mem 20675MB
[2025-04-02 20:11:35 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][168/234]	eta 0:00:58 lr 0.000173	time 0.8780 (0.8930)	loss 0.3944 (0.4692)	grad_norm 3.2224 (2.9016)	mem 20675MB
[2025-04-02 20:11:37 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][170/234]	eta 0:00:57 lr 0.000173	time 0.8819 (0.8929)	loss 0.5461 (0.4700)	grad_norm 2.0382 (2.8950)	mem 20675MB
[2025-04-02 20:11:38 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][172/234]	eta 0:00:55 lr 0.000173	time 0.8788 (0.8927)	loss 0.3903 (0.4685)	grad_norm 3.4745 (2.9013)	mem 20675MB
[2025-04-02 20:11:40 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][174/234]	eta 0:00:53 lr 0.000172	time 0.8819 (0.8926)	loss 0.4967 (0.4692)	grad_norm 2.0817 (2.8966)	mem 20675MB
[2025-04-02 20:11:42 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][176/234]	eta 0:00:51 lr 0.000172	time 0.8781 (0.8925)	loss 0.5255 (0.4693)	grad_norm 2.4063 (2.8916)	mem 20675MB
[2025-04-02 20:11:44 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][178/234]	eta 0:00:49 lr 0.000171	time 0.9163 (0.8926)	loss 0.5532 (0.4691)	grad_norm 2.7084 (2.8913)	mem 20675MB
[2025-04-02 20:11:46 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][180/234]	eta 0:00:48 lr 0.000171	time 0.8789 (0.8925)	loss 0.2977 (0.4687)	grad_norm 2.9611 (2.8944)	mem 20675MB
[2025-04-02 20:11:47 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][182/234]	eta 0:00:46 lr 0.000171	time 0.8788 (0.8923)	loss 0.4918 (0.4687)	grad_norm 2.4442 (2.8890)	mem 20675MB
[2025-04-02 20:11:49 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][184/234]	eta 0:00:44 lr 0.000170	time 0.8813 (0.8922)	loss 0.5658 (0.4691)	grad_norm 2.6140 (2.8898)	mem 20675MB
[2025-04-02 20:11:51 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][186/234]	eta 0:00:42 lr 0.000170	time 0.8785 (0.8921)	loss 0.3646 (0.4678)	grad_norm 2.3750 (2.8887)	mem 20675MB
[2025-04-02 20:11:53 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][188/234]	eta 0:00:41 lr 0.000169	time 0.8792 (0.8920)	loss 0.4852 (0.4682)	grad_norm 2.2142 (2.8839)	mem 20675MB
[2025-04-02 20:11:54 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][190/234]	eta 0:00:39 lr 0.000169	time 0.8776 (0.8918)	loss 0.4928 (0.4687)	grad_norm 2.8171 (2.8912)	mem 20675MB
[2025-04-02 20:11:56 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][192/234]	eta 0:00:37 lr 0.000169	time 0.8769 (0.8917)	loss 0.4502 (0.4689)	grad_norm 1.7598 (2.8810)	mem 20675MB
[2025-04-02 20:11:58 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][194/234]	eta 0:00:35 lr 0.000168	time 0.8796 (0.8916)	loss 0.3337 (0.4679)	grad_norm 2.7405 (2.8794)	mem 20675MB
[2025-04-02 20:12:00 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][196/234]	eta 0:00:33 lr 0.000168	time 0.8782 (0.8914)	loss 0.5106 (0.4684)	grad_norm 2.0666 (2.8766)	mem 20675MB
[2025-04-02 20:12:01 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][198/234]	eta 0:00:32 lr 0.000168	time 0.8788 (0.8913)	loss 0.5547 (0.4693)	grad_norm 2.6176 (2.8747)	mem 20675MB
[2025-04-02 20:12:03 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][200/234]	eta 0:00:30 lr 0.000167	time 0.8784 (0.8912)	loss 0.4922 (0.4694)	grad_norm 3.2028 (2.8691)	mem 20675MB
[2025-04-02 20:12:05 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][202/234]	eta 0:00:28 lr 0.000167	time 0.8779 (0.8911)	loss 0.4742 (0.4696)	grad_norm 3.4207 (2.8708)	mem 20675MB
[2025-04-02 20:12:07 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][204/234]	eta 0:00:26 lr 0.000166	time 0.8773 (0.8910)	loss 0.4799 (0.4689)	grad_norm 2.5468 (2.8713)	mem 20675MB
[2025-04-02 20:12:08 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][206/234]	eta 0:00:24 lr 0.000166	time 0.8786 (0.8909)	loss 0.4930 (0.4682)	grad_norm 1.8218 (2.8689)	mem 20675MB
[2025-04-02 20:12:10 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][208/234]	eta 0:00:23 lr 0.000166	time 0.8784 (0.8908)	loss 0.5248 (0.4683)	grad_norm 2.2997 (2.8674)	mem 20675MB
[2025-04-02 20:12:12 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][210/234]	eta 0:00:21 lr 0.000165	time 0.8818 (0.8907)	loss 0.5079 (0.4680)	grad_norm 3.5497 (2.8698)	mem 20675MB
[2025-04-02 20:12:14 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][212/234]	eta 0:00:19 lr 0.000165	time 0.8787 (0.8906)	loss 0.3959 (0.4682)	grad_norm 2.4119 (2.8681)	mem 20675MB
[2025-04-02 20:12:15 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][214/234]	eta 0:00:17 lr 0.000165	time 0.8780 (0.8905)	loss 0.4620 (0.4685)	grad_norm 3.7286 (2.8728)	mem 20675MB
[2025-04-02 20:12:17 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][216/234]	eta 0:00:16 lr 0.000164	time 0.8794 (0.8904)	loss 0.4882 (0.4683)	grad_norm 2.6162 (2.8834)	mem 20675MB
[2025-04-02 20:12:19 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][218/234]	eta 0:00:14 lr 0.000164	time 0.8791 (0.8903)	loss 0.3730 (0.4676)	grad_norm 2.9387 (2.8822)	mem 20675MB
[2025-04-02 20:12:21 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][220/234]	eta 0:00:12 lr 0.000163	time 0.8788 (0.8902)	loss 0.4659 (0.4673)	grad_norm 2.0370 (2.8748)	mem 20675MB
[2025-04-02 20:12:23 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][222/234]	eta 0:00:10 lr 0.000163	time 0.8792 (0.8901)	loss 0.3949 (0.4666)	grad_norm 2.7064 (2.8775)	mem 20675MB
[2025-04-02 20:12:24 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][224/234]	eta 0:00:08 lr 0.000163	time 0.8771 (0.8900)	loss 0.4841 (0.4671)	grad_norm 2.7333 (2.8812)	mem 20675MB
[2025-04-02 20:12:26 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][226/234]	eta 0:00:07 lr 0.000162	time 0.8786 (0.8899)	loss 0.5504 (0.4672)	grad_norm 4.0525 (2.8878)	mem 20675MB
[2025-04-02 20:12:28 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][228/234]	eta 0:00:05 lr 0.000162	time 0.8788 (0.8899)	loss 0.5266 (0.4678)	grad_norm 2.3557 (2.8848)	mem 20675MB
[2025-04-02 20:12:30 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][230/234]	eta 0:00:03 lr 0.000162	time 0.8780 (0.8898)	loss 0.4711 (0.4678)	grad_norm 2.6099 (2.8826)	mem 20675MB
[2025-04-02 20:12:31 simmim_finetune] (main_finetune.py 252): INFO Train: [22/30][232/234]	eta 0:00:01 lr 0.000161	time 0.8784 (0.8897)	loss 0.3767 (0.4669)	grad_norm 3.4687 (2.8960)	mem 20675MB
[2025-04-02 20:12:32 simmim_finetune] (main_finetune.py 260): INFO EPOCH 22 training takes 0:03:28
[2025-04-02 20:12:34 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.802 (1.802)	Loss 0.6972 (0.6972)	Acc@1 67.188 (67.188)	Mem 20675MB
[2025-04-02 20:12:34 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 73.481
[2025-04-02 20:12:34 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 73.5%
[2025-04-02 20:12:35 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:12:35 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [8.120768290014515e-07, 8.120768290014515e-07, 1.1320420723994428e-06, 1.1320420723994428e-06, 1.6242962930117372e-06, 1.6242962930117372e-06, 2.3816104785691133e-06, 2.3816104785691133e-06, 3.5467092255804604e-06, 3.5467092255804604e-06, 5.3391688363671495e-06, 5.3391688363671495e-06, 8.096799006808206e-06, 8.096799006808206e-06, 1.233930696133291e-05, 1.233930696133291e-05, 1.8866242275986303e-05, 1.8866242275986303e-05, 2.8907681221606908e-05, 2.8907681221606908e-05, 4.435604883025399e-05, 4.435604883025399e-05, 6.812276822817259e-05, 6.812276822817259e-05, 0.0001046869519172781, 0.0001046869519172781, 0.00016093954220820967, 0.00016093954220820967]
[2025-04-02 20:12:38 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][0/234]	eta 0:13:08 lr 0.000161	time 3.3701 (3.3701)	loss 0.3943 (0.3943)	grad_norm 2.4977 (2.4977)	mem 20675MB
[2025-04-02 20:12:40 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][2/234]	eta 0:06:37 lr 0.000160	time 0.8839 (1.7116)	loss 0.4840 (0.4605)	grad_norm 2.0882 (2.5514)	mem 20675MB
[2025-04-02 20:12:41 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][4/234]	eta 0:05:17 lr 0.000160	time 0.8787 (1.3792)	loss 0.3538 (0.4426)	grad_norm 3.6743 (2.6172)	mem 20675MB
[2025-04-02 20:12:43 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][6/234]	eta 0:04:42 lr 0.000160	time 0.8780 (1.2369)	loss 0.5929 (0.4749)	grad_norm 3.1607 (2.6967)	mem 20675MB
[2025-04-02 20:12:45 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][8/234]	eta 0:04:21 lr 0.000159	time 0.8808 (1.1582)	loss 0.3599 (0.4604)	grad_norm 3.1222 (2.7760)	mem 20675MB
[2025-04-02 20:12:47 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][10/234]	eta 0:04:08 lr 0.000159	time 0.8786 (1.1076)	loss 0.3525 (0.4508)	grad_norm 4.1919 (2.8084)	mem 20675MB
[2025-04-02 20:12:48 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][12/234]	eta 0:03:58 lr 0.000159	time 0.8816 (1.0730)	loss 0.4797 (0.4611)	grad_norm 1.5521 (2.6766)	mem 20675MB
[2025-04-02 20:12:50 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][14/234]	eta 0:03:50 lr 0.000158	time 0.8863 (1.0476)	loss 0.5461 (0.4717)	grad_norm 2.7813 (2.6581)	mem 20675MB
[2025-04-02 20:12:52 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][16/234]	eta 0:03:44 lr 0.000158	time 0.8818 (1.0282)	loss 0.4551 (0.4719)	grad_norm 3.2242 (2.6570)	mem 20675MB
[2025-04-02 20:12:54 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][18/234]	eta 0:03:38 lr 0.000157	time 0.8783 (1.0125)	loss 0.5597 (0.4774)	grad_norm 2.1815 (2.6454)	mem 20675MB
[2025-04-02 20:12:56 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][20/234]	eta 0:03:33 lr 0.000157	time 0.8783 (0.9998)	loss 0.2799 (0.4697)	grad_norm 3.5763 (2.7267)	mem 20675MB
[2025-04-02 20:12:57 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][22/234]	eta 0:03:29 lr 0.000157	time 0.8767 (0.9893)	loss 0.5194 (0.4721)	grad_norm 2.5029 (2.6922)	mem 20675MB
[2025-04-02 20:12:59 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][24/234]	eta 0:03:25 lr 0.000156	time 0.8841 (0.9808)	loss 0.4019 (0.4684)	grad_norm 2.3538 (2.6582)	mem 20675MB
[2025-04-02 20:13:01 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][26/234]	eta 0:03:22 lr 0.000156	time 0.8770 (0.9732)	loss 0.4512 (0.4705)	grad_norm 5.3145 (2.7684)	mem 20675MB
[2025-04-02 20:13:03 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][28/234]	eta 0:03:19 lr 0.000156	time 0.8779 (0.9668)	loss 0.4241 (0.4729)	grad_norm 3.1309 (2.7744)	mem 20675MB
[2025-04-02 20:13:04 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][30/234]	eta 0:03:16 lr 0.000155	time 0.8781 (0.9612)	loss 0.4588 (0.4776)	grad_norm 2.2773 (2.7773)	mem 20675MB
[2025-04-02 20:13:06 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][32/234]	eta 0:03:13 lr 0.000155	time 0.8782 (0.9563)	loss 0.4853 (0.4756)	grad_norm 2.0821 (2.7431)	mem 20675MB
[2025-04-02 20:13:08 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][34/234]	eta 0:03:10 lr 0.000154	time 0.8780 (0.9519)	loss 0.5094 (0.4723)	grad_norm 3.1283 (2.7589)	mem 20675MB
[2025-04-02 20:13:10 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][36/234]	eta 0:03:07 lr 0.000154	time 0.8772 (0.9479)	loss 0.5964 (0.4770)	grad_norm 4.8992 (2.8035)	mem 20675MB
[2025-04-02 20:13:11 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][38/234]	eta 0:03:05 lr 0.000154	time 0.8768 (0.9443)	loss 0.4397 (0.4757)	grad_norm 4.7031 (2.8377)	mem 20675MB
[2025-04-02 20:13:13 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][40/234]	eta 0:03:02 lr 0.000153	time 0.8776 (0.9411)	loss 0.3302 (0.4698)	grad_norm 4.8441 (2.9126)	mem 20675MB
[2025-04-02 20:13:15 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][42/234]	eta 0:03:00 lr 0.000153	time 0.8795 (0.9383)	loss 0.4426 (0.4706)	grad_norm 3.8739 (2.9349)	mem 20675MB
[2025-04-02 20:13:17 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][44/234]	eta 0:02:57 lr 0.000153	time 0.8785 (0.9357)	loss 0.5141 (0.4674)	grad_norm 3.4975 (2.9321)	mem 20675MB
[2025-04-02 20:13:18 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][46/234]	eta 0:02:55 lr 0.000152	time 0.8782 (0.9333)	loss 0.3739 (0.4678)	grad_norm 3.4396 (2.9490)	mem 20675MB
[2025-04-02 20:13:20 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][48/234]	eta 0:02:53 lr 0.000152	time 0.8806 (0.9312)	loss 0.5034 (0.4703)	grad_norm 2.4139 (2.9583)	mem 20675MB
[2025-04-02 20:13:22 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][50/234]	eta 0:02:50 lr 0.000152	time 0.8774 (0.9292)	loss 0.4333 (0.4693)	grad_norm 2.7337 (2.9303)	mem 20675MB
[2025-04-02 20:13:24 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][52/234]	eta 0:02:48 lr 0.000151	time 0.8770 (0.9273)	loss 0.5290 (0.4692)	grad_norm 2.8413 (2.9121)	mem 20675MB
[2025-04-02 20:13:25 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][54/234]	eta 0:02:46 lr 0.000151	time 0.8774 (0.9257)	loss 0.4600 (0.4685)	grad_norm 3.8736 (2.9350)	mem 20675MB
[2025-04-02 20:13:27 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][56/234]	eta 0:02:44 lr 0.000150	time 0.8801 (0.9242)	loss 0.3382 (0.4679)	grad_norm 2.7004 (2.9523)	mem 20675MB
[2025-04-02 20:13:29 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][58/234]	eta 0:02:42 lr 0.000150	time 0.8766 (0.9228)	loss 0.4641 (0.4667)	grad_norm 1.7791 (2.9277)	mem 20675MB
[2025-04-02 20:13:31 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][60/234]	eta 0:02:40 lr 0.000150	time 0.8782 (0.9214)	loss 0.3720 (0.4667)	grad_norm 2.6879 (2.9643)	mem 20675MB
[2025-04-02 20:13:32 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][62/234]	eta 0:02:38 lr 0.000149	time 0.8762 (0.9200)	loss 0.5027 (0.4677)	grad_norm 2.4328 (2.9612)	mem 20675MB
[2025-04-02 20:13:34 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][64/234]	eta 0:02:36 lr 0.000149	time 0.8773 (0.9187)	loss 0.3060 (0.4649)	grad_norm 2.3524 (2.9505)	mem 20675MB
[2025-04-02 20:13:36 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][66/234]	eta 0:02:34 lr 0.000149	time 0.8767 (0.9175)	loss 0.5240 (0.4670)	grad_norm 2.5591 (2.9446)	mem 20675MB
[2025-04-02 20:13:38 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][68/234]	eta 0:02:32 lr 0.000148	time 0.8880 (0.9165)	loss 0.4479 (0.4664)	grad_norm 2.6582 (2.9287)	mem 20675MB
[2025-04-02 20:13:40 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][70/234]	eta 0:02:30 lr 0.000148	time 0.8782 (0.9155)	loss 0.4931 (0.4681)	grad_norm 2.3383 (2.9240)	mem 20675MB
[2025-04-02 20:13:41 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][72/234]	eta 0:02:28 lr 0.000148	time 0.8784 (0.9146)	loss 0.4150 (0.4669)	grad_norm 2.9525 (2.9437)	mem 20675MB
[2025-04-02 20:13:43 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][74/234]	eta 0:02:26 lr 0.000147	time 0.8792 (0.9137)	loss 0.3357 (0.4648)	grad_norm 2.2113 (2.9345)	mem 20675MB
[2025-04-02 20:13:45 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][76/234]	eta 0:02:24 lr 0.000147	time 0.8797 (0.9128)	loss 0.2661 (0.4637)	grad_norm 3.2239 (2.9334)	mem 20675MB
[2025-04-02 20:13:47 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][78/234]	eta 0:02:22 lr 0.000146	time 0.8785 (0.9120)	loss 0.5387 (0.4658)	grad_norm 2.4468 (2.9235)	mem 20675MB
[2025-04-02 20:13:48 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][80/234]	eta 0:02:20 lr 0.000146	time 0.8785 (0.9112)	loss 0.4487 (0.4658)	grad_norm 2.5837 (2.9118)	mem 20675MB
[2025-04-02 20:13:50 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][82/234]	eta 0:02:18 lr 0.000146	time 0.8794 (0.9105)	loss 0.4922 (0.4665)	grad_norm 2.9845 (2.8997)	mem 20675MB
[2025-04-02 20:13:52 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][84/234]	eta 0:02:16 lr 0.000145	time 0.8790 (0.9099)	loss 0.4865 (0.4678)	grad_norm 2.8893 (2.9044)	mem 20675MB
[2025-04-02 20:13:54 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][86/234]	eta 0:02:14 lr 0.000145	time 0.8783 (0.9092)	loss 0.5289 (0.4677)	grad_norm 2.0418 (2.8970)	mem 20675MB
[2025-04-02 20:13:55 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][88/234]	eta 0:02:12 lr 0.000145	time 0.8798 (0.9085)	loss 0.3610 (0.4665)	grad_norm 2.7313 (2.8868)	mem 20675MB
[2025-04-02 20:13:57 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][90/234]	eta 0:02:10 lr 0.000144	time 0.8791 (0.9079)	loss 0.4733 (0.4646)	grad_norm 2.8365 (2.8863)	mem 20675MB
[2025-04-02 20:13:59 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][92/234]	eta 0:02:08 lr 0.000144	time 0.8787 (0.9073)	loss 0.5717 (0.4662)	grad_norm 3.7168 (2.8919)	mem 20675MB
[2025-04-02 20:14:01 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][94/234]	eta 0:02:06 lr 0.000144	time 0.8773 (0.9067)	loss 0.4508 (0.4657)	grad_norm 3.2689 (2.8938)	mem 20675MB
[2025-04-02 20:14:02 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][96/234]	eta 0:02:05 lr 0.000143	time 0.8782 (0.9061)	loss 0.3998 (0.4660)	grad_norm 2.8691 (2.9042)	mem 20675MB
[2025-04-02 20:14:04 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][98/234]	eta 0:02:03 lr 0.000143	time 0.8801 (0.9056)	loss 0.4829 (0.4667)	grad_norm 3.5778 (2.9072)	mem 20675MB
[2025-04-02 20:14:06 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][100/234]	eta 0:02:01 lr 0.000143	time 0.8773 (0.9051)	loss 0.4521 (0.4655)	grad_norm 3.1618 (2.9103)	mem 20675MB
[2025-04-02 20:14:08 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][102/234]	eta 0:01:59 lr 0.000142	time 0.8786 (0.9046)	loss 0.3155 (0.4629)	grad_norm 3.9287 (2.9414)	mem 20675MB
[2025-04-02 20:14:09 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][104/234]	eta 0:01:57 lr 0.000142	time 0.8775 (0.9042)	loss 0.4863 (0.4637)	grad_norm 2.7175 (2.9331)	mem 20675MB
[2025-04-02 20:14:11 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][106/234]	eta 0:01:55 lr 0.000141	time 0.8781 (0.9037)	loss 0.5853 (0.4647)	grad_norm 3.0895 (2.9272)	mem 20675MB
[2025-04-02 20:14:13 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][108/234]	eta 0:01:53 lr 0.000141	time 0.8772 (0.9032)	loss 0.4871 (0.4654)	grad_norm 2.5996 (2.9265)	mem 20675MB
[2025-04-02 20:14:15 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][110/234]	eta 0:01:51 lr 0.000141	time 0.8783 (0.9028)	loss 0.4707 (0.4643)	grad_norm 1.9631 (2.9329)	mem 20675MB
[2025-04-02 20:14:16 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][112/234]	eta 0:01:50 lr 0.000140	time 0.8783 (0.9024)	loss 0.5339 (0.4651)	grad_norm 2.3658 (2.9319)	mem 20675MB
[2025-04-02 20:14:18 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][114/234]	eta 0:01:48 lr 0.000140	time 0.8779 (0.9020)	loss 0.4183 (0.4655)	grad_norm 2.7300 (2.9269)	mem 20675MB
[2025-04-02 20:14:20 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][116/234]	eta 0:01:46 lr 0.000140	time 0.8783 (0.9016)	loss 0.4850 (0.4647)	grad_norm 2.1370 (2.9151)	mem 20675MB
[2025-04-02 20:14:22 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][118/234]	eta 0:01:44 lr 0.000139	time 0.8780 (0.9012)	loss 0.4038 (0.4638)	grad_norm 2.2933 (2.9111)	mem 20675MB
[2025-04-02 20:14:24 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][120/234]	eta 0:01:42 lr 0.000139	time 0.8779 (0.9009)	loss 0.4645 (0.4637)	grad_norm 1.7902 (2.9061)	mem 20675MB
[2025-04-02 20:14:25 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][122/234]	eta 0:01:40 lr 0.000139	time 0.8771 (0.9005)	loss 0.5882 (0.4641)	grad_norm 3.2235 (2.9064)	mem 20675MB
[2025-04-02 20:14:27 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][124/234]	eta 0:01:39 lr 0.000138	time 0.8778 (0.9002)	loss 0.2985 (0.4638)	grad_norm 2.8448 (2.9156)	mem 20675MB
[2025-04-02 20:14:29 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][126/234]	eta 0:01:37 lr 0.000138	time 0.8784 (0.8998)	loss 0.5671 (0.4653)	grad_norm 2.4747 (2.9083)	mem 20675MB
[2025-04-02 20:14:31 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][128/234]	eta 0:01:35 lr 0.000138	time 0.8782 (0.8995)	loss 0.4880 (0.4647)	grad_norm 2.2571 (2.9018)	mem 20675MB
[2025-04-02 20:14:32 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][130/234]	eta 0:01:33 lr 0.000137	time 0.8842 (0.8993)	loss 0.2937 (0.4635)	grad_norm 3.1923 (2.8983)	mem 20675MB
[2025-04-02 20:14:34 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][132/234]	eta 0:01:31 lr 0.000137	time 0.8787 (0.8990)	loss 0.4692 (0.4628)	grad_norm 3.3497 (2.9166)	mem 20675MB
[2025-04-02 20:14:36 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][134/234]	eta 0:01:29 lr 0.000137	time 0.8870 (0.8988)	loss 0.5165 (0.4630)	grad_norm 3.0951 (2.9215)	mem 20675MB
[2025-04-02 20:14:38 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][136/234]	eta 0:01:28 lr 0.000136	time 0.8836 (0.8985)	loss 0.5638 (0.4643)	grad_norm 2.2577 (2.9173)	mem 20675MB
[2025-04-02 20:14:39 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][138/234]	eta 0:01:26 lr 0.000136	time 0.8788 (0.8982)	loss 0.4885 (0.4643)	grad_norm 1.9918 (2.9037)	mem 20675MB
[2025-04-02 20:14:41 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][140/234]	eta 0:01:24 lr 0.000135	time 0.8786 (0.8980)	loss 0.5118 (0.4637)	grad_norm 1.9460 (2.9001)	mem 20675MB
[2025-04-02 20:14:43 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][142/234]	eta 0:01:22 lr 0.000135	time 0.8790 (0.8977)	loss 0.5702 (0.4645)	grad_norm 2.5583 (2.8987)	mem 20675MB
[2025-04-02 20:14:45 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][144/234]	eta 0:01:20 lr 0.000135	time 0.8787 (0.8975)	loss 0.4622 (0.4634)	grad_norm 1.7483 (2.8881)	mem 20675MB
[2025-04-02 20:14:46 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][146/234]	eta 0:01:18 lr 0.000134	time 0.8789 (0.8974)	loss 0.5066 (0.4631)	grad_norm 2.0405 (2.8805)	mem 20675MB
[2025-04-02 20:14:48 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][148/234]	eta 0:01:17 lr 0.000134	time 0.8794 (0.8971)	loss 0.5413 (0.4639)	grad_norm 2.9681 (2.8933)	mem 20675MB
[2025-04-02 20:14:50 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][150/234]	eta 0:01:15 lr 0.000134	time 0.8790 (0.8969)	loss 0.5746 (0.4635)	grad_norm 2.1795 (2.9031)	mem 20675MB
[2025-04-02 20:14:52 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][152/234]	eta 0:01:13 lr 0.000133	time 0.8804 (0.8967)	loss 0.3485 (0.4617)	grad_norm 5.6570 (2.9207)	mem 20675MB
[2025-04-02 20:14:53 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][154/234]	eta 0:01:11 lr 0.000133	time 0.8786 (0.8966)	loss 0.5347 (0.4630)	grad_norm 3.8530 (2.9254)	mem 20675MB
[2025-04-02 20:14:55 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][156/234]	eta 0:01:09 lr 0.000133	time 0.8792 (0.8965)	loss 0.5051 (0.4631)	grad_norm 3.2310 (2.9240)	mem 20675MB
[2025-04-02 20:14:57 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][158/234]	eta 0:01:08 lr 0.000132	time 0.8803 (0.8963)	loss 0.4694 (0.4632)	grad_norm 2.4851 (2.9167)	mem 20675MB
[2025-04-02 20:14:59 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][160/234]	eta 0:01:06 lr 0.000132	time 0.8783 (0.8961)	loss 0.3295 (0.4622)	grad_norm 3.2789 (2.9183)	mem 20675MB
[2025-04-02 20:15:01 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][162/234]	eta 0:01:04 lr 0.000132	time 0.8889 (0.8960)	loss 0.3164 (0.4615)	grad_norm 3.4036 (2.9196)	mem 20675MB
[2025-04-02 20:15:02 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][164/234]	eta 0:01:02 lr 0.000131	time 0.8788 (0.8958)	loss 0.4890 (0.4608)	grad_norm 2.6527 (2.9250)	mem 20675MB
[2025-04-02 20:15:04 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][166/234]	eta 0:01:00 lr 0.000131	time 0.8861 (0.8956)	loss 0.3142 (0.4601)	grad_norm 4.8845 (2.9420)	mem 20675MB
[2025-04-02 20:15:06 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][168/234]	eta 0:00:59 lr 0.000131	time 0.8783 (0.8955)	loss 0.4552 (0.4603)	grad_norm 1.9818 (2.9358)	mem 20675MB
[2025-04-02 20:15:08 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][170/234]	eta 0:00:57 lr 0.000130	time 0.8785 (0.8953)	loss 0.6005 (0.4612)	grad_norm 4.3381 (2.9446)	mem 20675MB
[2025-04-02 20:15:09 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][172/234]	eta 0:00:55 lr 0.000130	time 0.8790 (0.8951)	loss 0.4917 (0.4607)	grad_norm 2.6803 (2.9689)	mem 20675MB
[2025-04-02 20:15:11 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][174/234]	eta 0:00:53 lr 0.000130	time 0.8787 (0.8949)	loss 0.5278 (0.4610)	grad_norm 2.2892 (2.9703)	mem 20675MB
[2025-04-02 20:15:13 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][176/234]	eta 0:00:51 lr 0.000129	time 0.8790 (0.8948)	loss 0.5870 (0.4620)	grad_norm 2.4913 (2.9647)	mem 20675MB
[2025-04-02 20:15:15 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][178/234]	eta 0:00:50 lr 0.000129	time 0.8843 (0.8946)	loss 0.3165 (0.4605)	grad_norm 3.8625 (2.9732)	mem 20675MB
[2025-04-02 20:15:16 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][180/234]	eta 0:00:48 lr 0.000129	time 0.8788 (0.8945)	loss 0.5677 (0.4614)	grad_norm 2.5777 (2.9692)	mem 20675MB
[2025-04-02 20:15:18 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][182/234]	eta 0:00:46 lr 0.000128	time 0.8789 (0.8943)	loss 0.4212 (0.4619)	grad_norm 3.3369 (2.9750)	mem 20675MB
[2025-04-02 20:15:20 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][184/234]	eta 0:00:44 lr 0.000128	time 0.8782 (0.8942)	loss 0.4911 (0.4624)	grad_norm 2.2130 (2.9681)	mem 20675MB
[2025-04-02 20:15:22 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][186/234]	eta 0:00:42 lr 0.000128	time 0.8791 (0.8940)	loss 0.5372 (0.4626)	grad_norm 2.5679 (2.9732)	mem 20675MB
[2025-04-02 20:15:23 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][188/234]	eta 0:00:41 lr 0.000127	time 0.8781 (0.8939)	loss 0.3860 (0.4628)	grad_norm 4.3366 (2.9802)	mem 20675MB
[2025-04-02 20:15:25 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][190/234]	eta 0:00:39 lr 0.000127	time 0.8786 (0.8937)	loss 0.3750 (0.4615)	grad_norm 2.9200 (2.9793)	mem 20675MB
[2025-04-02 20:15:27 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][192/234]	eta 0:00:37 lr 0.000127	time 0.8784 (0.8936)	loss 0.3517 (0.4612)	grad_norm 2.7797 (2.9779)	mem 20675MB
[2025-04-02 20:15:29 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][194/234]	eta 0:00:35 lr 0.000126	time 0.8780 (0.8934)	loss 0.4186 (0.4611)	grad_norm 1.8657 (2.9671)	mem 20675MB
[2025-04-02 20:15:30 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][196/234]	eta 0:00:33 lr 0.000126	time 0.8837 (0.8933)	loss 0.5482 (0.4615)	grad_norm 2.2515 (2.9577)	mem 20675MB
[2025-04-02 20:15:32 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][198/234]	eta 0:00:32 lr 0.000126	time 0.8782 (0.8931)	loss 0.5527 (0.4614)	grad_norm 2.9807 (2.9588)	mem 20675MB
[2025-04-02 20:15:34 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][200/234]	eta 0:00:30 lr 0.000125	time 0.8786 (0.8930)	loss 0.4585 (0.4610)	grad_norm 3.4259 (2.9651)	mem 20675MB
[2025-04-02 20:15:36 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][202/234]	eta 0:00:28 lr 0.000125	time 0.8812 (0.8929)	loss 0.4993 (0.4612)	grad_norm 2.8237 (2.9636)	mem 20675MB
[2025-04-02 20:15:38 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][204/234]	eta 0:00:26 lr 0.000125	time 0.8788 (0.8928)	loss 0.5282 (0.4621)	grad_norm 3.7078 (2.9634)	mem 20675MB
[2025-04-02 20:15:39 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][206/234]	eta 0:00:24 lr 0.000124	time 0.8778 (0.8926)	loss 0.5362 (0.4630)	grad_norm 2.6444 (2.9635)	mem 20675MB
[2025-04-02 20:15:41 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][208/234]	eta 0:00:23 lr 0.000124	time 0.8790 (0.8925)	loss 0.4758 (0.4628)	grad_norm 2.7142 (2.9608)	mem 20675MB
[2025-04-02 20:15:43 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][210/234]	eta 0:00:21 lr 0.000124	time 0.8771 (0.8924)	loss 0.3807 (0.4618)	grad_norm 2.9198 (2.9600)	mem 20675MB
[2025-04-02 20:15:45 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][212/234]	eta 0:00:19 lr 0.000123	time 0.8808 (0.8923)	loss 0.5443 (0.4619)	grad_norm 2.2768 (2.9530)	mem 20675MB
[2025-04-02 20:15:46 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][214/234]	eta 0:00:17 lr 0.000123	time 0.8950 (0.8923)	loss 0.4353 (0.4613)	grad_norm 2.3553 (2.9526)	mem 20675MB
[2025-04-02 20:15:48 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][216/234]	eta 0:00:16 lr 0.000123	time 0.8858 (0.8922)	loss 0.5347 (0.4621)	grad_norm 2.7718 (2.9512)	mem 20675MB
[2025-04-02 20:15:50 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][218/234]	eta 0:00:14 lr 0.000122	time 0.8771 (0.8921)	loss 0.3292 (0.4614)	grad_norm 2.9568 (2.9485)	mem 20675MB
[2025-04-02 20:15:52 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][220/234]	eta 0:00:12 lr 0.000122	time 0.8772 (0.8919)	loss 0.3213 (0.4614)	grad_norm 3.8722 (2.9533)	mem 20675MB
[2025-04-02 20:15:53 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][222/234]	eta 0:00:10 lr 0.000122	time 0.8810 (0.8918)	loss 0.4242 (0.4608)	grad_norm 3.5896 (2.9588)	mem 20675MB
[2025-04-02 20:15:55 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][224/234]	eta 0:00:08 lr 0.000121	time 0.8809 (0.8917)	loss 0.4771 (0.4611)	grad_norm 2.0234 (2.9504)	mem 20675MB
[2025-04-02 20:15:57 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][226/234]	eta 0:00:07 lr 0.000121	time 0.8763 (0.8916)	loss 0.4246 (0.4611)	grad_norm 3.0082 (2.9499)	mem 20675MB
[2025-04-02 20:15:59 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][228/234]	eta 0:00:05 lr 0.000121	time 0.8773 (0.8915)	loss 0.3156 (0.4608)	grad_norm 3.8892 (2.9503)	mem 20675MB
[2025-04-02 20:16:00 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][230/234]	eta 0:00:03 lr 0.000120	time 0.8769 (0.8914)	loss 0.4560 (0.4604)	grad_norm 2.4274 (2.9451)	mem 20675MB
[2025-04-02 20:16:02 simmim_finetune] (main_finetune.py 252): INFO Train: [23/30][232/234]	eta 0:00:01 lr 0.000120	time 0.8791 (0.8913)	loss 0.5666 (0.4603)	grad_norm 3.1849 (2.9444)	mem 20675MB
[2025-04-02 20:16:03 simmim_finetune] (main_finetune.py 260): INFO EPOCH 23 training takes 0:03:28
[2025-04-02 20:16:05 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.746 (1.746)	Loss 0.7074 (0.7074)	Acc@1 64.844 (64.844)	Mem 20675MB
[2025-04-02 20:16:05 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.823
[2025-04-02 20:16:05 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.8%
[2025-04-02 20:16:05 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:16:05 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [6.6801693820116e-07, 6.6801693820116e-07, 9.059753176875197e-07, 9.059753176875197e-07, 1.2720651322819197e-06, 1.2720651322819197e-06, 1.8352802316579195e-06, 1.8352802316579195e-06, 2.7017649999286885e-06, 2.7017649999286885e-06, 4.034818489576025e-06, 4.034818489576025e-06, 6.085670012110389e-06, 6.085670012110389e-06, 9.24082620062479e-06, 9.24082620062479e-06, 1.4094912644493108e-05, 1.4094912644493108e-05, 2.156273794275206e-05, 2.156273794275206e-05, 3.305169994007351e-05, 3.305169994007351e-05, 5.072702608979883e-05, 5.072702608979883e-05, 7.791983555091471e-05, 7.791983555091471e-05, 0.00011975492702955451, 0.00011975492702955451]
[2025-04-02 20:16:08 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][0/234]	eta 0:12:22 lr 0.000120	time 3.1738 (3.1738)	loss 0.3120 (0.3120)	grad_norm 2.5013 (2.5013)	mem 20675MB
[2025-04-02 20:16:10 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][2/234]	eta 0:06:22 lr 0.000119	time 0.8777 (1.6476)	loss 0.5192 (0.4262)	grad_norm 4.3194 (3.3513)	mem 20675MB
[2025-04-02 20:16:12 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][4/234]	eta 0:05:08 lr 0.000119	time 0.8792 (1.3406)	loss 0.4294 (0.4461)	grad_norm 4.5077 (3.5262)	mem 20675MB
[2025-04-02 20:16:14 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][6/234]	eta 0:04:35 lr 0.000119	time 0.8784 (1.2088)	loss 0.4095 (0.4447)	grad_norm 2.8424 (3.2297)	mem 20675MB
[2025-04-02 20:16:15 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][8/234]	eta 0:04:16 lr 0.000118	time 0.8795 (1.1357)	loss 0.5252 (0.4484)	grad_norm 2.5115 (3.3727)	mem 20675MB
[2025-04-02 20:16:17 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][10/234]	eta 0:04:03 lr 0.000118	time 0.8780 (1.0890)	loss 0.5566 (0.4635)	grad_norm 3.4586 (3.3587)	mem 20675MB
[2025-04-02 20:16:19 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][12/234]	eta 0:03:54 lr 0.000118	time 0.8792 (1.0567)	loss 0.5352 (0.4766)	grad_norm 2.7729 (3.2445)	mem 20675MB
[2025-04-02 20:16:21 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][14/234]	eta 0:03:47 lr 0.000117	time 0.8854 (1.0336)	loss 0.5243 (0.4775)	grad_norm 3.0733 (3.2585)	mem 20675MB
[2025-04-02 20:16:22 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][16/234]	eta 0:03:41 lr 0.000117	time 0.8775 (1.0154)	loss 0.5731 (0.4850)	grad_norm 2.1863 (3.1925)	mem 20675MB
[2025-04-02 20:16:24 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][18/234]	eta 0:03:36 lr 0.000117	time 0.8804 (1.0012)	loss 0.5122 (0.4860)	grad_norm 2.0814 (3.1215)	mem 20675MB
[2025-04-02 20:16:26 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][20/234]	eta 0:03:31 lr 0.000116	time 0.8776 (0.9896)	loss 0.3626 (0.4850)	grad_norm 2.5167 (3.0526)	mem 20675MB
[2025-04-02 20:16:28 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][22/234]	eta 0:03:27 lr 0.000116	time 0.8780 (0.9801)	loss 0.4671 (0.4796)	grad_norm 2.8684 (3.0149)	mem 20675MB
[2025-04-02 20:16:29 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][24/234]	eta 0:03:24 lr 0.000116	time 0.8776 (0.9719)	loss 0.4927 (0.4832)	grad_norm 2.4840 (2.9788)	mem 20675MB
[2025-04-02 20:16:31 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][26/234]	eta 0:03:20 lr 0.000115	time 0.8787 (0.9651)	loss 0.4822 (0.4834)	grad_norm 2.2527 (2.9332)	mem 20675MB
[2025-04-02 20:16:33 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][28/234]	eta 0:03:17 lr 0.000115	time 0.8806 (0.9592)	loss 0.3130 (0.4742)	grad_norm 2.4515 (2.9285)	mem 20675MB
[2025-04-02 20:16:35 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][30/234]	eta 0:03:14 lr 0.000115	time 0.8821 (0.9543)	loss 0.3943 (0.4734)	grad_norm 3.7565 (2.9549)	mem 20675MB
[2025-04-02 20:16:36 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][32/234]	eta 0:03:11 lr 0.000114	time 0.8788 (0.9498)	loss 0.3682 (0.4658)	grad_norm 3.1461 (2.9704)	mem 20675MB
[2025-04-02 20:16:38 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][34/234]	eta 0:03:09 lr 0.000114	time 0.8872 (0.9461)	loss 0.5473 (0.4693)	grad_norm 2.6779 (2.9506)	mem 20675MB
[2025-04-02 20:16:40 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][36/234]	eta 0:03:06 lr 0.000114	time 0.8906 (0.9432)	loss 0.3629 (0.4675)	grad_norm 3.0196 (2.9377)	mem 20675MB
[2025-04-02 20:16:42 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][38/234]	eta 0:03:04 lr 0.000113	time 0.8769 (0.9407)	loss 0.4709 (0.4693)	grad_norm 2.8836 (2.9097)	mem 20675MB
[2025-04-02 20:16:44 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][40/234]	eta 0:03:01 lr 0.000113	time 0.8781 (0.9378)	loss 0.3359 (0.4654)	grad_norm 4.0731 (2.9129)	mem 20675MB
[2025-04-02 20:16:45 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][42/234]	eta 0:02:59 lr 0.000113	time 0.8791 (0.9358)	loss 0.5390 (0.4708)	grad_norm 1.9196 (2.9017)	mem 20675MB
[2025-04-02 20:16:47 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][44/234]	eta 0:02:57 lr 0.000112	time 0.8776 (0.9333)	loss 0.3469 (0.4688)	grad_norm 3.0337 (2.8902)	mem 20675MB
[2025-04-02 20:16:49 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][46/234]	eta 0:02:55 lr 0.000112	time 0.8788 (0.9310)	loss 0.3920 (0.4669)	grad_norm 2.9149 (2.8898)	mem 20675MB
[2025-04-02 20:16:51 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][48/234]	eta 0:02:52 lr 0.000112	time 0.8778 (0.9289)	loss 0.4361 (0.4644)	grad_norm 2.6742 (2.8911)	mem 20675MB
[2025-04-02 20:16:52 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][50/234]	eta 0:02:50 lr 0.000111	time 0.8800 (0.9272)	loss 0.4549 (0.4663)	grad_norm 2.5051 (2.8790)	mem 20675MB
[2025-04-02 20:16:54 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][52/234]	eta 0:02:48 lr 0.000111	time 0.8783 (0.9254)	loss 0.3881 (0.4635)	grad_norm 2.7302 (2.8931)	mem 20675MB
[2025-04-02 20:16:56 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][54/234]	eta 0:02:46 lr 0.000111	time 0.8805 (0.9238)	loss 0.4893 (0.4659)	grad_norm 3.1138 (2.8999)	mem 20675MB
[2025-04-02 20:16:58 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][56/234]	eta 0:02:44 lr 0.000111	time 0.8800 (0.9223)	loss 0.5638 (0.4656)	grad_norm 2.3053 (2.8937)	mem 20675MB
[2025-04-02 20:16:59 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][58/234]	eta 0:02:42 lr 0.000110	time 0.8776 (0.9209)	loss 0.5382 (0.4656)	grad_norm 2.7469 (2.8814)	mem 20675MB
[2025-04-02 20:17:01 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][60/234]	eta 0:02:40 lr 0.000110	time 0.8773 (0.9196)	loss 0.5176 (0.4663)	grad_norm 2.1934 (2.8541)	mem 20675MB
[2025-04-02 20:17:03 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][62/234]	eta 0:02:37 lr 0.000110	time 0.8863 (0.9184)	loss 0.4503 (0.4669)	grad_norm 3.2825 (2.8542)	mem 20675MB
[2025-04-02 20:17:05 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][64/234]	eta 0:02:35 lr 0.000109	time 0.8825 (0.9173)	loss 0.5172 (0.4662)	grad_norm 3.8103 (2.8671)	mem 20675MB
[2025-04-02 20:17:07 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][66/234]	eta 0:02:33 lr 0.000109	time 0.8786 (0.9162)	loss 0.5598 (0.4685)	grad_norm 1.8704 (2.8566)	mem 20675MB
[2025-04-02 20:17:08 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][68/234]	eta 0:02:31 lr 0.000109	time 0.8785 (0.9152)	loss 0.4368 (0.4690)	grad_norm 2.8962 (2.8578)	mem 20675MB
[2025-04-02 20:17:10 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][70/234]	eta 0:02:29 lr 0.000108	time 0.8787 (0.9142)	loss 0.5135 (0.4699)	grad_norm 2.2612 (2.8456)	mem 20675MB
[2025-04-02 20:17:12 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][72/234]	eta 0:02:27 lr 0.000108	time 0.8789 (0.9132)	loss 0.4167 (0.4698)	grad_norm 3.2594 (2.8401)	mem 20675MB
[2025-04-02 20:17:14 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][74/234]	eta 0:02:26 lr 0.000108	time 0.8886 (0.9125)	loss 0.5292 (0.4695)	grad_norm 2.4201 (2.8271)	mem 20675MB
[2025-04-02 20:17:15 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][76/234]	eta 0:02:24 lr 0.000107	time 0.8786 (0.9117)	loss 0.5187 (0.4704)	grad_norm 2.0289 (2.8137)	mem 20675MB
[2025-04-02 20:17:17 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][78/234]	eta 0:02:22 lr 0.000107	time 0.8786 (0.9109)	loss 0.2856 (0.4684)	grad_norm 3.3256 (2.8104)	mem 20675MB
[2025-04-02 20:17:19 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][80/234]	eta 0:02:20 lr 0.000107	time 0.8786 (0.9102)	loss 0.4425 (0.4689)	grad_norm 2.1405 (2.8000)	mem 20675MB
[2025-04-02 20:17:21 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][82/234]	eta 0:02:18 lr 0.000106	time 0.8794 (0.9095)	loss 0.5620 (0.4706)	grad_norm 1.8316 (2.7873)	mem 20675MB
[2025-04-02 20:17:22 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][84/234]	eta 0:02:16 lr 0.000106	time 0.8783 (0.9088)	loss 0.5227 (0.4691)	grad_norm 1.7077 (2.7825)	mem 20675MB
[2025-04-02 20:17:24 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][86/234]	eta 0:02:14 lr 0.000106	time 0.8779 (0.9081)	loss 0.5409 (0.4715)	grad_norm 2.6008 (2.7797)	mem 20675MB
[2025-04-02 20:17:26 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][88/234]	eta 0:02:12 lr 0.000106	time 0.8773 (0.9074)	loss 0.4058 (0.4716)	grad_norm 3.6855 (2.7987)	mem 20675MB
[2025-04-02 20:17:28 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][90/234]	eta 0:02:10 lr 0.000105	time 0.8793 (0.9068)	loss 0.3632 (0.4708)	grad_norm 2.3446 (2.7898)	mem 20675MB
[2025-04-02 20:17:29 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][92/234]	eta 0:02:08 lr 0.000105	time 0.8777 (0.9062)	loss 0.2807 (0.4694)	grad_norm 2.7250 (2.7885)	mem 20675MB
[2025-04-02 20:17:31 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][94/234]	eta 0:02:06 lr 0.000105	time 0.8782 (0.9056)	loss 0.4416 (0.4676)	grad_norm 2.5933 (2.8037)	mem 20675MB
[2025-04-02 20:17:33 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][96/234]	eta 0:02:04 lr 0.000104	time 0.8779 (0.9051)	loss 0.5894 (0.4687)	grad_norm 2.5658 (2.7924)	mem 20675MB
[2025-04-02 20:17:35 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][98/234]	eta 0:02:03 lr 0.000104	time 0.8772 (0.9045)	loss 0.5388 (0.4679)	grad_norm 2.5578 (2.8004)	mem 20675MB
[2025-04-02 20:17:36 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][100/234]	eta 0:02:01 lr 0.000104	time 0.8774 (0.9040)	loss 0.3583 (0.4665)	grad_norm 3.5802 (2.7985)	mem 20675MB
[2025-04-02 20:17:38 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][102/234]	eta 0:01:59 lr 0.000103	time 0.8767 (0.9035)	loss 0.4988 (0.4656)	grad_norm 2.2358 (2.8033)	mem 20675MB
[2025-04-02 20:17:40 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][104/234]	eta 0:01:57 lr 0.000103	time 0.8790 (0.9030)	loss 0.4862 (0.4657)	grad_norm 2.3112 (2.7927)	mem 20675MB
[2025-04-02 20:17:42 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][106/234]	eta 0:01:55 lr 0.000103	time 0.8781 (0.9026)	loss 0.4226 (0.4650)	grad_norm 2.5895 (2.7923)	mem 20675MB
[2025-04-02 20:17:43 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][108/234]	eta 0:01:53 lr 0.000102	time 0.8774 (0.9021)	loss 0.4062 (0.4639)	grad_norm 2.7170 (2.7954)	mem 20675MB
[2025-04-02 20:17:45 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][110/234]	eta 0:01:51 lr 0.000102	time 0.8769 (0.9017)	loss 0.4897 (0.4654)	grad_norm 2.0590 (2.7982)	mem 20675MB
[2025-04-02 20:17:47 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][112/234]	eta 0:01:49 lr 0.000102	time 0.8769 (0.9013)	loss 0.4544 (0.4652)	grad_norm 1.9890 (2.7888)	mem 20675MB
[2025-04-02 20:17:49 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][114/234]	eta 0:01:48 lr 0.000102	time 0.8765 (0.9009)	loss 0.4179 (0.4635)	grad_norm 6.7408 (2.8205)	mem 20675MB
[2025-04-02 20:17:50 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][116/234]	eta 0:01:46 lr 0.000101	time 0.8767 (0.9005)	loss 0.3662 (0.4623)	grad_norm 3.6974 (2.8223)	mem 20675MB
[2025-04-02 20:17:52 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][118/234]	eta 0:01:44 lr 0.000101	time 0.8772 (0.9001)	loss 0.5303 (0.4632)	grad_norm 2.1520 (2.8126)	mem 20675MB
[2025-04-02 20:17:54 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][120/234]	eta 0:01:42 lr 0.000101	time 0.8768 (0.8997)	loss 0.3798 (0.4629)	grad_norm 3.0242 (2.8183)	mem 20675MB
[2025-04-02 20:17:56 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][122/234]	eta 0:01:40 lr 0.000100	time 0.8765 (0.8994)	loss 0.4791 (0.4635)	grad_norm 2.4388 (2.8120)	mem 20675MB
[2025-04-02 20:17:58 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][124/234]	eta 0:01:38 lr 0.000100	time 0.8765 (0.8990)	loss 0.5190 (0.4633)	grad_norm 3.5917 (2.8157)	mem 20675MB
[2025-04-02 20:17:59 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][126/234]	eta 0:01:37 lr 0.000100	time 0.8776 (0.8987)	loss 0.3454 (0.4624)	grad_norm 3.3060 (2.8117)	mem 20675MB
[2025-04-02 20:18:01 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][128/234]	eta 0:01:35 lr 0.000099	time 0.8766 (0.8983)	loss 0.4007 (0.4613)	grad_norm 3.2363 (2.8230)	mem 20675MB
[2025-04-02 20:18:03 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][130/234]	eta 0:01:33 lr 0.000099	time 0.8765 (0.8980)	loss 0.5042 (0.4618)	grad_norm 2.7156 (2.8203)	mem 20675MB
[2025-04-02 20:18:05 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][132/234]	eta 0:01:31 lr 0.000099	time 0.8776 (0.8977)	loss 0.5439 (0.4629)	grad_norm 2.4654 (2.8174)	mem 20675MB
[2025-04-02 20:18:06 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][134/234]	eta 0:01:29 lr 0.000098	time 0.8769 (0.8974)	loss 0.4469 (0.4622)	grad_norm 2.6295 (2.8247)	mem 20675MB
[2025-04-02 20:18:08 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][136/234]	eta 0:01:27 lr 0.000098	time 0.8769 (0.8971)	loss 0.4113 (0.4623)	grad_norm 1.9802 (2.8165)	mem 20675MB
[2025-04-02 20:18:10 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][138/234]	eta 0:01:26 lr 0.000098	time 0.8765 (0.8969)	loss 0.4521 (0.4619)	grad_norm 2.6422 (2.8186)	mem 20675MB
[2025-04-02 20:18:12 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][140/234]	eta 0:01:24 lr 0.000098	time 0.8766 (0.8966)	loss 0.4765 (0.4619)	grad_norm 2.5502 (2.8233)	mem 20675MB
[2025-04-02 20:18:13 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][142/234]	eta 0:01:22 lr 0.000097	time 0.8776 (0.8963)	loss 0.5697 (0.4618)	grad_norm 2.1665 (2.8190)	mem 20675MB
[2025-04-02 20:18:15 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][144/234]	eta 0:01:20 lr 0.000097	time 0.8775 (0.8961)	loss 0.5157 (0.4627)	grad_norm 2.8582 (2.8165)	mem 20675MB
[2025-04-02 20:18:17 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][146/234]	eta 0:01:18 lr 0.000097	time 0.8770 (0.8958)	loss 0.5567 (0.4637)	grad_norm 2.6438 (2.8132)	mem 20675MB
[2025-04-02 20:18:19 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][148/234]	eta 0:01:17 lr 0.000096	time 0.8769 (0.8956)	loss 0.3654 (0.4630)	grad_norm 3.3746 (2.8119)	mem 20675MB
[2025-04-02 20:18:20 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][150/234]	eta 0:01:15 lr 0.000096	time 0.8766 (0.8953)	loss 0.4224 (0.4625)	grad_norm 2.8548 (2.8111)	mem 20675MB
[2025-04-02 20:18:22 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][152/234]	eta 0:01:13 lr 0.000096	time 0.8766 (0.8951)	loss 0.5260 (0.4624)	grad_norm 2.3196 (2.8094)	mem 20675MB
[2025-04-02 20:18:24 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][154/234]	eta 0:01:11 lr 0.000095	time 0.8777 (0.8949)	loss 0.3759 (0.4618)	grad_norm 3.7149 (2.8145)	mem 20675MB
[2025-04-02 20:18:26 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][156/234]	eta 0:01:09 lr 0.000095	time 0.8772 (0.8947)	loss 0.3315 (0.4615)	grad_norm 3.0172 (2.8121)	mem 20675MB
[2025-04-02 20:18:27 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][158/234]	eta 0:01:07 lr 0.000095	time 0.8763 (0.8945)	loss 0.3948 (0.4617)	grad_norm 3.5895 (2.8152)	mem 20675MB
[2025-04-02 20:18:29 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][160/234]	eta 0:01:06 lr 0.000095	time 0.8775 (0.8943)	loss 0.5568 (0.4626)	grad_norm 3.7820 (2.8207)	mem 20675MB
[2025-04-02 20:18:31 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][162/234]	eta 0:01:04 lr 0.000094	time 0.8777 (0.8941)	loss 0.3608 (0.4613)	grad_norm 3.0176 (2.8304)	mem 20675MB
[2025-04-02 20:18:33 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][164/234]	eta 0:01:02 lr 0.000094	time 0.8775 (0.8939)	loss 0.5164 (0.4617)	grad_norm 2.5380 (2.8261)	mem 20675MB
[2025-04-02 20:18:34 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][166/234]	eta 0:01:00 lr 0.000094	time 0.8781 (0.8937)	loss 0.4132 (0.4614)	grad_norm 3.4630 (2.8276)	mem 20675MB
[2025-04-02 20:18:36 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][168/234]	eta 0:00:58 lr 0.000093	time 0.8769 (0.8935)	loss 0.5213 (0.4622)	grad_norm 2.4792 (2.8245)	mem 20675MB
[2025-04-02 20:18:38 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][170/234]	eta 0:00:57 lr 0.000093	time 0.8783 (0.8933)	loss 0.3311 (0.4615)	grad_norm 3.1510 (2.8263)	mem 20675MB
[2025-04-02 20:18:40 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][172/234]	eta 0:00:55 lr 0.000093	time 0.8769 (0.8931)	loss 0.5224 (0.4611)	grad_norm 2.1264 (2.8241)	mem 20675MB
[2025-04-02 20:18:41 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][174/234]	eta 0:00:53 lr 0.000093	time 0.8777 (0.8930)	loss 0.4792 (0.4616)	grad_norm 2.4909 (2.8184)	mem 20675MB
[2025-04-02 20:18:43 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][176/234]	eta 0:00:51 lr 0.000092	time 0.8780 (0.8928)	loss 0.4696 (0.4609)	grad_norm 3.0072 (2.8222)	mem 20675MB
[2025-04-02 20:18:45 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][178/234]	eta 0:00:49 lr 0.000092	time 0.8776 (0.8926)	loss 0.5693 (0.4612)	grad_norm 3.3710 (2.8282)	mem 20675MB
[2025-04-02 20:18:47 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][180/234]	eta 0:00:48 lr 0.000092	time 0.8767 (0.8925)	loss 0.4544 (0.4609)	grad_norm 3.1264 (2.8243)	mem 20675MB
[2025-04-02 20:18:48 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][182/234]	eta 0:00:46 lr 0.000091	time 0.8775 (0.8923)	loss 0.5309 (0.4613)	grad_norm 2.7315 (2.8256)	mem 20675MB
[2025-04-02 20:18:50 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][184/234]	eta 0:00:44 lr 0.000091	time 0.8780 (0.8922)	loss 0.5575 (0.4619)	grad_norm 2.6781 (2.8221)	mem 20675MB
[2025-04-02 20:18:52 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][186/234]	eta 0:00:42 lr 0.000091	time 0.8773 (0.8920)	loss 0.4196 (0.4616)	grad_norm 4.3863 (2.8270)	mem 20675MB
[2025-04-02 20:18:54 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][188/234]	eta 0:00:41 lr 0.000091	time 0.8770 (0.8919)	loss 0.5604 (0.4616)	grad_norm 3.2141 (2.8320)	mem 20675MB
[2025-04-02 20:18:55 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][190/234]	eta 0:00:39 lr 0.000090	time 0.8776 (0.8917)	loss 0.5244 (0.4621)	grad_norm 1.7790 (2.8243)	mem 20675MB
[2025-04-02 20:18:57 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][192/234]	eta 0:00:37 lr 0.000090	time 0.8774 (0.8916)	loss 0.5078 (0.4627)	grad_norm 4.4963 (2.8331)	mem 20675MB
[2025-04-02 20:18:59 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][194/234]	eta 0:00:35 lr 0.000090	time 0.8779 (0.8915)	loss 0.4872 (0.4627)	grad_norm 2.8513 (2.8350)	mem 20675MB
[2025-04-02 20:19:01 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][196/234]	eta 0:00:33 lr 0.000089	time 0.8766 (0.8913)	loss 0.3468 (0.4616)	grad_norm 2.7236 (2.8422)	mem 20675MB
[2025-04-02 20:19:02 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][198/234]	eta 0:00:32 lr 0.000089	time 0.8776 (0.8912)	loss 0.3935 (0.4612)	grad_norm 4.9681 (2.8474)	mem 20675MB
[2025-04-02 20:19:04 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][200/234]	eta 0:00:30 lr 0.000089	time 0.8770 (0.8911)	loss 0.4300 (0.4604)	grad_norm 2.0012 (2.8551)	mem 20675MB
[2025-04-02 20:19:06 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][202/234]	eta 0:00:28 lr 0.000088	time 0.8780 (0.8910)	loss 0.5604 (0.4616)	grad_norm 2.9655 (2.8555)	mem 20675MB
[2025-04-02 20:19:08 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][204/234]	eta 0:00:26 lr 0.000088	time 0.8772 (0.8908)	loss 0.4825 (0.4620)	grad_norm 2.9369 (2.8553)	mem 20675MB
[2025-04-02 20:19:10 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][206/234]	eta 0:00:24 lr 0.000088	time 0.8764 (0.8907)	loss 0.4914 (0.4620)	grad_norm 2.6697 (2.8505)	mem 20675MB
[2025-04-02 20:19:11 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][208/234]	eta 0:00:23 lr 0.000088	time 0.8772 (0.8906)	loss 0.4380 (0.4623)	grad_norm 2.2190 (2.8563)	mem 20675MB
[2025-04-02 20:19:13 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][210/234]	eta 0:00:21 lr 0.000087	time 0.8776 (0.8905)	loss 0.5401 (0.4628)	grad_norm 2.6054 (2.8510)	mem 20675MB
[2025-04-02 20:19:15 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][212/234]	eta 0:00:19 lr 0.000087	time 0.8766 (0.8903)	loss 0.4869 (0.4632)	grad_norm 2.0506 (2.8444)	mem 20675MB
[2025-04-02 20:19:17 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][214/234]	eta 0:00:17 lr 0.000087	time 0.8768 (0.8902)	loss 0.3872 (0.4634)	grad_norm 2.5639 (2.8437)	mem 20675MB
[2025-04-02 20:19:18 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][216/234]	eta 0:00:16 lr 0.000087	time 0.8771 (0.8901)	loss 0.4887 (0.4634)	grad_norm 2.8561 (2.8445)	mem 20675MB
[2025-04-02 20:19:20 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][218/234]	eta 0:00:14 lr 0.000086	time 0.8777 (0.8900)	loss 0.5593 (0.4636)	grad_norm 1.5665 (2.8384)	mem 20675MB
[2025-04-02 20:19:22 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][220/234]	eta 0:00:12 lr 0.000086	time 0.8776 (0.8899)	loss 0.5663 (0.4646)	grad_norm 2.6980 (2.8373)	mem 20675MB
[2025-04-02 20:19:24 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][222/234]	eta 0:00:10 lr 0.000086	time 0.8781 (0.8898)	loss 0.3918 (0.4636)	grad_norm 1.5301 (2.8318)	mem 20675MB
[2025-04-02 20:19:25 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][224/234]	eta 0:00:08 lr 0.000085	time 0.8774 (0.8897)	loss 0.3893 (0.4636)	grad_norm 1.9995 (2.8257)	mem 20675MB
[2025-04-02 20:19:27 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][226/234]	eta 0:00:07 lr 0.000085	time 0.8771 (0.8896)	loss 0.4838 (0.4633)	grad_norm 2.5011 (2.8224)	mem 20675MB
[2025-04-02 20:19:29 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][228/234]	eta 0:00:05 lr 0.000085	time 0.8780 (0.8895)	loss 0.6401 (0.4639)	grad_norm 3.3197 (2.8214)	mem 20675MB
[2025-04-02 20:19:31 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][230/234]	eta 0:00:03 lr 0.000085	time 0.8778 (0.8894)	loss 0.4164 (0.4639)	grad_norm 2.3647 (2.8160)	mem 20675MB
[2025-04-02 20:19:32 simmim_finetune] (main_finetune.py 252): INFO Train: [24/30][232/234]	eta 0:00:01 lr 0.000084	time 0.8778 (0.8893)	loss 0.4415 (0.4639)	grad_norm 3.3635 (2.8259)	mem 20675MB
[2025-04-02 20:19:33 simmim_finetune] (main_finetune.py 260): INFO EPOCH 24 training takes 0:03:28
[2025-04-02 20:19:34 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.068 (1.068)	Loss 0.7119 (0.7119)	Acc@1 63.281 (63.281)	Mem 20675MB
[2025-04-02 20:19:35 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 70.718
[2025-04-02 20:19:35 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 70.7%
[2025-04-02 20:19:35 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:19:35 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [5.433247410873829e-07, 5.433247410873829e-07, 7.103014199577992e-07, 7.103014199577992e-07, 9.67188618219978e-07, 9.67188618219978e-07, 1.3623996924694841e-06, 1.3623996924694841e-06, 1.9704167297764165e-06, 1.9704167297764165e-06, 2.9058275564024662e-06, 2.9058275564024662e-06, 4.344921135827157e-06, 4.344921135827157e-06, 6.558911258018989e-06, 6.558911258018989e-06, 9.965049907544885e-06, 9.965049907544885e-06, 1.5205263214507804e-05, 1.5205263214507804e-05, 2.32671298406046e-05, 2.32671298406046e-05, 3.567000157306121e-05, 3.567000157306121e-05, 5.475134269991753e-05, 5.475134269991753e-05, 8.410725212585033e-05, 8.410725212585033e-05]
[2025-04-02 20:19:37 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][0/234]	eta 0:07:48 lr 0.000084	time 2.0036 (2.0036)	loss 0.4188 (0.4188)	grad_norm 2.6133 (2.6133)	mem 20675MB
[2025-04-02 20:19:38 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][2/234]	eta 0:04:51 lr 0.000084	time 0.8788 (1.2547)	loss 0.3435 (0.4208)	grad_norm 3.2846 (2.8621)	mem 20675MB
[2025-04-02 20:19:40 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][4/234]	eta 0:04:14 lr 0.000083	time 0.8788 (1.1044)	loss 0.4911 (0.4441)	grad_norm 2.8980 (2.7820)	mem 20675MB
[2025-04-02 20:19:42 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][6/234]	eta 0:03:57 lr 0.000083	time 0.8776 (1.0397)	loss 0.4321 (0.4470)	grad_norm 3.3434 (2.8004)	mem 20675MB
[2025-04-02 20:19:44 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][8/234]	eta 0:03:46 lr 0.000083	time 0.8767 (1.0038)	loss 0.4799 (0.4596)	grad_norm 3.2464 (2.8799)	mem 20675MB
[2025-04-02 20:19:45 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][10/234]	eta 0:03:39 lr 0.000083	time 0.8771 (0.9809)	loss 0.5272 (0.4706)	grad_norm 3.9799 (2.9003)	mem 20675MB
[2025-04-02 20:19:47 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][12/234]	eta 0:03:34 lr 0.000082	time 0.8776 (0.9652)	loss 0.5176 (0.4783)	grad_norm 3.1259 (2.9330)	mem 20675MB
[2025-04-02 20:19:49 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][14/234]	eta 0:03:29 lr 0.000082	time 0.8785 (0.9536)	loss 0.5030 (0.4873)	grad_norm 2.4154 (2.8410)	mem 20675MB
[2025-04-02 20:19:51 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][16/234]	eta 0:03:25 lr 0.000082	time 0.8779 (0.9448)	loss 0.5381 (0.4904)	grad_norm 2.2979 (2.7566)	mem 20675MB
[2025-04-02 20:19:52 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][18/234]	eta 0:03:22 lr 0.000081	time 0.8767 (0.9378)	loss 0.5095 (0.4902)	grad_norm 2.0248 (2.7155)	mem 20675MB
[2025-04-02 20:19:54 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][20/234]	eta 0:03:19 lr 0.000081	time 0.8766 (0.9320)	loss 0.5107 (0.4919)	grad_norm 2.8082 (2.7445)	mem 20675MB
[2025-04-02 20:19:56 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][22/234]	eta 0:03:16 lr 0.000081	time 0.8780 (0.9274)	loss 0.4572 (0.4911)	grad_norm 2.5037 (2.7462)	mem 20675MB
[2025-04-02 20:19:58 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][24/234]	eta 0:03:13 lr 0.000081	time 0.8771 (0.9234)	loss 0.5935 (0.4966)	grad_norm 2.8577 (2.7564)	mem 20675MB
[2025-04-02 20:19:59 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][26/234]	eta 0:03:11 lr 0.000080	time 0.8779 (0.9200)	loss 0.4860 (0.4970)	grad_norm 1.8347 (2.6881)	mem 20675MB
[2025-04-02 20:20:01 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][28/234]	eta 0:03:08 lr 0.000080	time 0.8766 (0.9171)	loss 0.3560 (0.4902)	grad_norm 3.9514 (2.7099)	mem 20675MB
[2025-04-02 20:20:03 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][30/234]	eta 0:03:06 lr 0.000080	time 0.8764 (0.9145)	loss 0.5024 (0.4898)	grad_norm 2.0419 (2.6690)	mem 20675MB
[2025-04-02 20:20:05 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][32/234]	eta 0:03:04 lr 0.000080	time 0.8762 (0.9123)	loss 0.3935 (0.4874)	grad_norm 3.4982 (2.6811)	mem 20675MB
[2025-04-02 20:20:06 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][34/234]	eta 0:03:02 lr 0.000079	time 0.8765 (0.9103)	loss 0.5875 (0.4908)	grad_norm 2.9617 (2.6702)	mem 20675MB
[2025-04-02 20:20:08 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][36/234]	eta 0:02:59 lr 0.000079	time 0.8774 (0.9085)	loss 0.5635 (0.4917)	grad_norm 2.1937 (2.6367)	mem 20675MB
[2025-04-02 20:20:10 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][38/234]	eta 0:02:57 lr 0.000079	time 0.8779 (0.9070)	loss 0.4284 (0.4897)	grad_norm 3.5026 (2.6394)	mem 20675MB
[2025-04-02 20:20:12 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][40/234]	eta 0:02:55 lr 0.000078	time 0.8777 (0.9056)	loss 0.4808 (0.4864)	grad_norm 3.1598 (2.6517)	mem 20675MB
[2025-04-02 20:20:13 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][42/234]	eta 0:02:53 lr 0.000078	time 0.8772 (0.9044)	loss 0.5081 (0.4827)	grad_norm 2.0068 (2.6549)	mem 20675MB
[2025-04-02 20:20:15 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][44/234]	eta 0:02:51 lr 0.000078	time 0.8771 (0.9032)	loss 0.4923 (0.4807)	grad_norm 3.3947 (2.6788)	mem 20675MB
[2025-04-02 20:20:17 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][46/234]	eta 0:02:49 lr 0.000078	time 0.8764 (0.9021)	loss 0.3479 (0.4750)	grad_norm 2.4092 (2.7023)	mem 20675MB
[2025-04-02 20:20:19 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][48/234]	eta 0:02:47 lr 0.000077	time 0.8775 (0.9011)	loss 0.4518 (0.4728)	grad_norm 2.6333 (2.7243)	mem 20675MB
[2025-04-02 20:20:21 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][50/234]	eta 0:02:45 lr 0.000077	time 0.8767 (0.9002)	loss 0.5343 (0.4713)	grad_norm 2.6354 (2.7169)	mem 20675MB
[2025-04-02 20:20:22 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][52/234]	eta 0:02:43 lr 0.000077	time 0.8769 (0.8993)	loss 0.5322 (0.4732)	grad_norm 2.2159 (2.6970)	mem 20675MB
[2025-04-02 20:20:24 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][54/234]	eta 0:02:41 lr 0.000077	time 0.8774 (0.8986)	loss 0.4891 (0.4726)	grad_norm 2.3028 (2.6794)	mem 20675MB
[2025-04-02 20:20:26 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][56/234]	eta 0:02:39 lr 0.000076	time 0.8775 (0.8979)	loss 0.5541 (0.4728)	grad_norm 2.7277 (2.6866)	mem 20675MB
[2025-04-02 20:20:28 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][58/234]	eta 0:02:37 lr 0.000076	time 0.8779 (0.8972)	loss 0.4661 (0.4719)	grad_norm 2.7769 (2.6972)	mem 20675MB
[2025-04-02 20:20:29 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][60/234]	eta 0:02:36 lr 0.000076	time 0.8770 (0.8966)	loss 0.5070 (0.4730)	grad_norm 2.3485 (2.7001)	mem 20675MB
[2025-04-02 20:20:31 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][62/234]	eta 0:02:34 lr 0.000076	time 0.8783 (0.8960)	loss 0.5293 (0.4732)	grad_norm 2.5259 (2.6894)	mem 20675MB
[2025-04-02 20:20:33 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][64/234]	eta 0:02:32 lr 0.000075	time 0.8783 (0.8955)	loss 0.4042 (0.4733)	grad_norm 2.9152 (2.6944)	mem 20675MB
[2025-04-02 20:20:35 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][66/234]	eta 0:02:30 lr 0.000075	time 0.8774 (0.8950)	loss 0.3315 (0.4714)	grad_norm 3.3251 (2.6933)	mem 20675MB
[2025-04-02 20:20:36 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][68/234]	eta 0:02:28 lr 0.000075	time 0.8770 (0.8945)	loss 0.4759 (0.4727)	grad_norm 2.3341 (2.6808)	mem 20675MB
[2025-04-02 20:20:38 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][70/234]	eta 0:02:26 lr 0.000074	time 0.8773 (0.8940)	loss 0.5342 (0.4744)	grad_norm 3.2158 (2.6847)	mem 20675MB
[2025-04-02 20:20:40 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][72/234]	eta 0:02:24 lr 0.000074	time 0.8776 (0.8936)	loss 0.3868 (0.4730)	grad_norm 4.6437 (2.7161)	mem 20675MB
[2025-04-02 20:20:42 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][74/234]	eta 0:02:22 lr 0.000074	time 0.8768 (0.8932)	loss 0.4526 (0.4703)	grad_norm 1.9669 (2.6961)	mem 20675MB
[2025-04-02 20:20:43 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][76/234]	eta 0:02:21 lr 0.000074	time 0.8768 (0.8928)	loss 0.3201 (0.4686)	grad_norm 5.0217 (2.7183)	mem 20675MB
[2025-04-02 20:20:45 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][78/234]	eta 0:02:19 lr 0.000073	time 0.8780 (0.8924)	loss 0.5537 (0.4698)	grad_norm 3.0317 (2.7480)	mem 20675MB
[2025-04-02 20:20:47 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][80/234]	eta 0:02:17 lr 0.000073	time 0.8771 (0.8921)	loss 0.5087 (0.4715)	grad_norm 2.0978 (2.7623)	mem 20675MB
[2025-04-02 20:20:49 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][82/234]	eta 0:02:15 lr 0.000073	time 0.8771 (0.8917)	loss 0.4670 (0.4725)	grad_norm 2.8762 (2.7766)	mem 20675MB
[2025-04-02 20:20:50 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][84/234]	eta 0:02:13 lr 0.000073	time 0.8769 (0.8914)	loss 0.5899 (0.4728)	grad_norm 1.9692 (2.7873)	mem 20675MB
[2025-04-02 20:20:52 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][86/234]	eta 0:02:11 lr 0.000072	time 0.8776 (0.8911)	loss 0.4481 (0.4734)	grad_norm 2.8722 (2.7883)	mem 20675MB
[2025-04-02 20:20:54 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][88/234]	eta 0:02:10 lr 0.000072	time 0.8785 (0.8909)	loss 0.4814 (0.4738)	grad_norm 2.2397 (2.7927)	mem 20675MB
[2025-04-02 20:20:56 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][90/234]	eta 0:02:08 lr 0.000072	time 0.8790 (0.8906)	loss 0.5524 (0.4743)	grad_norm 2.5202 (2.7808)	mem 20675MB
[2025-04-02 20:20:57 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][92/234]	eta 0:02:06 lr 0.000072	time 0.8768 (0.8903)	loss 0.4307 (0.4737)	grad_norm 3.5906 (2.7883)	mem 20675MB
[2025-04-02 20:20:59 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][94/234]	eta 0:02:04 lr 0.000071	time 0.8770 (0.8901)	loss 0.3761 (0.4737)	grad_norm 3.6682 (2.7980)	mem 20675MB
[2025-04-02 20:21:01 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][96/234]	eta 0:02:02 lr 0.000071	time 0.8764 (0.8898)	loss 0.5480 (0.4730)	grad_norm 2.1397 (2.7843)	mem 20675MB
[2025-04-02 20:21:03 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][98/234]	eta 0:02:00 lr 0.000071	time 0.8774 (0.8896)	loss 0.5110 (0.4739)	grad_norm 1.9534 (2.7722)	mem 20675MB
[2025-04-02 20:21:04 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][100/234]	eta 0:01:59 lr 0.000071	time 0.8772 (0.8893)	loss 0.4573 (0.4744)	grad_norm 3.0349 (2.7722)	mem 20675MB
[2025-04-02 20:21:06 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][102/234]	eta 0:01:57 lr 0.000070	time 0.8772 (0.8891)	loss 0.4590 (0.4726)	grad_norm 3.3023 (2.7807)	mem 20675MB
[2025-04-02 20:21:08 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][104/234]	eta 0:01:55 lr 0.000070	time 0.8777 (0.8889)	loss 0.3779 (0.4720)	grad_norm 2.5148 (2.7713)	mem 20675MB
[2025-04-02 20:21:10 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][106/234]	eta 0:01:53 lr 0.000070	time 0.8776 (0.8887)	loss 0.4323 (0.4719)	grad_norm 2.1294 (2.7539)	mem 20675MB
[2025-04-02 20:21:11 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][108/234]	eta 0:01:51 lr 0.000070	time 0.8771 (0.8885)	loss 0.3803 (0.4696)	grad_norm 2.8564 (2.7511)	mem 20675MB
[2025-04-02 20:21:13 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][110/234]	eta 0:01:50 lr 0.000069	time 0.8767 (0.8883)	loss 0.4121 (0.4694)	grad_norm 2.9460 (2.7466)	mem 20675MB
[2025-04-02 20:21:15 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][112/234]	eta 0:01:48 lr 0.000069	time 0.8772 (0.8881)	loss 0.5512 (0.4692)	grad_norm 2.5337 (2.7744)	mem 20675MB
[2025-04-02 20:21:17 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][114/234]	eta 0:01:46 lr 0.000069	time 0.8789 (0.8880)	loss 0.5700 (0.4707)	grad_norm 2.6731 (2.7651)	mem 20675MB
[2025-04-02 20:21:18 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][116/234]	eta 0:01:44 lr 0.000068	time 0.8784 (0.8878)	loss 0.5020 (0.4715)	grad_norm 2.4489 (2.7632)	mem 20675MB
[2025-04-02 20:21:20 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][118/234]	eta 0:01:42 lr 0.000068	time 0.8783 (0.8877)	loss 0.5527 (0.4729)	grad_norm 2.9727 (2.7592)	mem 20675MB
[2025-04-02 20:21:22 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][120/234]	eta 0:01:41 lr 0.000068	time 0.8781 (0.8876)	loss 0.4319 (0.4714)	grad_norm 3.2550 (2.7759)	mem 20675MB
[2025-04-02 20:21:24 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][122/234]	eta 0:01:39 lr 0.000068	time 0.8781 (0.8874)	loss 0.4584 (0.4722)	grad_norm 2.4686 (2.7692)	mem 20675MB
[2025-04-02 20:21:26 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][124/234]	eta 0:01:37 lr 0.000067	time 0.8791 (0.8873)	loss 0.5086 (0.4728)	grad_norm 2.1544 (2.7592)	mem 20675MB
[2025-04-02 20:21:27 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][126/234]	eta 0:01:35 lr 0.000067	time 0.8793 (0.8872)	loss 0.5024 (0.4727)	grad_norm 2.6307 (2.7591)	mem 20675MB
[2025-04-02 20:21:29 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][128/234]	eta 0:01:34 lr 0.000067	time 0.8773 (0.8870)	loss 0.4758 (0.4722)	grad_norm 2.1718 (2.7627)	mem 20675MB
[2025-04-02 20:21:31 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][130/234]	eta 0:01:32 lr 0.000067	time 0.8776 (0.8869)	loss 0.5232 (0.4727)	grad_norm 1.9770 (2.7524)	mem 20675MB
[2025-04-02 20:21:33 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][132/234]	eta 0:01:30 lr 0.000066	time 0.8786 (0.8868)	loss 0.3934 (0.4728)	grad_norm 2.3650 (2.7509)	mem 20675MB
[2025-04-02 20:21:34 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][134/234]	eta 0:01:28 lr 0.000066	time 0.8773 (0.8867)	loss 0.4095 (0.4718)	grad_norm 3.1930 (2.7498)	mem 20675MB
[2025-04-02 20:21:36 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][136/234]	eta 0:01:26 lr 0.000066	time 0.8771 (0.8866)	loss 0.4805 (0.4719)	grad_norm 1.6208 (2.7391)	mem 20675MB
[2025-04-02 20:21:38 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][138/234]	eta 0:01:25 lr 0.000066	time 0.8766 (0.8864)	loss 0.5521 (0.4722)	grad_norm 3.1161 (2.7408)	mem 20675MB
[2025-04-02 20:21:40 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][140/234]	eta 0:01:23 lr 0.000065	time 0.8783 (0.8863)	loss 0.3455 (0.4714)	grad_norm 2.0005 (2.7336)	mem 20675MB
[2025-04-02 20:21:41 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][142/234]	eta 0:01:21 lr 0.000065	time 0.8788 (0.8862)	loss 0.4487 (0.4719)	grad_norm 3.1482 (2.7419)	mem 20675MB
[2025-04-02 20:21:43 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][144/234]	eta 0:01:19 lr 0.000065	time 0.8765 (0.8861)	loss 0.5152 (0.4715)	grad_norm 3.1328 (2.7454)	mem 20675MB
[2025-04-02 20:21:45 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][146/234]	eta 0:01:17 lr 0.000065	time 0.8775 (0.8860)	loss 0.4986 (0.4715)	grad_norm 1.6587 (2.7401)	mem 20675MB
[2025-04-02 20:21:47 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][148/234]	eta 0:01:16 lr 0.000064	time 0.8778 (0.8859)	loss 0.3515 (0.4706)	grad_norm 3.1373 (2.7401)	mem 20675MB
[2025-04-02 20:21:48 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][150/234]	eta 0:01:14 lr 0.000064	time 0.8769 (0.8858)	loss 0.5207 (0.4713)	grad_norm 3.1716 (2.7390)	mem 20675MB
[2025-04-02 20:21:50 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][152/234]	eta 0:01:12 lr 0.000064	time 0.8784 (0.8857)	loss 0.3944 (0.4703)	grad_norm 2.7543 (2.7366)	mem 20675MB
[2025-04-02 20:21:52 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][154/234]	eta 0:01:10 lr 0.000064	time 0.8790 (0.8856)	loss 0.4684 (0.4704)	grad_norm 2.1418 (2.7348)	mem 20675MB
[2025-04-02 20:21:54 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][156/234]	eta 0:01:09 lr 0.000063	time 0.8790 (0.8855)	loss 0.4505 (0.4695)	grad_norm 2.2269 (2.7317)	mem 20675MB
[2025-04-02 20:21:55 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][158/234]	eta 0:01:07 lr 0.000063	time 0.8786 (0.8855)	loss 0.5041 (0.4701)	grad_norm 1.9543 (2.7240)	mem 20675MB
[2025-04-02 20:21:57 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][160/234]	eta 0:01:05 lr 0.000063	time 0.8788 (0.8854)	loss 0.4492 (0.4701)	grad_norm 3.3124 (2.7316)	mem 20675MB
[2025-04-02 20:21:59 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][162/234]	eta 0:01:03 lr 0.000063	time 0.8779 (0.8853)	loss 0.5840 (0.4713)	grad_norm 3.4183 (2.7368)	mem 20675MB
[2025-04-02 20:22:01 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][164/234]	eta 0:01:01 lr 0.000063	time 0.8787 (0.8852)	loss 0.5049 (0.4716)	grad_norm 2.0963 (2.7276)	mem 20675MB
[2025-04-02 20:22:02 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][166/234]	eta 0:01:00 lr 0.000062	time 0.8788 (0.8852)	loss 0.4251 (0.4708)	grad_norm 2.3538 (2.7406)	mem 20675MB
[2025-04-02 20:22:04 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][168/234]	eta 0:00:58 lr 0.000062	time 0.8783 (0.8851)	loss 0.4772 (0.4711)	grad_norm 4.8257 (2.7547)	mem 20675MB
[2025-04-02 20:22:06 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][170/234]	eta 0:00:56 lr 0.000062	time 0.8764 (0.8850)	loss 0.5036 (0.4705)	grad_norm 2.6402 (2.7555)	mem 20675MB
[2025-04-02 20:22:08 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][172/234]	eta 0:00:54 lr 0.000062	time 0.8764 (0.8849)	loss 0.4876 (0.4701)	grad_norm 2.6525 (2.7549)	mem 20675MB
[2025-04-02 20:22:09 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][174/234]	eta 0:00:53 lr 0.000061	time 0.8768 (0.8849)	loss 0.4123 (0.4700)	grad_norm 3.1434 (2.7536)	mem 20675MB
[2025-04-02 20:22:11 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][176/234]	eta 0:00:51 lr 0.000061	time 0.8768 (0.8848)	loss 0.3040 (0.4694)	grad_norm 1.9546 (2.7461)	mem 20675MB
[2025-04-02 20:22:13 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][178/234]	eta 0:00:49 lr 0.000061	time 0.8771 (0.8847)	loss 0.3669 (0.4690)	grad_norm 3.5037 (2.7517)	mem 20675MB
[2025-04-02 20:22:15 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][180/234]	eta 0:00:47 lr 0.000061	time 0.8788 (0.8846)	loss 0.3031 (0.4686)	grad_norm 2.6782 (2.7491)	mem 20675MB
[2025-04-02 20:22:16 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][182/234]	eta 0:00:45 lr 0.000060	time 0.8787 (0.8846)	loss 0.3128 (0.4672)	grad_norm 2.7526 (2.7576)	mem 20675MB
[2025-04-02 20:22:18 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][184/234]	eta 0:00:44 lr 0.000060	time 0.8780 (0.8845)	loss 0.4849 (0.4671)	grad_norm 2.6547 (2.7547)	mem 20675MB
[2025-04-02 20:22:20 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][186/234]	eta 0:00:42 lr 0.000060	time 0.8780 (0.8845)	loss 0.3258 (0.4664)	grad_norm 2.9882 (2.7597)	mem 20675MB
[2025-04-02 20:22:22 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][188/234]	eta 0:00:40 lr 0.000060	time 0.8771 (0.8844)	loss 0.4878 (0.4670)	grad_norm 1.9118 (2.7532)	mem 20675MB
[2025-04-02 20:22:24 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][190/234]	eta 0:00:38 lr 0.000059	time 0.8793 (0.8843)	loss 0.3712 (0.4667)	grad_norm 3.7604 (2.7583)	mem 20675MB
[2025-04-02 20:22:25 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][192/234]	eta 0:00:37 lr 0.000059	time 0.8794 (0.8843)	loss 0.4695 (0.4665)	grad_norm 1.4926 (2.7514)	mem 20675MB
[2025-04-02 20:22:27 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][194/234]	eta 0:00:35 lr 0.000059	time 0.8782 (0.8842)	loss 0.5961 (0.4669)	grad_norm 3.0300 (2.7501)	mem 20675MB
[2025-04-02 20:22:29 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][196/234]	eta 0:00:33 lr 0.000059	time 0.8789 (0.8842)	loss 0.4096 (0.4666)	grad_norm 3.2748 (2.7488)	mem 20675MB
[2025-04-02 20:22:31 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][198/234]	eta 0:00:31 lr 0.000058	time 0.8796 (0.8841)	loss 0.3292 (0.4661)	grad_norm 2.9643 (2.7499)	mem 20675MB
[2025-04-02 20:22:32 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][200/234]	eta 0:00:30 lr 0.000058	time 0.8773 (0.8841)	loss 0.4276 (0.4661)	grad_norm 5.4848 (2.7648)	mem 20675MB
[2025-04-02 20:22:34 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][202/234]	eta 0:00:28 lr 0.000058	time 0.8778 (0.8840)	loss 0.5820 (0.4668)	grad_norm 2.4092 (2.7613)	mem 20675MB
[2025-04-02 20:22:36 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][204/234]	eta 0:00:26 lr 0.000058	time 0.8779 (0.8840)	loss 0.4637 (0.4669)	grad_norm 2.2920 (2.7597)	mem 20675MB
[2025-04-02 20:22:38 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][206/234]	eta 0:00:24 lr 0.000058	time 0.8779 (0.8839)	loss 0.4117 (0.4669)	grad_norm 3.2131 (2.7585)	mem 20675MB
[2025-04-02 20:22:39 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][208/234]	eta 0:00:22 lr 0.000057	time 0.8777 (0.8839)	loss 0.4535 (0.4664)	grad_norm 3.6845 (2.7705)	mem 20675MB
[2025-04-02 20:22:41 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][210/234]	eta 0:00:21 lr 0.000057	time 0.8790 (0.8838)	loss 0.4617 (0.4666)	grad_norm 1.7984 (2.7644)	mem 20675MB
[2025-04-02 20:22:43 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][212/234]	eta 0:00:19 lr 0.000057	time 0.8783 (0.8838)	loss 0.5007 (0.4669)	grad_norm 3.1923 (2.7650)	mem 20675MB
[2025-04-02 20:22:45 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][214/234]	eta 0:00:17 lr 0.000057	time 0.8791 (0.8837)	loss 0.5273 (0.4675)	grad_norm 1.9153 (2.7602)	mem 20675MB
[2025-04-02 20:22:46 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][216/234]	eta 0:00:15 lr 0.000056	time 0.8774 (0.8837)	loss 0.4574 (0.4675)	grad_norm 2.1991 (2.7580)	mem 20675MB
[2025-04-02 20:22:48 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][218/234]	eta 0:00:14 lr 0.000056	time 0.8774 (0.8836)	loss 0.5318 (0.4682)	grad_norm 2.0631 (2.7522)	mem 20675MB
[2025-04-02 20:22:50 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][220/234]	eta 0:00:12 lr 0.000056	time 0.8777 (0.8836)	loss 0.4499 (0.4681)	grad_norm 2.4047 (2.7490)	mem 20675MB
[2025-04-02 20:22:52 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][222/234]	eta 0:00:10 lr 0.000056	time 0.8771 (0.8836)	loss 0.5328 (0.4681)	grad_norm 2.2795 (2.7504)	mem 20675MB
[2025-04-02 20:22:53 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][224/234]	eta 0:00:08 lr 0.000055	time 0.8787 (0.8835)	loss 0.5354 (0.4681)	grad_norm 2.2200 (2.7479)	mem 20675MB
[2025-04-02 20:22:55 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][226/234]	eta 0:00:07 lr 0.000055	time 0.8780 (0.8835)	loss 0.4647 (0.4684)	grad_norm 2.3361 (2.7441)	mem 20675MB
[2025-04-02 20:22:57 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][228/234]	eta 0:00:05 lr 0.000055	time 0.8784 (0.8834)	loss 0.5060 (0.4684)	grad_norm 2.3025 (2.7451)	mem 20675MB
[2025-04-02 20:22:59 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][230/234]	eta 0:00:03 lr 0.000055	time 0.8787 (0.8834)	loss 0.4845 (0.4678)	grad_norm 2.1782 (2.7437)	mem 20675MB
[2025-04-02 20:23:00 simmim_finetune] (main_finetune.py 252): INFO Train: [25/30][232/234]	eta 0:00:01 lr 0.000055	time 0.8772 (0.8834)	loss 0.5284 (0.4682)	grad_norm 2.3279 (2.7435)	mem 20675MB
[2025-04-02 20:23:01 simmim_finetune] (main_finetune.py 260): INFO EPOCH 25 training takes 0:03:26
[2025-04-02 20:23:01 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt25.pth saving......
[2025-04-02 20:23:05 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt25.pth saved !!!
[2025-04-02 20:23:06 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.119 (1.119)	Loss 0.6994 (0.6994)	Acc@1 64.062 (64.062)	Mem 20675MB
[2025-04-02 20:23:06 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.271
[2025-04-02 20:23:06 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.3%
[2025-04-02 20:23:06 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:23:06 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [4.3936639146521827e-07, 4.3936639146521827e-07, 5.471642233812025e-07, 5.471642233812025e-07, 7.130070417134859e-07, 7.130070417134859e-07, 9.681498391477682e-07, 9.681498391477682e-07, 1.3606772198158945e-06, 1.3606772198158945e-06, 1.964565497766858e-06, 1.964565497766858e-06, 2.893624386922187e-06, 2.893624386922187e-06, 4.322945754853461e-06, 4.322945754853461e-06, 6.52190170551696e-06, 6.52190170551696e-06, 9.904910860383884e-06, 9.904910860383884e-06, 1.5109540329409917e-05, 1.5109540329409917e-05, 2.3116662589449968e-05, 2.3116662589449968e-05, 3.543531222028082e-05, 3.543531222028082e-05, 5.438708088309751e-05, 5.438708088309751e-05]
[2025-04-02 20:23:08 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][0/234]	eta 0:08:43 lr 0.000054	time 2.2354 (2.2354)	loss 0.4011 (0.4011)	grad_norm 2.2523 (2.2523)	mem 20675MB
[2025-04-02 20:23:10 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][2/234]	eta 0:05:08 lr 0.000054	time 0.8785 (1.3314)	loss 0.5017 (0.4690)	grad_norm 4.5737 (3.0641)	mem 20675MB
[2025-04-02 20:23:12 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][4/234]	eta 0:04:24 lr 0.000054	time 0.8780 (1.1508)	loss 0.3415 (0.4687)	grad_norm 2.4698 (2.8855)	mem 20675MB
[2025-04-02 20:23:14 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][6/234]	eta 0:04:04 lr 0.000054	time 0.8773 (1.0734)	loss 0.5404 (0.4856)	grad_norm 2.1180 (2.6098)	mem 20675MB
[2025-04-02 20:23:15 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][8/234]	eta 0:03:52 lr 0.000053	time 0.8782 (1.0301)	loss 0.5273 (0.4822)	grad_norm 2.2036 (2.5058)	mem 20675MB
[2025-04-02 20:23:17 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][10/234]	eta 0:03:44 lr 0.000053	time 0.8786 (1.0027)	loss 0.3643 (0.4561)	grad_norm 2.1655 (2.5347)	mem 20675MB
[2025-04-02 20:23:19 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][12/234]	eta 0:03:38 lr 0.000053	time 0.8787 (0.9840)	loss 0.4548 (0.4543)	grad_norm 1.8032 (2.4652)	mem 20675MB
[2025-04-02 20:23:21 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][14/234]	eta 0:03:33 lr 0.000053	time 0.8788 (0.9701)	loss 0.3457 (0.4541)	grad_norm 3.4829 (2.5398)	mem 20675MB
[2025-04-02 20:23:22 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][16/234]	eta 0:03:29 lr 0.000052	time 0.8771 (0.9593)	loss 0.4978 (0.4541)	grad_norm 2.8907 (2.5843)	mem 20675MB
[2025-04-02 20:23:24 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][18/234]	eta 0:03:25 lr 0.000052	time 0.8767 (0.9507)	loss 0.4848 (0.4530)	grad_norm 2.5886 (2.6207)	mem 20675MB
[2025-04-02 20:23:26 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][20/234]	eta 0:03:22 lr 0.000052	time 0.8769 (0.9441)	loss 0.3877 (0.4552)	grad_norm 3.2252 (2.6552)	mem 20675MB
[2025-04-02 20:23:28 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][22/234]	eta 0:03:18 lr 0.000052	time 0.8766 (0.9384)	loss 0.2670 (0.4414)	grad_norm 2.3205 (2.7276)	mem 20675MB
[2025-04-02 20:23:29 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][24/234]	eta 0:03:16 lr 0.000052	time 0.8801 (0.9340)	loss 0.4050 (0.4453)	grad_norm 9.3459 (3.0164)	mem 20675MB
[2025-04-02 20:23:31 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][26/234]	eta 0:03:13 lr 0.000051	time 0.8783 (0.9300)	loss 0.4854 (0.4458)	grad_norm 2.2969 (2.9997)	mem 20675MB
[2025-04-02 20:23:33 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][28/234]	eta 0:03:10 lr 0.000051	time 0.8772 (0.9264)	loss 0.3378 (0.4397)	grad_norm 4.2269 (3.0326)	mem 20675MB
[2025-04-02 20:23:35 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][30/234]	eta 0:03:08 lr 0.000051	time 0.8770 (0.9233)	loss 0.5405 (0.4430)	grad_norm 4.9011 (3.0817)	mem 20675MB
[2025-04-02 20:23:36 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][32/234]	eta 0:03:05 lr 0.000051	time 0.8768 (0.9205)	loss 0.4715 (0.4452)	grad_norm 3.4161 (3.0657)	mem 20675MB
[2025-04-02 20:23:38 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][34/234]	eta 0:03:03 lr 0.000050	time 0.8779 (0.9181)	loss 0.4835 (0.4472)	grad_norm 2.2683 (3.0162)	mem 20675MB
[2025-04-02 20:23:40 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][36/234]	eta 0:03:01 lr 0.000050	time 0.8766 (0.9159)	loss 0.6222 (0.4509)	grad_norm 2.6323 (3.0033)	mem 20675MB
[2025-04-02 20:23:42 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][38/234]	eta 0:02:59 lr 0.000050	time 0.8775 (0.9140)	loss 0.3399 (0.4472)	grad_norm 3.3893 (3.0674)	mem 20675MB
[2025-04-02 20:23:43 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][40/234]	eta 0:02:56 lr 0.000050	time 0.8771 (0.9122)	loss 0.4150 (0.4478)	grad_norm 2.6104 (3.0561)	mem 20675MB
[2025-04-02 20:23:45 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][42/234]	eta 0:02:54 lr 0.000050	time 0.8774 (0.9106)	loss 0.4536 (0.4470)	grad_norm 2.3879 (3.0380)	mem 20675MB
[2025-04-02 20:23:47 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][44/234]	eta 0:02:52 lr 0.000049	time 0.8778 (0.9092)	loss 0.5749 (0.4493)	grad_norm 3.0323 (3.0345)	mem 20675MB
[2025-04-02 20:23:49 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][46/234]	eta 0:02:50 lr 0.000049	time 0.8779 (0.9079)	loss 0.5687 (0.4511)	grad_norm 3.4499 (3.0470)	mem 20675MB
[2025-04-02 20:23:50 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][48/234]	eta 0:02:48 lr 0.000049	time 0.8766 (0.9067)	loss 0.5244 (0.4537)	grad_norm 2.6757 (3.0426)	mem 20675MB
[2025-04-02 20:23:52 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][50/234]	eta 0:02:46 lr 0.000049	time 0.8769 (0.9056)	loss 0.3340 (0.4500)	grad_norm 2.8340 (3.0443)	mem 20675MB
[2025-04-02 20:23:54 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][52/234]	eta 0:02:44 lr 0.000049	time 0.8790 (0.9046)	loss 0.3408 (0.4456)	grad_norm 3.3563 (3.0513)	mem 20675MB
[2025-04-02 20:23:56 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][54/234]	eta 0:02:42 lr 0.000048	time 0.8785 (0.9037)	loss 0.4392 (0.4459)	grad_norm 3.5859 (3.0450)	mem 20675MB
[2025-04-02 20:23:57 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][56/234]	eta 0:02:40 lr 0.000048	time 0.8792 (0.9029)	loss 0.4556 (0.4467)	grad_norm 2.3331 (3.0251)	mem 20675MB
[2025-04-02 20:23:59 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][58/234]	eta 0:02:38 lr 0.000048	time 0.8789 (0.9021)	loss 0.3830 (0.4434)	grad_norm 3.2695 (3.0274)	mem 20675MB
[2025-04-02 20:24:01 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][60/234]	eta 0:02:36 lr 0.000048	time 0.8780 (0.9013)	loss 0.4480 (0.4440)	grad_norm 1.8549 (2.9975)	mem 20675MB
[2025-04-02 20:24:03 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][62/234]	eta 0:02:34 lr 0.000047	time 0.8784 (0.9006)	loss 0.4039 (0.4442)	grad_norm 2.5482 (3.0066)	mem 20675MB
[2025-04-02 20:24:04 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][64/234]	eta 0:02:32 lr 0.000047	time 0.8783 (0.9000)	loss 0.5416 (0.4459)	grad_norm 2.9820 (3.0125)	mem 20675MB
[2025-04-02 20:24:06 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][66/234]	eta 0:02:31 lr 0.000047	time 0.8785 (0.8993)	loss 0.5634 (0.4456)	grad_norm 2.8029 (3.0091)	mem 20675MB
[2025-04-02 20:24:08 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][68/234]	eta 0:02:29 lr 0.000047	time 0.8780 (0.8988)	loss 0.5654 (0.4473)	grad_norm 2.1778 (2.9976)	mem 20675MB
[2025-04-02 20:24:10 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][70/234]	eta 0:02:27 lr 0.000047	time 0.8781 (0.8982)	loss 0.5325 (0.4485)	grad_norm 3.0026 (2.9901)	mem 20675MB
[2025-04-02 20:24:12 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][72/234]	eta 0:02:25 lr 0.000046	time 0.8774 (0.8976)	loss 0.3523 (0.4476)	grad_norm 5.0167 (3.0072)	mem 20675MB
[2025-04-02 20:24:13 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][74/234]	eta 0:02:23 lr 0.000046	time 0.8784 (0.8971)	loss 0.3332 (0.4467)	grad_norm 3.5574 (3.0027)	mem 20675MB
[2025-04-02 20:24:15 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][76/234]	eta 0:02:21 lr 0.000046	time 0.8780 (0.8967)	loss 0.5825 (0.4476)	grad_norm 2.8329 (3.0056)	mem 20675MB
[2025-04-02 20:24:17 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][78/234]	eta 0:02:19 lr 0.000046	time 0.8774 (0.8962)	loss 0.4501 (0.4476)	grad_norm 2.3295 (3.0032)	mem 20675MB
[2025-04-02 20:24:19 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][80/234]	eta 0:02:17 lr 0.000046	time 0.8782 (0.8958)	loss 0.4810 (0.4484)	grad_norm 1.9783 (2.9889)	mem 20675MB
[2025-04-02 20:24:20 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][82/234]	eta 0:02:16 lr 0.000045	time 0.8781 (0.8954)	loss 0.4321 (0.4497)	grad_norm 3.5329 (2.9885)	mem 20675MB
[2025-04-02 20:24:22 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][84/234]	eta 0:02:14 lr 0.000045	time 0.8780 (0.8950)	loss 0.4039 (0.4490)	grad_norm 2.3952 (2.9939)	mem 20675MB
[2025-04-02 20:24:24 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][86/234]	eta 0:02:12 lr 0.000045	time 0.8779 (0.8946)	loss 0.5081 (0.4497)	grad_norm 2.8544 (2.9943)	mem 20675MB
[2025-04-02 20:24:26 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][88/234]	eta 0:02:10 lr 0.000045	time 0.8784 (0.8943)	loss 0.5204 (0.4515)	grad_norm 3.0063 (2.9875)	mem 20675MB
[2025-04-02 20:24:27 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][90/234]	eta 0:02:08 lr 0.000045	time 0.8788 (0.8940)	loss 0.5480 (0.4539)	grad_norm 4.4364 (2.9932)	mem 20675MB
[2025-04-02 20:24:29 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][92/234]	eta 0:02:06 lr 0.000044	time 0.8801 (0.8937)	loss 0.5885 (0.4548)	grad_norm 1.9605 (2.9759)	mem 20675MB
[2025-04-02 20:24:31 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][94/234]	eta 0:02:05 lr 0.000044	time 0.8786 (0.8934)	loss 0.3890 (0.4550)	grad_norm 3.3909 (2.9840)	mem 20675MB
[2025-04-02 20:24:33 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][96/234]	eta 0:02:03 lr 0.000044	time 0.8780 (0.8931)	loss 0.4444 (0.4559)	grad_norm 2.1124 (2.9705)	mem 20675MB
[2025-04-02 20:24:34 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][98/234]	eta 0:02:01 lr 0.000044	time 0.8786 (0.8928)	loss 0.3545 (0.4555)	grad_norm 3.5791 (2.9630)	mem 20675MB
[2025-04-02 20:24:36 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][100/234]	eta 0:01:59 lr 0.000043	time 0.8784 (0.8925)	loss 0.5484 (0.4546)	grad_norm 2.1024 (2.9625)	mem 20675MB
[2025-04-02 20:24:38 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][102/234]	eta 0:01:57 lr 0.000043	time 0.8784 (0.8923)	loss 0.4811 (0.4550)	grad_norm 1.9296 (2.9422)	mem 20675MB
[2025-04-02 20:24:40 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][104/234]	eta 0:01:55 lr 0.000043	time 0.8788 (0.8920)	loss 0.5928 (0.4565)	grad_norm 2.7927 (2.9344)	mem 20675MB
[2025-04-02 20:24:41 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][106/234]	eta 0:01:54 lr 0.000043	time 0.8780 (0.8918)	loss 0.4621 (0.4565)	grad_norm 2.0340 (2.9216)	mem 20675MB
[2025-04-02 20:24:43 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][108/234]	eta 0:01:52 lr 0.000043	time 0.8769 (0.8915)	loss 0.2915 (0.4552)	grad_norm 2.9190 (2.9187)	mem 20675MB
[2025-04-02 20:24:45 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][110/234]	eta 0:01:50 lr 0.000042	time 0.8781 (0.8913)	loss 0.3878 (0.4557)	grad_norm 2.9133 (2.9292)	mem 20675MB
[2025-04-02 20:24:47 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][112/234]	eta 0:01:48 lr 0.000042	time 0.8776 (0.8911)	loss 0.4684 (0.4567)	grad_norm 1.8006 (2.9153)	mem 20675MB
[2025-04-02 20:24:48 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][114/234]	eta 0:01:46 lr 0.000042	time 0.8785 (0.8909)	loss 0.4780 (0.4572)	grad_norm 2.2628 (2.9059)	mem 20675MB
[2025-04-02 20:24:50 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][116/234]	eta 0:01:45 lr 0.000042	time 0.8794 (0.8907)	loss 0.4048 (0.4568)	grad_norm 2.4705 (2.9023)	mem 20675MB
[2025-04-02 20:24:52 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][118/234]	eta 0:01:43 lr 0.000042	time 0.8781 (0.8905)	loss 0.4993 (0.4578)	grad_norm 1.9855 (2.8852)	mem 20675MB
[2025-04-02 20:24:54 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][120/234]	eta 0:01:41 lr 0.000041	time 0.8776 (0.8903)	loss 0.4151 (0.4569)	grad_norm 4.7359 (2.9028)	mem 20675MB
[2025-04-02 20:24:55 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][122/234]	eta 0:01:39 lr 0.000041	time 0.8787 (0.8901)	loss 0.3297 (0.4556)	grad_norm 3.0510 (2.9029)	mem 20675MB
[2025-04-02 20:24:57 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][124/234]	eta 0:01:37 lr 0.000041	time 0.8790 (0.8900)	loss 0.3342 (0.4542)	grad_norm 3.8627 (2.9103)	mem 20675MB
[2025-04-02 20:24:59 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][126/234]	eta 0:01:36 lr 0.000041	time 0.8791 (0.8898)	loss 0.5109 (0.4541)	grad_norm 2.2295 (2.9141)	mem 20675MB
[2025-04-02 20:25:01 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][128/234]	eta 0:01:34 lr 0.000041	time 0.8780 (0.8896)	loss 0.4166 (0.4526)	grad_norm 3.1563 (2.9162)	mem 20675MB
[2025-04-02 20:25:03 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][130/234]	eta 0:01:32 lr 0.000040	time 0.8785 (0.8895)	loss 0.3479 (0.4527)	grad_norm 4.0183 (2.9267)	mem 20675MB
[2025-04-02 20:25:04 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][132/234]	eta 0:01:30 lr 0.000040	time 0.8776 (0.8893)	loss 0.4186 (0.4525)	grad_norm 2.2794 (2.9197)	mem 20675MB
[2025-04-02 20:25:06 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][134/234]	eta 0:01:28 lr 0.000040	time 0.8791 (0.8892)	loss 0.4205 (0.4523)	grad_norm 1.6926 (2.9180)	mem 20675MB
[2025-04-02 20:25:08 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][136/234]	eta 0:01:27 lr 0.000040	time 0.8773 (0.8890)	loss 0.4218 (0.4526)	grad_norm 3.1574 (2.9203)	mem 20675MB
[2025-04-02 20:25:10 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][138/234]	eta 0:01:25 lr 0.000040	time 0.8782 (0.8889)	loss 0.5264 (0.4536)	grad_norm 2.5482 (2.9118)	mem 20675MB
[2025-04-02 20:25:11 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][140/234]	eta 0:01:23 lr 0.000039	time 0.8781 (0.8887)	loss 0.4357 (0.4537)	grad_norm 1.9591 (2.9063)	mem 20675MB
[2025-04-02 20:25:13 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][142/234]	eta 0:01:21 lr 0.000039	time 0.8772 (0.8886)	loss 0.4925 (0.4533)	grad_norm 2.3759 (2.9136)	mem 20675MB
[2025-04-02 20:25:15 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][144/234]	eta 0:01:19 lr 0.000039	time 0.8780 (0.8884)	loss 0.4340 (0.4523)	grad_norm 3.7770 (2.9251)	mem 20675MB
[2025-04-02 20:25:17 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][146/234]	eta 0:01:18 lr 0.000039	time 0.8776 (0.8883)	loss 0.4121 (0.4524)	grad_norm 3.1031 (2.9217)	mem 20675MB
[2025-04-02 20:25:18 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][148/234]	eta 0:01:16 lr 0.000039	time 0.8768 (0.8882)	loss 0.4779 (0.4520)	grad_norm 2.3815 (2.9149)	mem 20675MB
[2025-04-02 20:25:20 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][150/234]	eta 0:01:14 lr 0.000039	time 0.8763 (0.8880)	loss 0.4843 (0.4528)	grad_norm 2.4365 (2.9097)	mem 20675MB
[2025-04-02 20:25:22 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][152/234]	eta 0:01:12 lr 0.000038	time 0.8781 (0.8879)	loss 0.4383 (0.4533)	grad_norm 2.0604 (2.8971)	mem 20675MB
[2025-04-02 20:25:24 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][154/234]	eta 0:01:11 lr 0.000038	time 0.8787 (0.8878)	loss 0.4368 (0.4536)	grad_norm 2.2042 (2.8912)	mem 20675MB
[2025-04-02 20:25:25 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][156/234]	eta 0:01:09 lr 0.000038	time 0.8775 (0.8877)	loss 0.4421 (0.4536)	grad_norm 3.3575 (2.9031)	mem 20675MB
[2025-04-02 20:25:27 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][158/234]	eta 0:01:07 lr 0.000038	time 0.8769 (0.8876)	loss 0.5329 (0.4545)	grad_norm 1.9720 (2.8938)	mem 20675MB
[2025-04-02 20:25:29 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][160/234]	eta 0:01:05 lr 0.000038	time 0.8772 (0.8874)	loss 0.3589 (0.4544)	grad_norm 4.6053 (2.9033)	mem 20675MB
[2025-04-02 20:25:31 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][162/234]	eta 0:01:03 lr 0.000037	time 0.8771 (0.8873)	loss 0.5531 (0.4555)	grad_norm 2.0789 (2.8929)	mem 20675MB
[2025-04-02 20:25:32 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][164/234]	eta 0:01:02 lr 0.000037	time 0.8780 (0.8872)	loss 0.4783 (0.4558)	grad_norm 2.0188 (2.8881)	mem 20675MB
[2025-04-02 20:25:34 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][166/234]	eta 0:01:00 lr 0.000037	time 0.8777 (0.8871)	loss 0.5529 (0.4570)	grad_norm 2.1737 (2.8847)	mem 20675MB
[2025-04-02 20:25:36 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][168/234]	eta 0:00:58 lr 0.000037	time 0.8766 (0.8870)	loss 0.5090 (0.4575)	grad_norm 2.1781 (2.8789)	mem 20675MB
[2025-04-02 20:25:38 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][170/234]	eta 0:00:56 lr 0.000037	time 0.8773 (0.8869)	loss 0.3741 (0.4567)	grad_norm 2.7967 (2.8750)	mem 20675MB
[2025-04-02 20:25:39 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][172/234]	eta 0:00:54 lr 0.000036	time 0.8778 (0.8868)	loss 0.4595 (0.4569)	grad_norm 2.1349 (2.8673)	mem 20675MB
[2025-04-02 20:25:41 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][174/234]	eta 0:00:53 lr 0.000036	time 0.8790 (0.8867)	loss 0.4353 (0.4571)	grad_norm 2.0812 (2.8596)	mem 20675MB
[2025-04-02 20:25:43 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][176/234]	eta 0:00:51 lr 0.000036	time 0.8775 (0.8866)	loss 0.5756 (0.4573)	grad_norm 2.2623 (2.8547)	mem 20675MB
[2025-04-02 20:25:45 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][178/234]	eta 0:00:49 lr 0.000036	time 0.8777 (0.8865)	loss 0.4408 (0.4580)	grad_norm 2.0930 (2.8551)	mem 20675MB
[2025-04-02 20:25:46 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][180/234]	eta 0:00:47 lr 0.000036	time 0.8775 (0.8865)	loss 0.5379 (0.4587)	grad_norm 1.6105 (2.8403)	mem 20675MB
[2025-04-02 20:25:48 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][182/234]	eta 0:00:46 lr 0.000035	time 0.8768 (0.8864)	loss 0.5033 (0.4592)	grad_norm 2.9253 (2.8355)	mem 20675MB
[2025-04-02 20:25:50 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][184/234]	eta 0:00:44 lr 0.000035	time 0.8777 (0.8863)	loss 0.3837 (0.4589)	grad_norm 2.0718 (2.8289)	mem 20675MB
[2025-04-02 20:25:52 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][186/234]	eta 0:00:42 lr 0.000035	time 0.8793 (0.8862)	loss 0.4892 (0.4596)	grad_norm 2.0403 (2.8266)	mem 20675MB
[2025-04-02 20:25:53 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][188/234]	eta 0:00:40 lr 0.000035	time 0.8780 (0.8862)	loss 0.4599 (0.4590)	grad_norm 2.2369 (2.8248)	mem 20675MB
[2025-04-02 20:25:55 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][190/234]	eta 0:00:38 lr 0.000035	time 0.8773 (0.8861)	loss 0.3670 (0.4584)	grad_norm 3.4565 (2.8220)	mem 20675MB
[2025-04-02 20:25:57 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][192/234]	eta 0:00:37 lr 0.000035	time 0.8774 (0.8860)	loss 0.5112 (0.4589)	grad_norm 2.2944 (2.8165)	mem 20675MB
[2025-04-02 20:25:59 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][194/234]	eta 0:00:35 lr 0.000034	time 0.8788 (0.8859)	loss 0.3053 (0.4580)	grad_norm 3.0994 (2.8128)	mem 20675MB
[2025-04-02 20:26:01 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][196/234]	eta 0:00:33 lr 0.000034	time 0.8779 (0.8859)	loss 0.5271 (0.4587)	grad_norm 2.7219 (2.8094)	mem 20675MB
[2025-04-02 20:26:02 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][198/234]	eta 0:00:31 lr 0.000034	time 0.8783 (0.8858)	loss 0.5260 (0.4583)	grad_norm 2.6123 (2.8210)	mem 20675MB
[2025-04-02 20:26:04 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][200/234]	eta 0:00:30 lr 0.000034	time 0.8796 (0.8857)	loss 0.4692 (0.4589)	grad_norm 2.5762 (2.8178)	mem 20675MB
[2025-04-02 20:26:06 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][202/234]	eta 0:00:28 lr 0.000034	time 0.8791 (0.8857)	loss 0.5304 (0.4591)	grad_norm 3.0008 (2.8177)	mem 20675MB
[2025-04-02 20:26:08 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][204/234]	eta 0:00:26 lr 0.000033	time 0.8777 (0.8856)	loss 0.4867 (0.4593)	grad_norm 2.0253 (2.8162)	mem 20675MB
[2025-04-02 20:26:09 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][206/234]	eta 0:00:24 lr 0.000033	time 0.8789 (0.8856)	loss 0.5096 (0.4596)	grad_norm 2.1258 (2.8099)	mem 20675MB
[2025-04-02 20:26:11 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][208/234]	eta 0:00:23 lr 0.000033	time 0.8790 (0.8855)	loss 0.5648 (0.4605)	grad_norm 3.1109 (2.8107)	mem 20675MB
[2025-04-02 20:26:13 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][210/234]	eta 0:00:21 lr 0.000033	time 0.8793 (0.8854)	loss 0.5734 (0.4610)	grad_norm 2.7494 (2.8067)	mem 20675MB
[2025-04-02 20:26:15 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][212/234]	eta 0:00:19 lr 0.000033	time 0.8786 (0.8854)	loss 0.5174 (0.4613)	grad_norm 2.8267 (2.8039)	mem 20675MB
[2025-04-02 20:26:16 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][214/234]	eta 0:00:17 lr 0.000033	time 0.8785 (0.8853)	loss 0.5801 (0.4618)	grad_norm 2.3860 (2.7997)	mem 20675MB
[2025-04-02 20:26:18 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][216/234]	eta 0:00:15 lr 0.000032	time 0.8777 (0.8853)	loss 0.5202 (0.4618)	grad_norm 2.1042 (2.8093)	mem 20675MB
[2025-04-02 20:26:20 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][218/234]	eta 0:00:14 lr 0.000032	time 0.8771 (0.8852)	loss 0.4795 (0.4622)	grad_norm 1.7284 (2.8008)	mem 20675MB
[2025-04-02 20:26:22 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][220/234]	eta 0:00:12 lr 0.000032	time 0.8770 (0.8851)	loss 0.5679 (0.4630)	grad_norm 2.0744 (2.7932)	mem 20675MB
[2025-04-02 20:26:23 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][222/234]	eta 0:00:10 lr 0.000032	time 0.8778 (0.8851)	loss 0.4962 (0.4634)	grad_norm 1.7280 (2.7861)	mem 20675MB
[2025-04-02 20:26:25 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][224/234]	eta 0:00:08 lr 0.000032	time 0.8774 (0.8850)	loss 0.5263 (0.4639)	grad_norm 2.8699 (2.7840)	mem 20675MB
[2025-04-02 20:26:27 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][226/234]	eta 0:00:07 lr 0.000032	time 0.8783 (0.8850)	loss 0.6034 (0.4645)	grad_norm 3.3754 (2.7907)	mem 20675MB
[2025-04-02 20:26:29 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][228/234]	eta 0:00:05 lr 0.000031	time 0.8789 (0.8849)	loss 0.4638 (0.4644)	grad_norm 2.3073 (2.7925)	mem 20675MB
[2025-04-02 20:26:30 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][230/234]	eta 0:00:03 lr 0.000031	time 0.8778 (0.8848)	loss 0.3300 (0.4643)	grad_norm 3.1110 (2.7906)	mem 20675MB
[2025-04-02 20:26:32 simmim_finetune] (main_finetune.py 252): INFO Train: [26/30][232/234]	eta 0:00:01 lr 0.000031	time 0.8775 (0.8848)	loss 0.5493 (0.4648)	grad_norm 2.3016 (2.7850)	mem 20675MB
[2025-04-02 20:26:33 simmim_finetune] (main_finetune.py 260): INFO EPOCH 26 training takes 0:03:27
[2025-04-02 20:26:34 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.085 (1.085)	Loss 0.6835 (0.6835)	Acc@1 64.844 (64.844)	Mem 20675MB
[2025-04-02 20:26:34 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.823
[2025-04-02 20:26:34 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.8%
[2025-04-02 20:26:34 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:26:34 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [3.572808787678097e-07, 3.572808787678097e-07, 4.18351093222076e-07, 4.18351093222076e-07, 5.123052693055626e-07, 5.123052693055626e-07, 6.568501555878498e-07, 6.568501555878498e-07, 8.792269037144452e-07, 8.792269037144452e-07, 1.2213449777553616e-06, 1.2213449777553616e-06, 1.747680476279848e-06, 1.747680476279848e-06, 2.55742739708675e-06, 2.55742739708675e-06, 3.8031918906358302e-06, 3.8031918906358302e-06, 5.7197526499421085e-06, 5.7197526499421085e-06, 8.668307664259457e-06, 8.668307664259457e-06, 1.320454614782461e-05, 1.320454614782461e-05, 2.0183374584078693e-05, 2.0183374584078693e-05, 3.092003371677728e-05, 3.092003371677728e-05]
[2025-04-02 20:26:36 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][0/234]	eta 0:07:53 lr 0.000031	time 2.0248 (2.0248)	loss 0.4672 (0.4672)	grad_norm 3.1270 (3.1270)	mem 20675MB
[2025-04-02 20:26:38 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][2/234]	eta 0:04:52 lr 0.000031	time 0.8787 (1.2616)	loss 0.3982 (0.4117)	grad_norm 3.2840 (2.9303)	mem 20675MB
[2025-04-02 20:26:40 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][4/234]	eta 0:04:15 lr 0.000030	time 0.8786 (1.1088)	loss 0.5407 (0.4602)	grad_norm 1.9576 (2.5614)	mem 20675MB
[2025-04-02 20:26:42 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][6/234]	eta 0:03:57 lr 0.000030	time 0.8775 (1.0431)	loss 0.5549 (0.4864)	grad_norm 3.9134 (2.7232)	mem 20675MB
[2025-04-02 20:26:43 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][8/234]	eta 0:03:47 lr 0.000030	time 0.8784 (1.0066)	loss 0.5286 (0.4764)	grad_norm 2.9854 (2.6905)	mem 20675MB
[2025-04-02 20:26:45 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][10/234]	eta 0:03:40 lr 0.000030	time 0.8788 (0.9835)	loss 0.3901 (0.4590)	grad_norm 2.6804 (2.7509)	mem 20675MB
[2025-04-02 20:26:47 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][12/234]	eta 0:03:34 lr 0.000030	time 0.8774 (0.9674)	loss 0.5470 (0.4550)	grad_norm 2.0763 (2.7423)	mem 20675MB
[2025-04-02 20:26:49 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][14/234]	eta 0:03:30 lr 0.000030	time 0.8773 (0.9555)	loss 0.4210 (0.4494)	grad_norm 2.0209 (2.6491)	mem 20675MB
[2025-04-02 20:26:51 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][16/234]	eta 0:03:26 lr 0.000029	time 0.8776 (0.9465)	loss 0.4763 (0.4514)	grad_norm 2.3384 (2.6122)	mem 20675MB
[2025-04-02 20:26:52 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][18/234]	eta 0:03:22 lr 0.000029	time 0.8773 (0.9393)	loss 0.4932 (0.4572)	grad_norm 2.4174 (2.5708)	mem 20675MB
[2025-04-02 20:26:54 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][20/234]	eta 0:03:19 lr 0.000029	time 0.8786 (0.9335)	loss 0.5077 (0.4578)	grad_norm 3.0602 (2.5452)	mem 20675MB
[2025-04-02 20:26:56 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][22/234]	eta 0:03:16 lr 0.000029	time 0.8778 (0.9288)	loss 0.2979 (0.4503)	grad_norm 3.0466 (2.5945)	mem 20675MB
[2025-04-02 20:26:58 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][24/234]	eta 0:03:14 lr 0.000029	time 0.8785 (0.9248)	loss 0.5679 (0.4575)	grad_norm 2.7020 (2.5856)	mem 20675MB
[2025-04-02 20:26:59 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][26/234]	eta 0:03:11 lr 0.000029	time 0.8794 (0.9215)	loss 0.4950 (0.4555)	grad_norm 2.3572 (2.6141)	mem 20675MB
[2025-04-02 20:27:01 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][28/234]	eta 0:03:09 lr 0.000028	time 0.8773 (0.9185)	loss 0.5140 (0.4602)	grad_norm 2.7980 (2.6041)	mem 20675MB
[2025-04-02 20:27:03 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][30/234]	eta 0:03:06 lr 0.000028	time 0.8776 (0.9159)	loss 0.5168 (0.4629)	grad_norm 1.9600 (2.5865)	mem 20675MB
[2025-04-02 20:27:05 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][32/234]	eta 0:03:04 lr 0.000028	time 0.8767 (0.9136)	loss 0.3482 (0.4541)	grad_norm 2.9106 (2.6144)	mem 20675MB
[2025-04-02 20:27:06 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][34/234]	eta 0:03:02 lr 0.000028	time 0.8770 (0.9116)	loss 0.3600 (0.4475)	grad_norm 2.5405 (2.6127)	mem 20675MB
[2025-04-02 20:27:08 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][36/234]	eta 0:03:00 lr 0.000028	time 0.8775 (0.9098)	loss 0.5301 (0.4513)	grad_norm 2.4992 (2.5980)	mem 20675MB
[2025-04-02 20:27:10 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][38/234]	eta 0:02:58 lr 0.000028	time 0.8783 (0.9082)	loss 0.4643 (0.4504)	grad_norm 1.4392 (2.5695)	mem 20675MB
[2025-04-02 20:27:12 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][40/234]	eta 0:02:55 lr 0.000027	time 0.8786 (0.9068)	loss 0.5105 (0.4492)	grad_norm 2.1320 (2.5693)	mem 20675MB
[2025-04-02 20:27:13 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][42/234]	eta 0:02:53 lr 0.000027	time 0.8776 (0.9055)	loss 0.5893 (0.4542)	grad_norm 3.8207 (2.5865)	mem 20675MB
[2025-04-02 20:27:15 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][44/234]	eta 0:02:51 lr 0.000027	time 0.8770 (0.9042)	loss 0.5072 (0.4558)	grad_norm 3.0114 (2.6186)	mem 20675MB
[2025-04-02 20:27:17 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][46/234]	eta 0:02:49 lr 0.000027	time 0.8789 (0.9032)	loss 0.4779 (0.4542)	grad_norm 2.1795 (2.6256)	mem 20675MB
[2025-04-02 20:27:19 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][48/234]	eta 0:02:47 lr 0.000027	time 0.8782 (0.9022)	loss 0.3551 (0.4530)	grad_norm 3.8571 (2.6464)	mem 20675MB
[2025-04-02 20:27:20 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][50/234]	eta 0:02:45 lr 0.000027	time 0.8777 (0.9013)	loss 0.4368 (0.4529)	grad_norm 3.1818 (2.6449)	mem 20675MB
[2025-04-02 20:27:22 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][52/234]	eta 0:02:43 lr 0.000027	time 0.8784 (0.9004)	loss 0.5810 (0.4563)	grad_norm 2.0133 (2.6214)	mem 20675MB
[2025-04-02 20:27:24 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][54/234]	eta 0:02:41 lr 0.000026	time 0.8783 (0.8997)	loss 0.4365 (0.4583)	grad_norm 2.4105 (2.6277)	mem 20675MB
[2025-04-02 20:27:26 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][56/234]	eta 0:02:40 lr 0.000026	time 0.8779 (0.8989)	loss 0.5241 (0.4616)	grad_norm 2.9797 (2.6399)	mem 20675MB
[2025-04-02 20:27:27 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][58/234]	eta 0:02:38 lr 0.000026	time 0.8780 (0.8983)	loss 0.5429 (0.4648)	grad_norm 3.4372 (2.6460)	mem 20675MB
[2025-04-02 20:27:29 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][60/234]	eta 0:02:36 lr 0.000026	time 0.8772 (0.8976)	loss 0.4906 (0.4647)	grad_norm 2.3089 (2.6456)	mem 20675MB
[2025-04-02 20:27:31 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][62/234]	eta 0:02:34 lr 0.000026	time 0.8778 (0.8970)	loss 0.5614 (0.4650)	grad_norm 2.6433 (2.6371)	mem 20675MB
[2025-04-02 20:27:33 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][64/234]	eta 0:02:32 lr 0.000026	time 0.8780 (0.8965)	loss 0.3311 (0.4623)	grad_norm 1.9435 (2.6191)	mem 20675MB
[2025-04-02 20:27:34 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][66/234]	eta 0:02:30 lr 0.000025	time 0.8788 (0.8960)	loss 0.4460 (0.4634)	grad_norm 3.2097 (2.6364)	mem 20675MB
[2025-04-02 20:27:36 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][68/234]	eta 0:02:28 lr 0.000025	time 0.8781 (0.8955)	loss 0.4099 (0.4629)	grad_norm 2.8757 (2.6321)	mem 20675MB
[2025-04-02 20:27:38 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][70/234]	eta 0:02:26 lr 0.000025	time 0.8776 (0.8950)	loss 0.3787 (0.4597)	grad_norm 3.7051 (2.6575)	mem 20675MB
[2025-04-02 20:27:40 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][72/234]	eta 0:02:24 lr 0.000025	time 0.8785 (0.8946)	loss 0.5544 (0.4616)	grad_norm 2.8427 (2.6539)	mem 20675MB
[2025-04-02 20:27:41 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][74/234]	eta 0:02:23 lr 0.000025	time 0.8788 (0.8942)	loss 0.4630 (0.4623)	grad_norm 2.4243 (2.6459)	mem 20675MB
[2025-04-02 20:27:43 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][76/234]	eta 0:02:21 lr 0.000025	time 0.8783 (0.8938)	loss 0.4667 (0.4623)	grad_norm 2.2221 (2.6326)	mem 20675MB
[2025-04-02 20:27:45 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][78/234]	eta 0:02:19 lr 0.000024	time 0.8774 (0.8934)	loss 0.5083 (0.4618)	grad_norm 2.5023 (2.6470)	mem 20675MB
[2025-04-02 20:27:47 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][80/234]	eta 0:02:17 lr 0.000024	time 0.8788 (0.8930)	loss 0.5284 (0.4622)	grad_norm 2.6975 (2.6530)	mem 20675MB
[2025-04-02 20:27:49 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][82/234]	eta 0:02:15 lr 0.000024	time 0.8778 (0.8927)	loss 0.2867 (0.4605)	grad_norm 3.2075 (2.6534)	mem 20675MB
[2025-04-02 20:27:50 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][84/234]	eta 0:02:13 lr 0.000024	time 0.8787 (0.8924)	loss 0.4094 (0.4600)	grad_norm 2.4627 (2.6515)	mem 20675MB
[2025-04-02 20:27:52 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][86/234]	eta 0:02:12 lr 0.000024	time 0.8795 (0.8921)	loss 0.4471 (0.4617)	grad_norm 2.9353 (2.6497)	mem 20675MB
[2025-04-02 20:27:54 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][88/234]	eta 0:02:10 lr 0.000024	time 0.8780 (0.8918)	loss 0.3621 (0.4614)	grad_norm 2.0412 (2.6449)	mem 20675MB
[2025-04-02 20:27:56 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][90/234]	eta 0:02:08 lr 0.000024	time 0.8792 (0.8916)	loss 0.3665 (0.4612)	grad_norm 2.2861 (2.6328)	mem 20675MB
[2025-04-02 20:27:57 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][92/234]	eta 0:02:06 lr 0.000023	time 0.8792 (0.8913)	loss 0.3530 (0.4595)	grad_norm 4.0844 (2.6566)	mem 20675MB
[2025-04-02 20:27:59 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][94/234]	eta 0:02:04 lr 0.000023	time 0.8792 (0.8911)	loss 0.5044 (0.4604)	grad_norm 1.9312 (2.6476)	mem 20675MB
[2025-04-02 20:28:01 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][96/234]	eta 0:02:02 lr 0.000023	time 0.8779 (0.8909)	loss 0.5362 (0.4611)	grad_norm 3.7584 (2.6613)	mem 20675MB
[2025-04-02 20:28:03 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][98/234]	eta 0:02:01 lr 0.000023	time 0.8783 (0.8906)	loss 0.4194 (0.4611)	grad_norm 2.4845 (2.6640)	mem 20675MB
[2025-04-02 20:28:04 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][100/234]	eta 0:01:59 lr 0.000023	time 0.8795 (0.8904)	loss 0.4968 (0.4623)	grad_norm 2.9697 (2.6619)	mem 20675MB
[2025-04-02 20:28:06 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][102/234]	eta 0:01:57 lr 0.000023	time 0.8787 (0.8902)	loss 0.5121 (0.4642)	grad_norm 2.6866 (2.6785)	mem 20675MB
[2025-04-02 20:28:08 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][104/234]	eta 0:01:55 lr 0.000022	time 0.8773 (0.8900)	loss 0.5205 (0.4638)	grad_norm 2.5413 (2.6743)	mem 20675MB
[2025-04-02 20:28:10 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][106/234]	eta 0:01:53 lr 0.000022	time 0.8788 (0.8898)	loss 0.4547 (0.4631)	grad_norm 2.4086 (2.6768)	mem 20675MB
[2025-04-02 20:28:11 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][108/234]	eta 0:01:52 lr 0.000022	time 0.8784 (0.8896)	loss 0.4846 (0.4636)	grad_norm 1.9131 (2.6742)	mem 20675MB
[2025-04-02 20:28:13 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][110/234]	eta 0:01:50 lr 0.000022	time 0.8789 (0.8894)	loss 0.3466 (0.4623)	grad_norm 3.6961 (2.6921)	mem 20675MB
[2025-04-02 20:28:15 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][112/234]	eta 0:01:48 lr 0.000022	time 0.8784 (0.8892)	loss 0.4959 (0.4617)	grad_norm 2.3664 (2.6831)	mem 20675MB
[2025-04-02 20:28:17 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][114/234]	eta 0:01:46 lr 0.000022	time 0.8781 (0.8890)	loss 0.3915 (0.4603)	grad_norm 2.9021 (2.6829)	mem 20675MB
[2025-04-02 20:28:18 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][116/234]	eta 0:01:44 lr 0.000022	time 0.8796 (0.8889)	loss 0.3851 (0.4598)	grad_norm 2.3881 (2.6822)	mem 20675MB
[2025-04-02 20:28:20 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][118/234]	eta 0:01:43 lr 0.000021	time 0.8800 (0.8888)	loss 0.4879 (0.4608)	grad_norm 2.5331 (2.6788)	mem 20675MB
[2025-04-02 20:28:22 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][120/234]	eta 0:01:41 lr 0.000021	time 0.8786 (0.8886)	loss 0.4932 (0.4615)	grad_norm 2.1662 (2.6756)	mem 20675MB
[2025-04-02 20:28:24 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][122/234]	eta 0:01:39 lr 0.000021	time 0.8795 (0.8885)	loss 0.5030 (0.4623)	grad_norm 1.7784 (2.6669)	mem 20675MB
[2025-04-02 20:28:25 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][124/234]	eta 0:01:37 lr 0.000021	time 0.8785 (0.8883)	loss 0.4253 (0.4625)	grad_norm 2.6341 (2.6686)	mem 20675MB
[2025-04-02 20:28:27 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][126/234]	eta 0:01:35 lr 0.000021	time 0.8790 (0.8882)	loss 0.4477 (0.4610)	grad_norm 1.6567 (2.6691)	mem 20675MB
[2025-04-02 20:28:29 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][128/234]	eta 0:01:34 lr 0.000021	time 0.8778 (0.8880)	loss 0.3398 (0.4589)	grad_norm 2.8553 (2.6773)	mem 20675MB
[2025-04-02 20:28:31 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][130/234]	eta 0:01:32 lr 0.000021	time 0.8774 (0.8879)	loss 0.4797 (0.4597)	grad_norm 2.5929 (2.6789)	mem 20675MB
[2025-04-02 20:28:32 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][132/234]	eta 0:01:30 lr 0.000020	time 0.8790 (0.8878)	loss 0.5376 (0.4594)	grad_norm 2.5067 (2.7012)	mem 20675MB
[2025-04-02 20:28:34 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][134/234]	eta 0:01:28 lr 0.000020	time 0.8773 (0.8876)	loss 0.5753 (0.4604)	grad_norm 2.5723 (2.6977)	mem 20675MB
[2025-04-02 20:28:36 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][136/234]	eta 0:01:26 lr 0.000020	time 0.8776 (0.8875)	loss 0.3999 (0.4601)	grad_norm 7.8084 (2.7362)	mem 20675MB
[2025-04-02 20:28:38 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][138/234]	eta 0:01:25 lr 0.000020	time 0.8775 (0.8874)	loss 0.5062 (0.4596)	grad_norm 2.6860 (2.7380)	mem 20675MB
[2025-04-02 20:28:40 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][140/234]	eta 0:01:23 lr 0.000020	time 0.8784 (0.8873)	loss 0.4144 (0.4586)	grad_norm 3.4059 (2.7434)	mem 20675MB
[2025-04-02 20:28:41 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][142/234]	eta 0:01:21 lr 0.000020	time 0.8767 (0.8871)	loss 0.3994 (0.4589)	grad_norm 3.9977 (2.7521)	mem 20675MB
[2025-04-02 20:28:43 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][144/234]	eta 0:01:19 lr 0.000020	time 0.8784 (0.8870)	loss 0.4367 (0.4587)	grad_norm 2.1375 (2.7422)	mem 20675MB
[2025-04-02 20:28:45 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][146/234]	eta 0:01:18 lr 0.000019	time 0.8775 (0.8869)	loss 0.5055 (0.4582)	grad_norm 2.6644 (2.7409)	mem 20675MB
[2025-04-02 20:28:47 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][148/234]	eta 0:01:16 lr 0.000019	time 0.8779 (0.8868)	loss 0.4634 (0.4574)	grad_norm 3.7088 (2.7695)	mem 20675MB
[2025-04-02 20:28:48 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][150/234]	eta 0:01:14 lr 0.000019	time 0.8781 (0.8867)	loss 0.5530 (0.4583)	grad_norm 3.0162 (2.7689)	mem 20675MB
[2025-04-02 20:28:50 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][152/234]	eta 0:01:12 lr 0.000019	time 0.8812 (0.8866)	loss 0.3837 (0.4574)	grad_norm 4.3803 (2.7821)	mem 20675MB
[2025-04-02 20:28:52 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][154/234]	eta 0:01:10 lr 0.000019	time 0.8818 (0.8866)	loss 0.4553 (0.4565)	grad_norm 3.0389 (2.7829)	mem 20675MB
[2025-04-02 20:28:54 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][156/234]	eta 0:01:09 lr 0.000019	time 0.8843 (0.8865)	loss 0.5376 (0.4565)	grad_norm 2.7652 (2.7833)	mem 20675MB
[2025-04-02 20:28:55 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][158/234]	eta 0:01:07 lr 0.000019	time 0.8767 (0.8865)	loss 0.3389 (0.4558)	grad_norm 4.5292 (2.7905)	mem 20675MB
[2025-04-02 20:28:57 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][160/234]	eta 0:01:05 lr 0.000019	time 0.8789 (0.8864)	loss 0.4145 (0.4558)	grad_norm 4.5776 (2.7965)	mem 20675MB
[2025-04-02 20:28:59 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][162/234]	eta 0:01:03 lr 0.000018	time 0.8852 (0.8864)	loss 0.4453 (0.4552)	grad_norm 2.5565 (2.8014)	mem 20675MB
[2025-04-02 20:29:01 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][164/234]	eta 0:01:02 lr 0.000018	time 0.8766 (0.8863)	loss 0.4861 (0.4550)	grad_norm 2.2783 (2.8060)	mem 20675MB
[2025-04-02 20:29:02 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][166/234]	eta 0:01:00 lr 0.000018	time 0.8834 (0.8863)	loss 0.4439 (0.4547)	grad_norm 2.1594 (2.7959)	mem 20675MB
[2025-04-02 20:29:04 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][168/234]	eta 0:00:58 lr 0.000018	time 0.8848 (0.8862)	loss 0.4347 (0.4552)	grad_norm 2.1909 (2.7983)	mem 20675MB
[2025-04-02 20:29:06 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][170/234]	eta 0:00:56 lr 0.000018	time 0.8776 (0.8862)	loss 0.3954 (0.4553)	grad_norm 3.6149 (2.7998)	mem 20675MB
[2025-04-02 20:29:08 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][172/234]	eta 0:00:54 lr 0.000018	time 0.8815 (0.8861)	loss 0.5752 (0.4556)	grad_norm 2.2854 (2.8035)	mem 20675MB
[2025-04-02 20:29:09 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][174/234]	eta 0:00:53 lr 0.000018	time 0.8779 (0.8861)	loss 0.3706 (0.4552)	grad_norm 2.4468 (2.8026)	mem 20675MB
[2025-04-02 20:29:11 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][176/234]	eta 0:00:51 lr 0.000017	time 0.8786 (0.8861)	loss 0.5399 (0.4559)	grad_norm 2.6247 (2.8005)	mem 20675MB
[2025-04-02 20:29:13 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][178/234]	eta 0:00:49 lr 0.000017	time 0.8780 (0.8861)	loss 0.3413 (0.4555)	grad_norm 4.5847 (2.8081)	mem 20675MB
[2025-04-02 20:29:15 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][180/234]	eta 0:00:47 lr 0.000017	time 0.8858 (0.8861)	loss 0.4218 (0.4547)	grad_norm 3.7143 (2.8139)	mem 20675MB
[2025-04-02 20:29:17 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][182/234]	eta 0:00:46 lr 0.000017	time 0.8783 (0.8861)	loss 0.4788 (0.4541)	grad_norm 2.6346 (2.8138)	mem 20675MB
[2025-04-02 20:29:18 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][184/234]	eta 0:00:44 lr 0.000017	time 0.8792 (0.8860)	loss 0.5123 (0.4549)	grad_norm 1.9968 (2.8147)	mem 20675MB
[2025-04-02 20:29:20 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][186/234]	eta 0:00:42 lr 0.000017	time 0.8849 (0.8860)	loss 0.4665 (0.4552)	grad_norm 2.3898 (2.8084)	mem 20675MB
[2025-04-02 20:29:22 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][188/234]	eta 0:00:40 lr 0.000017	time 0.9016 (0.8861)	loss 0.5269 (0.4560)	grad_norm 2.4171 (2.8078)	mem 20675MB
[2025-04-02 20:29:24 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][190/234]	eta 0:00:38 lr 0.000017	time 0.8792 (0.8861)	loss 0.4750 (0.4559)	grad_norm 3.2957 (2.8079)	mem 20675MB
[2025-04-02 20:29:25 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][192/234]	eta 0:00:37 lr 0.000016	time 0.8786 (0.8860)	loss 0.5689 (0.4569)	grad_norm 2.5618 (2.8052)	mem 20675MB
[2025-04-02 20:29:27 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][194/234]	eta 0:00:35 lr 0.000016	time 0.8807 (0.8860)	loss 0.5515 (0.4565)	grad_norm 3.4449 (2.8033)	mem 20675MB
[2025-04-02 20:29:29 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][196/234]	eta 0:00:33 lr 0.000016	time 0.8798 (0.8859)	loss 0.4801 (0.4566)	grad_norm 2.6163 (2.7989)	mem 20675MB
[2025-04-02 20:29:31 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][198/234]	eta 0:00:31 lr 0.000016	time 0.8806 (0.8859)	loss 0.5136 (0.4565)	grad_norm 2.9094 (2.7991)	mem 20675MB
[2025-04-02 20:29:32 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][200/234]	eta 0:00:30 lr 0.000016	time 0.8804 (0.8859)	loss 0.5349 (0.4562)	grad_norm 2.9904 (2.8002)	mem 20675MB
[2025-04-02 20:29:34 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][202/234]	eta 0:00:28 lr 0.000016	time 0.8802 (0.8858)	loss 0.3908 (0.4559)	grad_norm 3.9062 (2.8049)	mem 20675MB
[2025-04-02 20:29:36 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][204/234]	eta 0:00:26 lr 0.000016	time 0.8788 (0.8858)	loss 0.5064 (0.4566)	grad_norm 1.9108 (2.8009)	mem 20675MB
[2025-04-02 20:29:38 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][206/234]	eta 0:00:24 lr 0.000016	time 0.8845 (0.8858)	loss 0.4126 (0.4562)	grad_norm 2.9720 (2.8048)	mem 20675MB
[2025-04-02 20:29:40 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][208/234]	eta 0:00:23 lr 0.000015	time 0.8779 (0.8857)	loss 0.3677 (0.4560)	grad_norm 2.4501 (2.8037)	mem 20675MB
[2025-04-02 20:29:41 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][210/234]	eta 0:00:21 lr 0.000015	time 0.8774 (0.8856)	loss 0.3885 (0.4561)	grad_norm 2.4656 (2.8100)	mem 20675MB
[2025-04-02 20:29:43 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][212/234]	eta 0:00:19 lr 0.000015	time 0.8772 (0.8856)	loss 0.3630 (0.4561)	grad_norm 3.1558 (2.8161)	mem 20675MB
[2025-04-02 20:29:45 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][214/234]	eta 0:00:17 lr 0.000015	time 0.8777 (0.8856)	loss 0.4774 (0.4558)	grad_norm 1.9261 (2.8136)	mem 20675MB
[2025-04-02 20:29:47 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][216/234]	eta 0:00:15 lr 0.000015	time 0.8774 (0.8855)	loss 0.5081 (0.4562)	grad_norm 3.4379 (2.8170)	mem 20675MB
[2025-04-02 20:29:48 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][218/234]	eta 0:00:14 lr 0.000015	time 0.8782 (0.8855)	loss 0.4644 (0.4562)	grad_norm 2.6197 (2.8158)	mem 20675MB
[2025-04-02 20:29:50 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][220/234]	eta 0:00:12 lr 0.000015	time 0.8775 (0.8854)	loss 0.4441 (0.4560)	grad_norm 1.8916 (2.8160)	mem 20675MB
[2025-04-02 20:29:52 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][222/234]	eta 0:00:10 lr 0.000015	time 0.8773 (0.8853)	loss 0.5332 (0.4569)	grad_norm 1.9550 (2.8102)	mem 20675MB
[2025-04-02 20:29:54 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][224/234]	eta 0:00:08 lr 0.000014	time 0.8766 (0.8853)	loss 0.5111 (0.4573)	grad_norm 2.9292 (2.8098)	mem 20675MB
[2025-04-02 20:29:55 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][226/234]	eta 0:00:07 lr 0.000014	time 0.8780 (0.8853)	loss 0.3942 (0.4571)	grad_norm 3.4788 (2.8087)	mem 20675MB
[2025-04-02 20:29:57 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][228/234]	eta 0:00:05 lr 0.000014	time 0.8783 (0.8853)	loss 0.4393 (0.4573)	grad_norm 3.5218 (2.8098)	mem 20675MB
[2025-04-02 20:29:59 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][230/234]	eta 0:00:03 lr 0.000014	time 0.8782 (0.8852)	loss 0.5037 (0.4579)	grad_norm 2.5603 (2.8075)	mem 20675MB
[2025-04-02 20:30:01 simmim_finetune] (main_finetune.py 252): INFO Train: [27/30][232/234]	eta 0:00:01 lr 0.000014	time 0.8780 (0.8852)	loss 0.5083 (0.4582)	grad_norm 3.3392 (2.8085)	mem 20675MB
[2025-04-02 20:30:02 simmim_finetune] (main_finetune.py 260): INFO EPOCH 27 training takes 0:03:27
[2025-04-02 20:30:04 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 2.228 (2.228)	Loss 0.6949 (0.6949)	Acc@1 64.844 (64.844)	Mem 20675MB
[2025-04-02 20:30:04 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.823
[2025-04-02 20:30:04 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.8%
[2025-04-02 20:30:04 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:30:04 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.979675490497683e-07, 2.979675490497683e-07, 3.2527333309032433e-07, 3.2527333309032433e-07, 3.6728223161425674e-07, 3.6728223161425674e-07, 4.3191130626646045e-07, 4.3191130626646045e-07, 5.313406518852354e-07, 5.313406518852354e-07, 6.843088759141199e-07, 6.843088759141199e-07, 9.196446051893269e-07, 9.196446051893269e-07, 1.2816995733050295e-06, 1.2816995733050295e-06, 1.838707216559957e-06, 1.838707216559957e-06, 2.695642052336769e-06, 2.695642052336769e-06, 4.014003338147248e-06, 4.014003338147248e-06, 6.04225147016337e-06, 6.04225147016337e-06, 9.162633211726634e-06, 9.162633211726634e-06, 1.3963220506439348e-05, 1.3963220506439348e-05]
[2025-04-02 20:30:08 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][0/234]	eta 0:14:13 lr 0.000014	time 3.6456 (3.6456)	loss 0.4294 (0.4294)	grad_norm 3.0667 (3.0667)	mem 20675MB
[2025-04-02 20:30:10 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][2/234]	eta 0:06:58 lr 0.000014	time 0.8830 (1.8055)	loss 0.3657 (0.4467)	grad_norm 2.5247 (2.9429)	mem 20675MB
[2025-04-02 20:30:12 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][4/234]	eta 0:05:30 lr 0.000014	time 0.8799 (1.4358)	loss 0.4343 (0.4302)	grad_norm 2.8726 (2.9030)	mem 20675MB
[2025-04-02 20:30:13 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][6/234]	eta 0:04:52 lr 0.000014	time 0.9099 (1.2815)	loss 0.4034 (0.4255)	grad_norm 4.5051 (3.0479)	mem 20675MB
[2025-04-02 20:30:15 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][8/234]	eta 0:04:29 lr 0.000013	time 0.8869 (1.1942)	loss 0.5374 (0.4372)	grad_norm 1.9298 (3.1060)	mem 20675MB
[2025-04-02 20:30:17 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][10/234]	eta 0:04:14 lr 0.000013	time 0.8790 (1.1379)	loss 0.4019 (0.4450)	grad_norm 3.4951 (3.0923)	mem 20675MB
[2025-04-02 20:30:19 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][12/234]	eta 0:04:03 lr 0.000013	time 0.8804 (1.0984)	loss 0.5287 (0.4520)	grad_norm 2.5649 (3.0250)	mem 20675MB
[2025-04-02 20:30:21 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][14/234]	eta 0:03:55 lr 0.000013	time 0.8795 (1.0697)	loss 0.5546 (0.4606)	grad_norm 2.3211 (3.0231)	mem 20675MB
[2025-04-02 20:30:22 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][16/234]	eta 0:03:48 lr 0.000013	time 0.8800 (1.0476)	loss 0.4456 (0.4651)	grad_norm 2.6942 (3.0163)	mem 20675MB
[2025-04-02 20:30:24 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][18/234]	eta 0:03:42 lr 0.000013	time 0.8822 (1.0306)	loss 0.5142 (0.4611)	grad_norm 2.5056 (2.9867)	mem 20675MB
[2025-04-02 20:30:26 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][20/234]	eta 0:03:37 lr 0.000013	time 0.8818 (1.0167)	loss 0.5529 (0.4660)	grad_norm 2.3172 (2.9150)	mem 20675MB
[2025-04-02 20:30:28 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][22/234]	eta 0:03:33 lr 0.000013	time 0.8950 (1.0063)	loss 0.2936 (0.4624)	grad_norm 2.9450 (2.9034)	mem 20675MB
[2025-04-02 20:30:29 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][24/234]	eta 0:03:29 lr 0.000013	time 0.8787 (0.9962)	loss 0.4628 (0.4553)	grad_norm 2.1454 (2.8658)	mem 20675MB
[2025-04-02 20:30:31 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][26/234]	eta 0:03:25 lr 0.000012	time 0.8776 (0.9877)	loss 0.5252 (0.4556)	grad_norm 4.5069 (2.9282)	mem 20675MB
[2025-04-02 20:30:33 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][28/234]	eta 0:03:21 lr 0.000012	time 0.8859 (0.9805)	loss 0.3457 (0.4482)	grad_norm 3.0004 (3.0296)	mem 20675MB
[2025-04-02 20:30:35 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][30/234]	eta 0:03:18 lr 0.000012	time 0.8788 (0.9739)	loss 0.4446 (0.4511)	grad_norm 2.1130 (2.9758)	mem 20675MB
[2025-04-02 20:30:36 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][32/234]	eta 0:03:15 lr 0.000012	time 0.8916 (0.9687)	loss 0.5058 (0.4550)	grad_norm 3.1657 (2.9692)	mem 20675MB
[2025-04-02 20:30:38 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][34/234]	eta 0:03:12 lr 0.000012	time 0.8784 (0.9638)	loss 0.5066 (0.4551)	grad_norm 2.7859 (2.9374)	mem 20675MB
[2025-04-02 20:30:40 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][36/234]	eta 0:03:09 lr 0.000012	time 0.8788 (0.9595)	loss 0.4190 (0.4556)	grad_norm 2.2726 (2.8921)	mem 20675MB
[2025-04-02 20:30:42 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][38/234]	eta 0:03:07 lr 0.000012	time 0.8777 (0.9554)	loss 0.5434 (0.4579)	grad_norm 3.7549 (2.9195)	mem 20675MB
[2025-04-02 20:30:43 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][40/234]	eta 0:03:04 lr 0.000012	time 0.8813 (0.9517)	loss 0.4640 (0.4571)	grad_norm 2.6343 (2.9095)	mem 20675MB
[2025-04-02 20:30:45 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][42/234]	eta 0:03:02 lr 0.000012	time 0.8794 (0.9484)	loss 0.5963 (0.4613)	grad_norm 2.4855 (2.8923)	mem 20675MB
[2025-04-02 20:30:47 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][44/234]	eta 0:02:59 lr 0.000011	time 0.8820 (0.9454)	loss 0.3670 (0.4615)	grad_norm 3.3387 (2.9041)	mem 20675MB
[2025-04-02 20:30:49 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][46/234]	eta 0:02:57 lr 0.000011	time 0.8775 (0.9426)	loss 0.4331 (0.4620)	grad_norm 3.0257 (2.8900)	mem 20675MB
[2025-04-02 20:30:51 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][48/234]	eta 0:02:54 lr 0.000011	time 0.8894 (0.9403)	loss 0.3589 (0.4605)	grad_norm 4.4830 (2.8987)	mem 20675MB
[2025-04-02 20:30:52 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][50/234]	eta 0:02:52 lr 0.000011	time 0.8794 (0.9381)	loss 0.3263 (0.4583)	grad_norm 3.3654 (2.9066)	mem 20675MB
[2025-04-02 20:30:54 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][52/234]	eta 0:02:50 lr 0.000011	time 0.8870 (0.9362)	loss 0.4202 (0.4568)	grad_norm 3.8926 (2.9326)	mem 20675MB
[2025-04-02 20:30:56 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][54/234]	eta 0:02:48 lr 0.000011	time 0.8842 (0.9343)	loss 0.4745 (0.4549)	grad_norm 3.0275 (2.9601)	mem 20675MB
[2025-04-02 20:30:58 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][56/234]	eta 0:02:46 lr 0.000011	time 0.8954 (0.9328)	loss 0.3872 (0.4526)	grad_norm 3.9719 (2.9895)	mem 20675MB
[2025-04-02 20:30:59 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][58/234]	eta 0:02:43 lr 0.000011	time 0.8789 (0.9317)	loss 0.5226 (0.4550)	grad_norm 3.1355 (2.9851)	mem 20675MB
[2025-04-02 20:31:01 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][60/234]	eta 0:02:41 lr 0.000011	time 0.8814 (0.9302)	loss 0.4286 (0.4559)	grad_norm 2.1602 (2.9598)	mem 20675MB
[2025-04-02 20:31:03 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][62/234]	eta 0:02:39 lr 0.000011	time 0.8789 (0.9288)	loss 0.4875 (0.4559)	grad_norm 3.0497 (2.9634)	mem 20675MB
[2025-04-02 20:31:05 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][64/234]	eta 0:02:37 lr 0.000010	time 0.8784 (0.9273)	loss 0.5509 (0.4580)	grad_norm 3.3928 (2.9690)	mem 20675MB
[2025-04-02 20:31:07 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][66/234]	eta 0:02:35 lr 0.000010	time 0.8822 (0.9262)	loss 0.4644 (0.4585)	grad_norm 3.2538 (2.9778)	mem 20675MB
[2025-04-02 20:31:08 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][68/234]	eta 0:02:33 lr 0.000010	time 0.8789 (0.9249)	loss 0.4981 (0.4598)	grad_norm 2.2175 (2.9592)	mem 20675MB
[2025-04-02 20:31:10 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][70/234]	eta 0:02:31 lr 0.000010	time 0.8788 (0.9236)	loss 0.4549 (0.4607)	grad_norm 3.1463 (2.9484)	mem 20675MB
[2025-04-02 20:31:12 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][72/234]	eta 0:02:29 lr 0.000010	time 0.8786 (0.9224)	loss 0.5276 (0.4597)	grad_norm 2.1095 (2.9318)	mem 20675MB
[2025-04-02 20:31:14 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][74/234]	eta 0:02:27 lr 0.000010	time 0.8785 (0.9212)	loss 0.5281 (0.4604)	grad_norm 2.4970 (2.9131)	mem 20675MB
[2025-04-02 20:31:15 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][76/234]	eta 0:02:25 lr 0.000010	time 0.8778 (0.9201)	loss 0.3218 (0.4600)	grad_norm 3.0349 (2.9129)	mem 20675MB
[2025-04-02 20:31:17 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][78/234]	eta 0:02:23 lr 0.000010	time 0.8779 (0.9191)	loss 0.3830 (0.4567)	grad_norm 3.9441 (2.9380)	mem 20675MB
[2025-04-02 20:31:19 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][80/234]	eta 0:02:21 lr 0.000010	time 0.8775 (0.9180)	loss 0.4159 (0.4568)	grad_norm 2.1782 (2.9198)	mem 20675MB
[2025-04-02 20:31:21 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][82/234]	eta 0:02:19 lr 0.000010	time 0.8766 (0.9171)	loss 0.5080 (0.4581)	grad_norm 2.3885 (2.9051)	mem 20675MB
[2025-04-02 20:31:22 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][84/234]	eta 0:02:17 lr 0.000009	time 0.8771 (0.9161)	loss 0.3058 (0.4576)	grad_norm 2.4885 (2.8932)	mem 20675MB
[2025-04-02 20:31:24 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][86/234]	eta 0:02:15 lr 0.000009	time 0.8766 (0.9153)	loss 0.5034 (0.4578)	grad_norm 3.1392 (2.8907)	mem 20675MB
[2025-04-02 20:31:26 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][88/234]	eta 0:02:13 lr 0.000009	time 0.8770 (0.9144)	loss 0.3129 (0.4572)	grad_norm 2.8044 (2.8794)	mem 20675MB
[2025-04-02 20:31:28 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][90/234]	eta 0:02:11 lr 0.000009	time 0.8768 (0.9136)	loss 0.5303 (0.4582)	grad_norm 2.3711 (2.8718)	mem 20675MB
[2025-04-02 20:31:29 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][92/234]	eta 0:02:09 lr 0.000009	time 0.8765 (0.9128)	loss 0.5079 (0.4597)	grad_norm 2.6941 (2.8712)	mem 20675MB
[2025-04-02 20:31:31 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][94/234]	eta 0:02:07 lr 0.000009	time 0.8768 (0.9121)	loss 0.4272 (0.4595)	grad_norm 2.5259 (2.8638)	mem 20675MB
[2025-04-02 20:31:33 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][96/234]	eta 0:02:05 lr 0.000009	time 0.8768 (0.9114)	loss 0.4150 (0.4586)	grad_norm 4.2013 (2.8659)	mem 20675MB
[2025-04-02 20:31:35 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][98/234]	eta 0:02:03 lr 0.000009	time 0.8768 (0.9107)	loss 0.4000 (0.4580)	grad_norm 3.4144 (2.8675)	mem 20675MB
[2025-04-02 20:31:36 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][100/234]	eta 0:02:01 lr 0.000009	time 0.8764 (0.9100)	loss 0.4522 (0.4584)	grad_norm 3.2425 (2.8621)	mem 20675MB
[2025-04-02 20:31:38 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][102/234]	eta 0:02:00 lr 0.000009	time 0.8766 (0.9094)	loss 0.3014 (0.4568)	grad_norm 2.3621 (2.8492)	mem 20675MB
[2025-04-02 20:31:40 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][104/234]	eta 0:01:58 lr 0.000009	time 0.8763 (0.9088)	loss 0.4902 (0.4571)	grad_norm 3.0923 (2.8533)	mem 20675MB
[2025-04-02 20:31:42 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][106/234]	eta 0:01:56 lr 0.000008	time 0.8765 (0.9082)	loss 0.4978 (0.4581)	grad_norm 2.6627 (2.8473)	mem 20675MB
[2025-04-02 20:31:43 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][108/234]	eta 0:01:54 lr 0.000008	time 0.8771 (0.9076)	loss 0.5014 (0.4578)	grad_norm 2.3699 (2.8402)	mem 20675MB
[2025-04-02 20:31:45 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][110/234]	eta 0:01:52 lr 0.000008	time 0.8763 (0.9071)	loss 0.3182 (0.4571)	grad_norm 4.0147 (2.8527)	mem 20675MB
[2025-04-02 20:31:47 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][112/234]	eta 0:01:50 lr 0.000008	time 0.8780 (0.9066)	loss 0.4361 (0.4562)	grad_norm 3.1401 (2.8701)	mem 20675MB
[2025-04-02 20:31:49 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][114/234]	eta 0:01:48 lr 0.000008	time 0.8772 (0.9061)	loss 0.4590 (0.4570)	grad_norm 3.3787 (2.8748)	mem 20675MB
[2025-04-02 20:31:50 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][116/234]	eta 0:01:46 lr 0.000008	time 0.8780 (0.9056)	loss 0.3792 (0.4569)	grad_norm 2.6368 (2.8677)	mem 20675MB
[2025-04-02 20:31:52 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][118/234]	eta 0:01:44 lr 0.000008	time 0.8779 (0.9052)	loss 0.3397 (0.4564)	grad_norm 4.3057 (2.8740)	mem 20675MB
[2025-04-02 20:31:54 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][120/234]	eta 0:01:43 lr 0.000008	time 0.8764 (0.9047)	loss 0.4683 (0.4557)	grad_norm 2.5104 (2.8663)	mem 20675MB
[2025-04-02 20:31:56 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][122/234]	eta 0:01:41 lr 0.000008	time 0.8766 (0.9043)	loss 0.3522 (0.4550)	grad_norm 4.0403 (2.8785)	mem 20675MB
[2025-04-02 20:31:57 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][124/234]	eta 0:01:39 lr 0.000008	time 0.8773 (0.9038)	loss 0.4658 (0.4558)	grad_norm 2.4651 (2.8768)	mem 20675MB
[2025-04-02 20:31:59 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][126/234]	eta 0:01:37 lr 0.000008	time 0.8768 (0.9034)	loss 0.3957 (0.4546)	grad_norm 3.2863 (2.8890)	mem 20675MB
[2025-04-02 20:32:01 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][128/234]	eta 0:01:35 lr 0.000007	time 0.8777 (0.9030)	loss 0.5340 (0.4546)	grad_norm 3.4637 (2.9039)	mem 20675MB
[2025-04-02 20:32:03 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][130/234]	eta 0:01:33 lr 0.000007	time 0.8777 (0.9027)	loss 0.4712 (0.4550)	grad_norm 1.8513 (2.8945)	mem 20675MB
[2025-04-02 20:32:04 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][132/234]	eta 0:01:32 lr 0.000007	time 0.8783 (0.9023)	loss 0.4420 (0.4547)	grad_norm 3.2309 (2.8988)	mem 20675MB
[2025-04-02 20:32:06 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][134/234]	eta 0:01:30 lr 0.000007	time 0.8769 (0.9020)	loss 0.4819 (0.4547)	grad_norm 3.1558 (2.9006)	mem 20675MB
[2025-04-02 20:32:08 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][136/234]	eta 0:01:28 lr 0.000007	time 0.8777 (0.9016)	loss 0.4130 (0.4553)	grad_norm 3.7205 (2.9037)	mem 20675MB
[2025-04-02 20:32:10 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][138/234]	eta 0:01:26 lr 0.000007	time 0.8772 (0.9013)	loss 0.3230 (0.4533)	grad_norm 3.7003 (2.9112)	mem 20675MB
[2025-04-02 20:32:12 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][140/234]	eta 0:01:24 lr 0.000007	time 0.8770 (0.9010)	loss 0.5223 (0.4539)	grad_norm 2.5522 (2.9033)	mem 20675MB
[2025-04-02 20:32:13 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][142/234]	eta 0:01:22 lr 0.000007	time 0.8769 (0.9006)	loss 0.5192 (0.4542)	grad_norm 2.2619 (2.9013)	mem 20675MB
[2025-04-02 20:32:15 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][144/234]	eta 0:01:21 lr 0.000007	time 0.8767 (0.9004)	loss 0.5164 (0.4554)	grad_norm 1.8779 (2.8952)	mem 20675MB
[2025-04-02 20:32:17 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][146/234]	eta 0:01:19 lr 0.000007	time 0.8768 (0.9000)	loss 0.5095 (0.4561)	grad_norm 2.6655 (2.8950)	mem 20675MB
[2025-04-02 20:32:19 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][148/234]	eta 0:01:17 lr 0.000007	time 0.8767 (0.8997)	loss 0.3692 (0.4559)	grad_norm 3.3705 (2.8917)	mem 20675MB
[2025-04-02 20:32:20 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][150/234]	eta 0:01:15 lr 0.000007	time 0.8769 (0.8994)	loss 0.4738 (0.4565)	grad_norm 1.8932 (2.8859)	mem 20675MB
[2025-04-02 20:32:22 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][152/234]	eta 0:01:13 lr 0.000006	time 0.8773 (0.8992)	loss 0.4877 (0.4568)	grad_norm 1.8149 (2.8872)	mem 20675MB
[2025-04-02 20:32:24 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][154/234]	eta 0:01:11 lr 0.000006	time 0.8778 (0.8989)	loss 0.5048 (0.4576)	grad_norm 1.9638 (2.8784)	mem 20675MB
[2025-04-02 20:32:26 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][156/234]	eta 0:01:10 lr 0.000006	time 0.8783 (0.8986)	loss 0.4420 (0.4575)	grad_norm 2.4524 (2.8735)	mem 20675MB
[2025-04-02 20:32:27 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][158/234]	eta 0:01:08 lr 0.000006	time 0.8787 (0.8984)	loss 0.4751 (0.4576)	grad_norm 2.0343 (2.8672)	mem 20675MB
[2025-04-02 20:32:29 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][160/234]	eta 0:01:06 lr 0.000006	time 0.8791 (0.8982)	loss 0.4791 (0.4573)	grad_norm 1.9907 (2.8697)	mem 20675MB
[2025-04-02 20:32:31 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][162/234]	eta 0:01:04 lr 0.000006	time 0.8778 (0.8979)	loss 0.4429 (0.4571)	grad_norm 3.6498 (2.8728)	mem 20675MB
[2025-04-02 20:32:33 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][164/234]	eta 0:01:02 lr 0.000006	time 0.8778 (0.8977)	loss 0.5700 (0.4582)	grad_norm 1.9851 (2.8653)	mem 20675MB
[2025-04-02 20:32:34 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][166/234]	eta 0:01:01 lr 0.000006	time 0.8768 (0.8975)	loss 0.5409 (0.4586)	grad_norm 2.6179 (2.8606)	mem 20675MB
[2025-04-02 20:32:36 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][168/234]	eta 0:00:59 lr 0.000006	time 0.8780 (0.8973)	loss 0.4790 (0.4591)	grad_norm 2.0448 (2.8512)	mem 20675MB
[2025-04-02 20:32:38 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][170/234]	eta 0:00:57 lr 0.000006	time 0.8792 (0.8971)	loss 0.4357 (0.4590)	grad_norm 2.6677 (2.8449)	mem 20675MB
[2025-04-02 20:32:40 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][172/234]	eta 0:00:55 lr 0.000006	time 0.8784 (0.8969)	loss 0.5344 (0.4597)	grad_norm 2.3418 (2.8486)	mem 20675MB
[2025-04-02 20:32:41 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][174/234]	eta 0:00:53 lr 0.000006	time 0.8781 (0.8966)	loss 0.4233 (0.4589)	grad_norm 2.2903 (2.8413)	mem 20675MB
[2025-04-02 20:32:43 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][176/234]	eta 0:00:51 lr 0.000006	time 0.8779 (0.8964)	loss 0.4929 (0.4595)	grad_norm 2.3105 (2.8532)	mem 20675MB
[2025-04-02 20:32:45 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][178/234]	eta 0:00:50 lr 0.000006	time 0.8776 (0.8962)	loss 0.5074 (0.4603)	grad_norm 2.2323 (2.8583)	mem 20675MB
[2025-04-02 20:32:47 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][180/234]	eta 0:00:48 lr 0.000005	time 0.8779 (0.8960)	loss 0.3965 (0.4603)	grad_norm 2.8395 (2.8575)	mem 20675MB
[2025-04-02 20:32:48 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][182/234]	eta 0:00:46 lr 0.000005	time 0.8794 (0.8959)	loss 0.4594 (0.4603)	grad_norm 1.7488 (2.8482)	mem 20675MB
[2025-04-02 20:32:50 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][184/234]	eta 0:00:44 lr 0.000005	time 0.8787 (0.8957)	loss 0.5091 (0.4599)	grad_norm 2.2102 (2.8577)	mem 20675MB
[2025-04-02 20:32:52 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][186/234]	eta 0:00:42 lr 0.000005	time 0.8790 (0.8955)	loss 0.3829 (0.4594)	grad_norm 1.7427 (2.8513)	mem 20675MB
[2025-04-02 20:32:54 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][188/234]	eta 0:00:41 lr 0.000005	time 0.8787 (0.8953)	loss 0.4967 (0.4594)	grad_norm 2.0600 (2.8479)	mem 20675MB
[2025-04-02 20:32:55 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][190/234]	eta 0:00:39 lr 0.000005	time 0.8782 (0.8952)	loss 0.4030 (0.4587)	grad_norm 3.7612 (2.8493)	mem 20675MB
[2025-04-02 20:32:57 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][192/234]	eta 0:00:37 lr 0.000005	time 0.8770 (0.8950)	loss 0.4259 (0.4581)	grad_norm 1.9721 (2.8489)	mem 20675MB
[2025-04-02 20:32:59 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][194/234]	eta 0:00:35 lr 0.000005	time 0.8770 (0.8948)	loss 0.3953 (0.4571)	grad_norm 2.6898 (2.8540)	mem 20675MB
[2025-04-02 20:33:01 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][196/234]	eta 0:00:33 lr 0.000005	time 0.8773 (0.8947)	loss 0.3231 (0.4561)	grad_norm 3.3387 (2.8694)	mem 20675MB
[2025-04-02 20:33:02 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][198/234]	eta 0:00:32 lr 0.000005	time 0.8768 (0.8945)	loss 0.5531 (0.4568)	grad_norm 2.2929 (2.8625)	mem 20675MB
[2025-04-02 20:33:04 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][200/234]	eta 0:00:30 lr 0.000005	time 0.8779 (0.8943)	loss 0.4916 (0.4568)	grad_norm 2.3741 (2.8674)	mem 20675MB
[2025-04-02 20:33:06 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][202/234]	eta 0:00:28 lr 0.000005	time 0.8774 (0.8942)	loss 0.4043 (0.4564)	grad_norm 2.3350 (2.8649)	mem 20675MB
[2025-04-02 20:33:08 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][204/234]	eta 0:00:26 lr 0.000005	time 0.8781 (0.8940)	loss 0.3805 (0.4565)	grad_norm 3.4346 (2.8658)	mem 20675MB
[2025-04-02 20:33:10 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][206/234]	eta 0:00:25 lr 0.000005	time 0.8781 (0.8939)	loss 0.3566 (0.4557)	grad_norm 3.6631 (2.8749)	mem 20675MB
[2025-04-02 20:33:11 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][208/234]	eta 0:00:23 lr 0.000004	time 0.8773 (0.8937)	loss 0.4755 (0.4558)	grad_norm 2.0780 (2.8696)	mem 20675MB
[2025-04-02 20:33:13 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][210/234]	eta 0:00:21 lr 0.000004	time 0.8771 (0.8936)	loss 0.5823 (0.4562)	grad_norm 2.5835 (2.8650)	mem 20675MB
[2025-04-02 20:33:15 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][212/234]	eta 0:00:19 lr 0.000004	time 0.8781 (0.8934)	loss 0.3327 (0.4555)	grad_norm 4.9419 (2.8748)	mem 20675MB
[2025-04-02 20:33:17 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][214/234]	eta 0:00:17 lr 0.000004	time 0.8770 (0.8933)	loss 0.4660 (0.4554)	grad_norm 2.0154 (2.8676)	mem 20675MB
[2025-04-02 20:33:18 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][216/234]	eta 0:00:16 lr 0.000004	time 0.8774 (0.8932)	loss 0.3470 (0.4542)	grad_norm 3.2517 (2.8705)	mem 20675MB
[2025-04-02 20:33:20 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][218/234]	eta 0:00:14 lr 0.000004	time 0.8768 (0.8930)	loss 0.3581 (0.4542)	grad_norm 2.2409 (2.8665)	mem 20675MB
[2025-04-02 20:33:22 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][220/234]	eta 0:00:12 lr 0.000004	time 0.8773 (0.8929)	loss 0.4702 (0.4551)	grad_norm 3.2778 (2.8703)	mem 20675MB
[2025-04-02 20:33:24 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][222/234]	eta 0:00:10 lr 0.000004	time 0.8763 (0.8927)	loss 0.5115 (0.4553)	grad_norm 2.2049 (2.8627)	mem 20675MB
[2025-04-02 20:33:25 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][224/234]	eta 0:00:08 lr 0.000004	time 0.8765 (0.8926)	loss 0.4923 (0.4554)	grad_norm 1.9652 (2.8553)	mem 20675MB
[2025-04-02 20:33:27 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][226/234]	eta 0:00:07 lr 0.000004	time 0.8774 (0.8925)	loss 0.3938 (0.4548)	grad_norm 9.1039 (2.8802)	mem 20675MB
[2025-04-02 20:33:29 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][228/234]	eta 0:00:05 lr 0.000004	time 0.8767 (0.8924)	loss 0.5617 (0.4555)	grad_norm 2.5045 (2.8775)	mem 20675MB
[2025-04-02 20:33:31 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][230/234]	eta 0:00:03 lr 0.000004	time 0.8774 (0.8922)	loss 0.3721 (0.4555)	grad_norm 3.6432 (2.8835)	mem 20675MB
[2025-04-02 20:33:32 simmim_finetune] (main_finetune.py 252): INFO Train: [28/30][232/234]	eta 0:00:01 lr 0.000004	time 0.8771 (0.8921)	loss 0.5233 (0.4560)	grad_norm 2.4261 (2.8790)	mem 20675MB
[2025-04-02 20:33:33 simmim_finetune] (main_finetune.py 260): INFO EPOCH 28 training takes 0:03:28
[2025-04-02 20:33:35 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.271 (1.271)	Loss 0.6808 (0.6808)	Acc@1 66.406 (66.406)	Mem 20675MB
[2025-04-02 20:33:35 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 72.928
[2025-04-02 20:33:35 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 72.9%
[2025-04-02 20:33:35 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:33:35 simmim_finetune] (main_finetune.py 184): INFO Current learning rate for different parameter groups: [2.620762515635973e-07, 2.620762515635973e-07, 2.689507224037247e-07, 2.689507224037247e-07, 2.795268313885362e-07, 2.795268313885362e-07, 2.9579776828824615e-07, 2.9579776828824615e-07, 3.2082997890318453e-07, 3.2082997890318453e-07, 3.5934107215693584e-07, 3.5934107215693584e-07, 4.185889079319379e-07, 4.185889079319379e-07, 5.097394245088641e-07, 5.097394245088641e-07, 6.49970988473366e-07, 6.49970988473366e-07, 8.657118561110611e-07, 8.657118561110611e-07, 1.1976208832459767e-06, 1.1976208832459767e-06, 1.7082501557612316e-06, 1.7082501557612316e-06, 2.493833651938547e-06, 2.493833651938547e-06, 3.702423646057493e-06, 3.702423646057493e-06]
[2025-04-02 20:33:37 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][0/234]	eta 0:08:38 lr 0.000004	time 2.2174 (2.2174)	loss 0.4272 (0.4272)	grad_norm 2.1886 (2.1886)	mem 20675MB
[2025-04-02 20:33:39 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][2/234]	eta 0:05:07 lr 0.000004	time 0.8797 (1.3267)	loss 0.5033 (0.4729)	grad_norm 2.3533 (2.0649)	mem 20675MB
[2025-04-02 20:33:41 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][4/234]	eta 0:04:23 lr 0.000004	time 0.8789 (1.1478)	loss 0.5149 (0.4681)	grad_norm 2.5423 (2.2592)	mem 20675MB
[2025-04-02 20:33:42 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][6/234]	eta 0:04:04 lr 0.000003	time 0.8795 (1.0712)	loss 0.4817 (0.4780)	grad_norm 3.1943 (2.4231)	mem 20675MB
[2025-04-02 20:33:44 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][8/234]	eta 0:03:52 lr 0.000003	time 0.8774 (1.0285)	loss 0.4877 (0.4817)	grad_norm 3.2631 (2.5245)	mem 20675MB
[2025-04-02 20:33:46 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][10/234]	eta 0:03:44 lr 0.000003	time 0.8769 (1.0011)	loss 0.5294 (0.4877)	grad_norm 4.1709 (2.6770)	mem 20675MB
[2025-04-02 20:33:48 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][12/234]	eta 0:03:38 lr 0.000003	time 0.8780 (0.9822)	loss 0.3694 (0.4679)	grad_norm 3.4044 (2.8636)	mem 20675MB
[2025-04-02 20:33:49 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][14/234]	eta 0:03:33 lr 0.000003	time 0.8790 (0.9685)	loss 0.5807 (0.4729)	grad_norm 2.7876 (2.9515)	mem 20675MB
[2025-04-02 20:33:51 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][16/234]	eta 0:03:28 lr 0.000003	time 0.8783 (0.9580)	loss 0.4638 (0.4727)	grad_norm 2.4881 (2.8815)	mem 20675MB
[2025-04-02 20:33:53 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][18/234]	eta 0:03:25 lr 0.000003	time 0.8776 (0.9496)	loss 0.4426 (0.4625)	grad_norm 2.3509 (2.8933)	mem 20675MB
[2025-04-02 20:33:55 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][20/234]	eta 0:03:21 lr 0.000003	time 0.8784 (0.9429)	loss 0.5141 (0.4586)	grad_norm 2.4118 (2.8852)	mem 20675MB
[2025-04-02 20:33:56 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][22/234]	eta 0:03:18 lr 0.000003	time 0.8779 (0.9373)	loss 0.5894 (0.4686)	grad_norm 2.5800 (2.8723)	mem 20675MB
[2025-04-02 20:33:58 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][24/234]	eta 0:03:15 lr 0.000003	time 0.8776 (0.9326)	loss 0.5048 (0.4685)	grad_norm 1.7157 (2.8020)	mem 20675MB
[2025-04-02 20:34:00 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][26/234]	eta 0:03:13 lr 0.000003	time 0.8785 (0.9286)	loss 0.3025 (0.4627)	grad_norm 2.4911 (2.7829)	mem 20675MB
[2025-04-02 20:34:02 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][28/234]	eta 0:03:10 lr 0.000003	time 0.8801 (0.9252)	loss 0.4005 (0.4638)	grad_norm 4.4616 (2.8271)	mem 20675MB
[2025-04-02 20:34:03 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][30/234]	eta 0:03:08 lr 0.000003	time 0.8782 (0.9224)	loss 0.5774 (0.4670)	grad_norm 3.6198 (2.8612)	mem 20675MB
[2025-04-02 20:34:05 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][32/234]	eta 0:03:05 lr 0.000003	time 0.8767 (0.9197)	loss 0.2764 (0.4650)	grad_norm 2.5327 (2.8582)	mem 20675MB
[2025-04-02 20:34:07 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][34/234]	eta 0:03:03 lr 0.000003	time 0.8773 (0.9173)	loss 0.4405 (0.4666)	grad_norm 2.7582 (2.8653)	mem 20675MB
[2025-04-02 20:34:09 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][36/234]	eta 0:03:01 lr 0.000003	time 0.8776 (0.9152)	loss 0.4005 (0.4654)	grad_norm 3.3845 (2.8544)	mem 20675MB
[2025-04-02 20:34:10 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][38/234]	eta 0:02:59 lr 0.000003	time 0.8779 (0.9133)	loss 0.4707 (0.4641)	grad_norm 3.9291 (2.8790)	mem 20675MB
[2025-04-02 20:34:12 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][40/234]	eta 0:02:56 lr 0.000003	time 0.8781 (0.9116)	loss 0.5190 (0.4649)	grad_norm 2.9977 (2.8643)	mem 20675MB
[2025-04-02 20:34:14 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][42/234]	eta 0:02:54 lr 0.000003	time 0.8771 (0.9101)	loss 0.5548 (0.4658)	grad_norm 2.4120 (2.8424)	mem 20675MB
[2025-04-02 20:34:16 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][44/234]	eta 0:02:52 lr 0.000003	time 0.8770 (0.9087)	loss 0.4299 (0.4644)	grad_norm 2.3195 (2.8040)	mem 20675MB
[2025-04-02 20:34:17 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][46/234]	eta 0:02:50 lr 0.000002	time 0.8785 (0.9074)	loss 0.3401 (0.4596)	grad_norm 4.0875 (2.8269)	mem 20675MB
[2025-04-02 20:34:19 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][48/234]	eta 0:02:48 lr 0.000002	time 0.8775 (0.9062)	loss 0.5561 (0.4614)	grad_norm 3.5168 (2.8354)	mem 20675MB
[2025-04-02 20:34:21 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][50/234]	eta 0:02:46 lr 0.000002	time 0.8776 (0.9051)	loss 0.3099 (0.4553)	grad_norm 4.5658 (2.8645)	mem 20675MB
[2025-04-02 20:34:23 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][52/234]	eta 0:02:44 lr 0.000002	time 0.8777 (0.9041)	loss 0.5420 (0.4590)	grad_norm 2.2819 (2.8600)	mem 20675MB
[2025-04-02 20:34:25 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][54/234]	eta 0:02:42 lr 0.000002	time 0.8777 (0.9032)	loss 0.3667 (0.4578)	grad_norm 3.1868 (2.8531)	mem 20675MB
[2025-04-02 20:34:26 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][56/234]	eta 0:02:40 lr 0.000002	time 0.8781 (0.9023)	loss 0.5772 (0.4605)	grad_norm 2.2641 (2.8307)	mem 20675MB
[2025-04-02 20:34:28 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][58/234]	eta 0:02:38 lr 0.000002	time 0.8783 (0.9015)	loss 0.3583 (0.4597)	grad_norm 3.5561 (2.8344)	mem 20675MB
[2025-04-02 20:34:30 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][60/234]	eta 0:02:36 lr 0.000002	time 0.8764 (0.9007)	loss 0.3704 (0.4600)	grad_norm 5.1404 (2.8675)	mem 20675MB
[2025-04-02 20:34:32 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][62/234]	eta 0:02:34 lr 0.000002	time 0.8776 (0.9000)	loss 0.3802 (0.4599)	grad_norm 2.8829 (2.8644)	mem 20675MB
[2025-04-02 20:34:33 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][64/234]	eta 0:02:32 lr 0.000002	time 0.8775 (0.8994)	loss 0.3287 (0.4568)	grad_norm 2.9976 (2.8652)	mem 20675MB
[2025-04-02 20:34:35 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][66/234]	eta 0:02:30 lr 0.000002	time 0.8774 (0.8987)	loss 0.4067 (0.4557)	grad_norm 2.7957 (2.8726)	mem 20675MB
[2025-04-02 20:34:37 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][68/234]	eta 0:02:29 lr 0.000002	time 0.8789 (0.8982)	loss 0.2995 (0.4536)	grad_norm 2.6559 (2.8637)	mem 20675MB
[2025-04-02 20:34:39 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][70/234]	eta 0:02:27 lr 0.000002	time 0.8774 (0.8976)	loss 0.2747 (0.4507)	grad_norm 2.7030 (2.8565)	mem 20675MB
[2025-04-02 20:34:40 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][72/234]	eta 0:02:25 lr 0.000002	time 0.8775 (0.8971)	loss 0.5149 (0.4506)	grad_norm 2.4719 (2.8507)	mem 20675MB
[2025-04-02 20:34:42 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][74/234]	eta 0:02:23 lr 0.000002	time 0.8772 (0.8966)	loss 0.5322 (0.4527)	grad_norm 2.1030 (2.8391)	mem 20675MB
[2025-04-02 20:34:44 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][76/234]	eta 0:02:21 lr 0.000002	time 0.8768 (0.8961)	loss 0.5036 (0.4519)	grad_norm 3.4335 (2.8407)	mem 20675MB
[2025-04-02 20:34:46 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][78/234]	eta 0:02:19 lr 0.000002	time 0.8769 (0.8956)	loss 0.4185 (0.4506)	grad_norm 2.2198 (2.8244)	mem 20675MB
[2025-04-02 20:34:47 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][80/234]	eta 0:02:17 lr 0.000002	time 0.8786 (0.8952)	loss 0.5008 (0.4501)	grad_norm 2.5920 (2.8249)	mem 20675MB
[2025-04-02 20:34:49 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][82/234]	eta 0:02:16 lr 0.000002	time 0.8774 (0.8948)	loss 0.4980 (0.4489)	grad_norm 3.1571 (2.8248)	mem 20675MB
[2025-04-02 20:34:51 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][84/234]	eta 0:02:14 lr 0.000002	time 0.8766 (0.8945)	loss 0.3610 (0.4481)	grad_norm 3.2352 (2.8488)	mem 20675MB
[2025-04-02 20:34:53 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][86/234]	eta 0:02:12 lr 0.000002	time 0.8767 (0.8941)	loss 0.5402 (0.4500)	grad_norm 2.6753 (2.8418)	mem 20675MB
[2025-04-02 20:34:54 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][88/234]	eta 0:02:10 lr 0.000002	time 0.8770 (0.8937)	loss 0.5414 (0.4514)	grad_norm 4.0167 (2.8430)	mem 20675MB
[2025-04-02 20:34:56 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][90/234]	eta 0:02:08 lr 0.000002	time 0.8767 (0.8934)	loss 0.3515 (0.4495)	grad_norm 5.0189 (2.8786)	mem 20675MB
[2025-04-02 20:34:58 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][92/234]	eta 0:02:06 lr 0.000002	time 0.8783 (0.8930)	loss 0.3306 (0.4489)	grad_norm 4.6566 (2.8879)	mem 20675MB
[2025-04-02 20:35:00 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][94/234]	eta 0:02:04 lr 0.000001	time 0.8782 (0.8927)	loss 0.4589 (0.4487)	grad_norm 2.7994 (2.8813)	mem 20675MB
[2025-04-02 20:35:01 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][96/234]	eta 0:02:03 lr 0.000001	time 0.8776 (0.8925)	loss 0.4444 (0.4494)	grad_norm 2.1771 (2.8846)	mem 20675MB
[2025-04-02 20:35:03 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][98/234]	eta 0:02:01 lr 0.000001	time 0.8780 (0.8922)	loss 0.3181 (0.4490)	grad_norm 3.3534 (2.8897)	mem 20675MB
[2025-04-02 20:35:05 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][100/234]	eta 0:01:59 lr 0.000001	time 0.8778 (0.8919)	loss 0.4280 (0.4499)	grad_norm 2.4611 (2.8951)	mem 20675MB
[2025-04-02 20:35:07 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][102/234]	eta 0:01:57 lr 0.000001	time 0.8766 (0.8916)	loss 0.5098 (0.4490)	grad_norm 3.3568 (2.8959)	mem 20675MB
[2025-04-02 20:35:08 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][104/234]	eta 0:01:55 lr 0.000001	time 0.8774 (0.8914)	loss 0.5757 (0.4506)	grad_norm 3.4379 (2.8920)	mem 20675MB
[2025-04-02 20:35:10 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][106/234]	eta 0:01:54 lr 0.000001	time 0.8771 (0.8911)	loss 0.5155 (0.4516)	grad_norm 3.7485 (2.8988)	mem 20675MB
[2025-04-02 20:35:12 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][108/234]	eta 0:01:52 lr 0.000001	time 0.8769 (0.8909)	loss 0.5073 (0.4507)	grad_norm 2.4745 (2.8936)	mem 20675MB
[2025-04-02 20:35:14 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][110/234]	eta 0:01:50 lr 0.000001	time 0.8782 (0.8907)	loss 0.3735 (0.4504)	grad_norm 2.7585 (2.8867)	mem 20675MB
[2025-04-02 20:35:15 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][112/234]	eta 0:01:48 lr 0.000001	time 0.8779 (0.8905)	loss 0.5201 (0.4513)	grad_norm 2.5524 (2.8789)	mem 20675MB
[2025-04-02 20:35:17 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][114/234]	eta 0:01:46 lr 0.000001	time 0.8778 (0.8903)	loss 0.3253 (0.4502)	grad_norm 2.9474 (2.8811)	mem 20675MB
[2025-04-02 20:35:19 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][116/234]	eta 0:01:45 lr 0.000001	time 0.8774 (0.8901)	loss 0.4758 (0.4513)	grad_norm 1.9685 (2.8698)	mem 20675MB
[2025-04-02 20:35:21 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][118/234]	eta 0:01:43 lr 0.000001	time 0.8777 (0.8899)	loss 0.3499 (0.4498)	grad_norm 3.4116 (2.8685)	mem 20675MB
[2025-04-02 20:35:23 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][120/234]	eta 0:01:41 lr 0.000001	time 0.8775 (0.8897)	loss 0.5359 (0.4493)	grad_norm 2.3074 (2.8678)	mem 20675MB
[2025-04-02 20:35:24 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][122/234]	eta 0:01:39 lr 0.000001	time 0.8768 (0.8895)	loss 0.4509 (0.4503)	grad_norm 3.4957 (2.8685)	mem 20675MB
[2025-04-02 20:35:26 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][124/234]	eta 0:01:37 lr 0.000001	time 0.8767 (0.8893)	loss 0.3114 (0.4487)	grad_norm 3.0984 (2.8701)	mem 20675MB
[2025-04-02 20:35:28 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][126/234]	eta 0:01:36 lr 0.000001	time 0.8778 (0.8891)	loss 0.6099 (0.4491)	grad_norm 2.6601 (2.8771)	mem 20675MB
[2025-04-02 20:35:30 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][128/234]	eta 0:01:34 lr 0.000001	time 0.8767 (0.8890)	loss 0.5657 (0.4508)	grad_norm 2.3002 (2.8781)	mem 20675MB
[2025-04-02 20:35:31 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][130/234]	eta 0:01:32 lr 0.000001	time 0.8771 (0.8888)	loss 0.4557 (0.4505)	grad_norm 3.4088 (2.8810)	mem 20675MB
[2025-04-02 20:35:33 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][132/234]	eta 0:01:30 lr 0.000001	time 0.8763 (0.8886)	loss 0.5167 (0.4510)	grad_norm 3.6055 (2.8772)	mem 20675MB
[2025-04-02 20:35:35 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][134/234]	eta 0:01:28 lr 0.000001	time 0.8764 (0.8885)	loss 0.4716 (0.4502)	grad_norm 3.1831 (2.8845)	mem 20675MB
[2025-04-02 20:35:37 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][136/234]	eta 0:01:27 lr 0.000001	time 0.8772 (0.8883)	loss 0.3623 (0.4505)	grad_norm 2.6834 (2.8841)	mem 20675MB
[2025-04-02 20:35:38 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][138/234]	eta 0:01:25 lr 0.000001	time 0.8768 (0.8882)	loss 0.3659 (0.4502)	grad_norm 2.5557 (2.8772)	mem 20675MB
[2025-04-02 20:35:40 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][140/234]	eta 0:01:23 lr 0.000001	time 0.8775 (0.8880)	loss 0.4783 (0.4499)	grad_norm 2.4951 (2.8758)	mem 20675MB
[2025-04-02 20:35:42 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][142/234]	eta 0:01:21 lr 0.000001	time 0.8770 (0.8879)	loss 0.5095 (0.4506)	grad_norm 2.3323 (2.8702)	mem 20675MB
[2025-04-02 20:35:44 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][144/234]	eta 0:01:19 lr 0.000001	time 0.8770 (0.8877)	loss 0.3653 (0.4505)	grad_norm 2.7163 (2.8732)	mem 20675MB
[2025-04-02 20:35:45 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][146/234]	eta 0:01:18 lr 0.000001	time 0.8766 (0.8876)	loss 0.3973 (0.4509)	grad_norm 3.4367 (2.8793)	mem 20675MB
[2025-04-02 20:35:47 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][148/234]	eta 0:01:16 lr 0.000001	time 0.8765 (0.8875)	loss 0.4813 (0.4510)	grad_norm 2.9056 (2.8755)	mem 20675MB
[2025-04-02 20:35:49 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][150/234]	eta 0:01:14 lr 0.000001	time 0.8770 (0.8873)	loss 0.3307 (0.4508)	grad_norm 3.7760 (2.8766)	mem 20675MB
[2025-04-02 20:35:51 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][152/234]	eta 0:01:12 lr 0.000001	time 0.8764 (0.8872)	loss 0.5044 (0.4513)	grad_norm 2.7945 (2.8759)	mem 20675MB
[2025-04-02 20:35:52 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][154/234]	eta 0:01:10 lr 0.000001	time 0.8769 (0.8871)	loss 0.3809 (0.4508)	grad_norm 2.1327 (2.8747)	mem 20675MB
[2025-04-02 20:35:54 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][156/234]	eta 0:01:09 lr 0.000001	time 0.8775 (0.8870)	loss 0.4549 (0.4498)	grad_norm 3.2700 (2.8743)	mem 20675MB
[2025-04-02 20:35:56 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][158/234]	eta 0:01:07 lr 0.000001	time 0.8777 (0.8869)	loss 0.3354 (0.4495)	grad_norm 3.5433 (2.8839)	mem 20675MB
[2025-04-02 20:35:58 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][160/234]	eta 0:01:05 lr 0.000001	time 0.8782 (0.8868)	loss 0.4912 (0.4502)	grad_norm 2.4769 (2.8802)	mem 20675MB
[2025-04-02 20:35:59 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][162/234]	eta 0:01:03 lr 0.000001	time 0.8772 (0.8866)	loss 0.5356 (0.4501)	grad_norm 2.1317 (2.8712)	mem 20675MB
[2025-04-02 20:36:01 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][164/234]	eta 0:01:02 lr 0.000001	time 0.8770 (0.8865)	loss 0.5089 (0.4496)	grad_norm 2.4735 (2.8736)	mem 20675MB
[2025-04-02 20:36:03 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][166/234]	eta 0:01:00 lr 0.000001	time 0.8768 (0.8864)	loss 0.2623 (0.4486)	grad_norm 2.4074 (2.8654)	mem 20675MB
[2025-04-02 20:36:05 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][168/234]	eta 0:00:58 lr 0.000001	time 0.8767 (0.8863)	loss 0.3108 (0.4468)	grad_norm 2.8698 (2.8620)	mem 20675MB
[2025-04-02 20:36:06 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][170/234]	eta 0:00:56 lr 0.000001	time 0.8772 (0.8862)	loss 0.4329 (0.4469)	grad_norm 3.6563 (2.8674)	mem 20675MB
[2025-04-02 20:36:08 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][172/234]	eta 0:00:54 lr 0.000000	time 0.8771 (0.8861)	loss 0.5350 (0.4470)	grad_norm 2.2364 (2.8651)	mem 20675MB
[2025-04-02 20:36:10 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][174/234]	eta 0:00:53 lr 0.000000	time 0.8773 (0.8860)	loss 0.3876 (0.4468)	grad_norm 3.2546 (2.8659)	mem 20675MB
[2025-04-02 20:36:12 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][176/234]	eta 0:00:51 lr 0.000000	time 0.8768 (0.8860)	loss 0.5046 (0.4471)	grad_norm 1.8568 (2.8578)	mem 20675MB
[2025-04-02 20:36:13 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][178/234]	eta 0:00:49 lr 0.000000	time 0.8765 (0.8859)	loss 0.5121 (0.4478)	grad_norm 2.7073 (2.8559)	mem 20675MB
[2025-04-02 20:36:15 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][180/234]	eta 0:00:47 lr 0.000000	time 0.8767 (0.8858)	loss 0.3599 (0.4473)	grad_norm 3.1554 (2.8579)	mem 20675MB
[2025-04-02 20:36:17 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][182/234]	eta 0:00:46 lr 0.000000	time 0.8768 (0.8857)	loss 0.4277 (0.4474)	grad_norm 1.8907 (2.8554)	mem 20675MB
[2025-04-02 20:36:19 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][184/234]	eta 0:00:44 lr 0.000000	time 0.8771 (0.8856)	loss 0.5294 (0.4475)	grad_norm 2.7705 (2.8564)	mem 20675MB
[2025-04-02 20:36:20 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][186/234]	eta 0:00:42 lr 0.000000	time 0.8766 (0.8855)	loss 0.4498 (0.4476)	grad_norm 3.3231 (2.8538)	mem 20675MB
[2025-04-02 20:36:22 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][188/234]	eta 0:00:40 lr 0.000000	time 0.8767 (0.8854)	loss 0.3233 (0.4469)	grad_norm 4.1293 (2.8654)	mem 20675MB
[2025-04-02 20:36:24 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][190/234]	eta 0:00:38 lr 0.000000	time 0.8770 (0.8853)	loss 0.5701 (0.4476)	grad_norm 2.6169 (2.8620)	mem 20675MB
[2025-04-02 20:36:26 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][192/234]	eta 0:00:37 lr 0.000000	time 0.8763 (0.8853)	loss 0.4872 (0.4479)	grad_norm 1.7572 (2.8491)	mem 20675MB
[2025-04-02 20:36:27 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][194/234]	eta 0:00:35 lr 0.000000	time 0.8771 (0.8852)	loss 0.4196 (0.4480)	grad_norm 3.5051 (2.8534)	mem 20675MB
[2025-04-02 20:36:29 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][196/234]	eta 0:00:33 lr 0.000000	time 0.8766 (0.8851)	loss 0.4547 (0.4483)	grad_norm 1.6126 (2.8499)	mem 20675MB
[2025-04-02 20:36:31 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][198/234]	eta 0:00:31 lr 0.000000	time 0.8768 (0.8850)	loss 0.5509 (0.4481)	grad_norm 3.2504 (2.8507)	mem 20675MB
[2025-04-02 20:36:33 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][200/234]	eta 0:00:30 lr 0.000000	time 0.8763 (0.8850)	loss 0.4252 (0.4477)	grad_norm 3.3744 (2.8666)	mem 20675MB
[2025-04-02 20:36:34 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][202/234]	eta 0:00:28 lr 0.000000	time 0.8766 (0.8849)	loss 0.5042 (0.4478)	grad_norm 3.2728 (2.8645)	mem 20675MB
[2025-04-02 20:36:36 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][204/234]	eta 0:00:26 lr 0.000000	time 0.8765 (0.8848)	loss 0.5470 (0.4475)	grad_norm 2.2115 (2.8621)	mem 20675MB
[2025-04-02 20:36:38 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][206/234]	eta 0:00:24 lr 0.000000	time 0.8767 (0.8847)	loss 0.5339 (0.4482)	grad_norm 2.7213 (2.8611)	mem 20675MB
[2025-04-02 20:36:40 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][208/234]	eta 0:00:23 lr 0.000000	time 0.8771 (0.8847)	loss 0.3672 (0.4482)	grad_norm 3.1050 (2.8630)	mem 20675MB
[2025-04-02 20:36:42 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][210/234]	eta 0:00:21 lr 0.000000	time 0.8771 (0.8846)	loss 0.5408 (0.4482)	grad_norm 2.2484 (2.8608)	mem 20675MB
[2025-04-02 20:36:43 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][212/234]	eta 0:00:19 lr 0.000000	time 0.8773 (0.8845)	loss 0.3479 (0.4479)	grad_norm 2.8660 (2.8584)	mem 20675MB
[2025-04-02 20:36:45 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][214/234]	eta 0:00:17 lr 0.000000	time 0.8776 (0.8845)	loss 0.3657 (0.4474)	grad_norm 2.5779 (2.8574)	mem 20675MB
[2025-04-02 20:36:47 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][216/234]	eta 0:00:15 lr 0.000000	time 0.8772 (0.8844)	loss 0.4556 (0.4468)	grad_norm 2.0325 (2.8514)	mem 20675MB
[2025-04-02 20:36:49 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][218/234]	eta 0:00:14 lr 0.000000	time 0.8783 (0.8844)	loss 0.4377 (0.4462)	grad_norm 1.9161 (2.8501)	mem 20675MB
[2025-04-02 20:36:50 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][220/234]	eta 0:00:12 lr 0.000000	time 0.8771 (0.8843)	loss 0.4194 (0.4458)	grad_norm 3.8697 (2.8654)	mem 20675MB
[2025-04-02 20:36:52 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][222/234]	eta 0:00:10 lr 0.000000	time 0.8765 (0.8843)	loss 0.4651 (0.4464)	grad_norm 2.9991 (2.8659)	mem 20675MB
[2025-04-02 20:36:54 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][224/234]	eta 0:00:08 lr 0.000000	time 0.8767 (0.8842)	loss 0.5109 (0.4467)	grad_norm 2.2967 (2.8661)	mem 20675MB
[2025-04-02 20:36:56 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][226/234]	eta 0:00:07 lr 0.000000	time 0.8765 (0.8841)	loss 0.3855 (0.4466)	grad_norm 2.6797 (2.8596)	mem 20675MB
[2025-04-02 20:36:57 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][228/234]	eta 0:00:05 lr 0.000000	time 0.8767 (0.8841)	loss 0.4887 (0.4469)	grad_norm 2.3750 (2.8578)	mem 20675MB
[2025-04-02 20:36:59 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][230/234]	eta 0:00:03 lr 0.000000	time 0.8776 (0.8840)	loss 0.4876 (0.4473)	grad_norm 2.3999 (2.8530)	mem 20675MB
[2025-04-02 20:37:01 simmim_finetune] (main_finetune.py 252): INFO Train: [29/30][232/234]	eta 0:00:01 lr 0.000000	time 0.8781 (0.8840)	loss 0.2711 (0.4466)	grad_norm 2.3663 (2.8485)	mem 20675MB
[2025-04-02 20:37:02 simmim_finetune] (main_finetune.py 260): INFO EPOCH 29 training takes 0:03:26
[2025-04-02 20:37:02 simmim_finetune] (utils.py 60): INFO checkpoint/face/ckpt29.pth saving......
[2025-04-02 20:37:06 simmim_finetune] (utils.py 62): INFO checkpoint/face/ckpt29.pth saved !!!
[2025-04-02 20:37:07 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 1.305 (1.305)	Loss 0.6920 (0.6920)	Acc@1 64.844 (64.844)	Mem 20675MB
[2025-04-02 20:37:07 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 71.823
[2025-04-02 20:37:07 simmim_finetune] (main_finetune.py 171): INFO Accuracy of the network on the 181 test images: 71.8%
[2025-04-02 20:37:07 simmim_finetune] (main_finetune.py 173): INFO Max accuracy: 74.03%
[2025-04-02 20:37:07 simmim_finetune] (main_finetune.py 177): INFO Training time 1:45:11
[2025-04-02 20:44:17 simmim_finetune] (main_finetune.py 375): INFO Full config saved to checkpoint/face/config.json
[2025-04-02 20:44:17 simmim_finetune] (main_finetune.py 378): INFO AMP_OPT_LEVEL: O0
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 1.0
  CUTMIX_MINMAX: null
  MIXUP: 0.8
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: ''
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  MASK_PATCH_SIZE: 32
  MASK_RATIO: 0.6
  NUM_WORKERS: 8
  PIN_MEMORY: true
  TRAIN_PATH: VBench-2.0_human_anomaly/dataset/hand_train.jsonl
  VAL_PATH: VBench-2.0_human_anomaly/dataset/hand_test.jsonl
EVAL_MODE: false
LOCAL_RANK: 0
LOSS:
  FOCAL: false
  FOCAL_ALPHA: 0.25
  FOCAL_GAMMA: 2.0
MODEL:
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NAME: simmim_finetune
  NUM_CLASSES: 2
  RESUME: ''
  SWIN:
    APE: false
    DEPTHS:
    - 2
    - 2
    - 6
    - 2
    EMBED_DIM: 96
    IN_CHANS: 3
    MLP_RATIO: 4.0
    NUM_HEADS:
    - 3
    - 6
    - 12
    - 24
    PATCH_NORM: true
    PATCH_SIZE: 4
    QKV_BIAS: true
    QK_SCALE: null
    WINDOW_SIZE: 7
  TYPE: vit
  VIT:
    DEPTH: 12
    EMBED_DIM: 768
    INIT_VALUES: 0.1
    IN_CHANS: 3
    MLP_RATIO: 4
    NUM_HEADS: 12
    PATCH_SIZE: 16
    QKV_BIAS: true
    USE_APE: false
    USE_MEAN_POOLING: true
    USE_RPB: true
    USE_SHARED_RPB: false
OUTPUT: checkpoint/face
PRETRAINED: pretrain/simmim_pretrain__vit_base__img224__800ep.pth
PRINT_FREQ: 2
SAVE_FREQ: 5
SEED: 0
TAG: simmim_finetune__vit_base__img224__800ep
TEST:
  CROP: true
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: true
  BASE_LR: 0.00125
  CLIP_GRAD: 5.0
  EPOCHS: 30
  LAYER_DECAY: 0.65
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    GAMMA: 0.1
    MULTISTEPS: []
    NAME: cosine
  MIN_LR: 2.5e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: adamw
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 3
  WARMUP_LR: 2.5e-07
  WEIGHT_DECAY: 0.05

[2025-04-02 20:44:17 simmim_finetune] (data_finetune.py 88): INFO Fine-tune data transform, is_train=True:
Compose(
    RandomResizedCropAndInterpolation(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BICUBIC)
    RandomHorizontalFlip(p=0.5)
    <timm.data.auto_augment.RandAugment object at 0x7f51f1d111e0>
    ToTensor()
    Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))
    <timm.data.random_erasing.RandomErasing object at 0x7f51f1d115d0>
)
[2025-04-02 20:44:18 simmim_finetune] (data_finetune.py 88): INFO Fine-tune data transform, is_train=False:
Compose(
    Resize(size=256, interpolation=bicubic, max_size=None, antialias=True)
    CenterCrop(size=(224, 224))
    ToTensor()
    Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
)
[2025-04-02 20:44:18 simmim_finetune] (data_finetune.py 26): INFO Build dataset: train images = 39848, val images = 142
[2025-04-02 20:44:18 simmim_finetune] (main_finetune.py 102): INFO Creating model:vit/simmim_finetune
[2025-04-02 20:44:27 simmim_finetune] (main_finetune.py 105): INFO VisionTransformer(
  (patch_embed): PatchEmbed(
    (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
  )
  (pos_drop): Dropout(p=0.0, inplace=False)
  (blocks): ModuleList(
    (0): Block(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=False)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): Identity()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (1-11): 11 x Block(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=False)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): Identity()
  (fc_norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
  (head): Linear(in_features=768, out_features=2, bias=True)
)
[2025-04-02 20:44:27 simmim_finetune] (optimizer.py 70): INFO >>>>>>>>>> Build Optimizer for Fine-tuning Stage
[2025-04-02 20:44:27 simmim_finetune] (optimizer.py 87): INFO No weight decay: {'pos_embed', 'cls_token'}
[2025-04-02 20:44:27 simmim_finetune] (optimizer.py 182): INFO Param groups = {
  "layer_0_no_decay": {
    "group_name": "layer_0_no_decay",
    "weight_decay": 0.0,
    "params": [
      "cls_token",
      "patch_embed.proj.bias"
    ],
    "lr": 4.621507363773394e-06,
    "lr_scale": 0.003697205891018715
  },
  "layer_0_decay": {
    "group_name": "layer_0_decay",
    "weight_decay": 0.05,
    "params": [
      "patch_embed.proj.weight"
    ],
    "lr": 4.621507363773394e-06,
    "lr_scale": 0.003697205891018715
  },
  "layer_1_no_decay": {
    "group_name": "layer_1_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.0.gamma_1",
      "blocks.0.gamma_2",
      "blocks.0.norm1.weight",
      "blocks.0.norm1.bias",
      "blocks.0.attn.q_bias",
      "blocks.0.attn.v_bias",
      "blocks.0.attn.proj.bias",
      "blocks.0.norm2.weight",
      "blocks.0.norm2.bias",
      "blocks.0.mlp.fc1.bias",
      "blocks.0.mlp.fc2.bias"
    ],
    "lr": 7.110011328882144e-06,
    "lr_scale": 0.005688009063105715
  },
  "layer_1_decay": {
    "group_name": "layer_1_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.0.attn.relative_position_bias_table",
      "blocks.0.attn.qkv.weight",
      "blocks.0.attn.proj.weight",
      "blocks.0.mlp.fc1.weight",
      "blocks.0.mlp.fc2.weight"
    ],
    "lr": 7.110011328882144e-06,
    "lr_scale": 0.005688009063105715
  },
  "layer_2_no_decay": {
    "group_name": "layer_2_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.1.gamma_1",
      "blocks.1.gamma_2",
      "blocks.1.norm1.weight",
      "blocks.1.norm1.bias",
      "blocks.1.attn.q_bias",
      "blocks.1.attn.v_bias",
      "blocks.1.attn.proj.bias",
      "blocks.1.norm2.weight",
      "blocks.1.norm2.bias",
      "blocks.1.mlp.fc1.bias",
      "blocks.1.mlp.fc2.bias"
    ],
    "lr": 1.093847896751099e-05,
    "lr_scale": 0.008750783174008792
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  "layer_2_decay": {
    "group_name": "layer_2_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.1.attn.relative_position_bias_table",
      "blocks.1.attn.qkv.weight",
      "blocks.1.attn.proj.weight",
      "blocks.1.mlp.fc1.weight",
      "blocks.1.mlp.fc2.weight"
    ],
    "lr": 1.093847896751099e-05,
    "lr_scale": 0.008750783174008792
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  "layer_3_no_decay": {
    "group_name": "layer_3_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.2.gamma_1",
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      "blocks.2.attn.q_bias",
      "blocks.2.attn.v_bias",
      "blocks.2.attn.proj.bias",
      "blocks.2.norm2.weight",
      "blocks.2.norm2.bias",
      "blocks.2.mlp.fc1.bias",
      "blocks.2.mlp.fc2.bias"
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    "lr": 1.682842918078614e-05,
    "lr_scale": 0.013462743344628911
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  "layer_3_decay": {
    "group_name": "layer_3_decay",
    "weight_decay": 0.05,
    "params": [
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      "blocks.2.attn.qkv.weight",
      "blocks.2.attn.proj.weight",
      "blocks.2.mlp.fc1.weight",
      "blocks.2.mlp.fc2.weight"
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    "lr": 1.682842918078614e-05,
    "lr_scale": 0.013462743344628911
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  "layer_4_no_decay": {
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    "weight_decay": 0.0,
    "params": [
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      "blocks.3.gamma_2",
      "blocks.3.norm1.weight",
      "blocks.3.norm1.bias",
      "blocks.3.attn.q_bias",
      "blocks.3.attn.v_bias",
      "blocks.3.attn.proj.bias",
      "blocks.3.norm2.weight",
      "blocks.3.norm2.bias",
      "blocks.3.mlp.fc1.bias",
      "blocks.3.mlp.fc2.bias"
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    "lr": 2.588989104736329e-05,
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  "layer_4_decay": {
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    "params": [
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    "lr": 2.588989104736329e-05,
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  "layer_5_no_decay": {
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    "weight_decay": 0.0,
    "params": [
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      "blocks.4.gamma_2",
      "blocks.4.norm1.weight",
      "blocks.4.norm1.bias",
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      "blocks.4.attn.v_bias",
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  "layer_10_no_decay": {
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  "layer_10_decay": {
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  },
  "layer_11_no_decay": {
    "group_name": "layer_11_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.10.gamma_1",
      "blocks.10.gamma_2",
      "blocks.10.norm1.weight",
      "blocks.10.norm1.bias",
      "blocks.10.attn.q_bias",
      "blocks.10.attn.v_bias",
      "blocks.10.attn.proj.bias",
      "blocks.10.norm2.weight",
      "blocks.10.norm2.bias",
      "blocks.10.mlp.fc1.bias",
      "blocks.10.mlp.fc2.bias"
    ],
    "lr": 0.0005281250000000001,
    "lr_scale": 0.42250000000000004
  },
  "layer_11_decay": {
    "group_name": "layer_11_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.10.attn.relative_position_bias_table",
      "blocks.10.attn.qkv.weight",
      "blocks.10.attn.proj.weight",
      "blocks.10.mlp.fc1.weight",
      "blocks.10.mlp.fc2.weight"
    ],
    "lr": 0.0005281250000000001,
    "lr_scale": 0.42250000000000004
  },
  "layer_12_no_decay": {
    "group_name": "layer_12_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.11.gamma_1",
      "blocks.11.gamma_2",
      "blocks.11.norm1.weight",
      "blocks.11.norm1.bias",
      "blocks.11.attn.q_bias",
      "blocks.11.attn.v_bias",
      "blocks.11.attn.proj.bias",
      "blocks.11.norm2.weight",
      "blocks.11.norm2.bias",
      "blocks.11.mlp.fc1.bias",
      "blocks.11.mlp.fc2.bias"
    ],
    "lr": 0.0008125000000000001,
    "lr_scale": 0.65
  },
  "layer_12_decay": {
    "group_name": "layer_12_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.11.attn.relative_position_bias_table",
      "blocks.11.attn.qkv.weight",
      "blocks.11.attn.proj.weight",
      "blocks.11.mlp.fc1.weight",
      "blocks.11.mlp.fc2.weight"
    ],
    "lr": 0.0008125000000000001,
    "lr_scale": 0.65
  },
  "layer_13_no_decay": {
    "group_name": "layer_13_no_decay",
    "weight_decay": 0.0,
    "params": [
      "fc_norm.weight",
      "fc_norm.bias",
      "head.bias"
    ],
    "lr": 0.00125,
    "lr_scale": 1.0
  },
  "layer_13_decay": {
    "group_name": "layer_13_decay",
    "weight_decay": 0.05,
    "params": [
      "head.weight"
    ],
    "lr": 0.00125,
    "lr_scale": 1.0
  }
}
[2025-04-02 20:44:27 simmim_finetune] (optimizer.py 105): INFO AdamW (
Parameter Group 0
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_0_no_decay
    lr: 4.621507363773394e-06
    lr_scale: 0.003697205891018715
    maximize: False
    weight_decay: 0.0

Parameter Group 1
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_0_decay
    lr: 4.621507363773394e-06
    lr_scale: 0.003697205891018715
    maximize: False
    weight_decay: 0.05

Parameter Group 2
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_1_no_decay
    lr: 7.110011328882144e-06
    lr_scale: 0.005688009063105715
    maximize: False
    weight_decay: 0.0

Parameter Group 3
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_1_decay
    lr: 7.110011328882144e-06
    lr_scale: 0.005688009063105715
    maximize: False
    weight_decay: 0.05

Parameter Group 4
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_2_no_decay
    lr: 1.093847896751099e-05
    lr_scale: 0.008750783174008792
    maximize: False
    weight_decay: 0.0

Parameter Group 5
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_2_decay
    lr: 1.093847896751099e-05
    lr_scale: 0.008750783174008792
    maximize: False
    weight_decay: 0.05

Parameter Group 6
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_3_no_decay
    lr: 1.682842918078614e-05
    lr_scale: 0.013462743344628911
    maximize: False
    weight_decay: 0.0

Parameter Group 7
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_3_decay
    lr: 1.682842918078614e-05
    lr_scale: 0.013462743344628911
    maximize: False
    weight_decay: 0.05

Parameter Group 8
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_4_no_decay
    lr: 2.588989104736329e-05
    lr_scale: 0.02071191283789063
    maximize: False
    weight_decay: 0.0

Parameter Group 9
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_4_decay
    lr: 2.588989104736329e-05
    lr_scale: 0.02071191283789063
    maximize: False
    weight_decay: 0.05

Parameter Group 10
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_5_no_decay
    lr: 3.983060161132814e-05
    lr_scale: 0.03186448128906251
    maximize: False
    weight_decay: 0.0

Parameter Group 11
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_5_decay
    lr: 3.983060161132814e-05
    lr_scale: 0.03186448128906251
    maximize: False
    weight_decay: 0.05

Parameter Group 12
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_6_no_decay
    lr: 6.127784863281252e-05
    lr_scale: 0.049022278906250015
    maximize: False
    weight_decay: 0.0

Parameter Group 13
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_6_decay
    lr: 6.127784863281252e-05
    lr_scale: 0.049022278906250015
    maximize: False
    weight_decay: 0.05

Parameter Group 14
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_7_no_decay
    lr: 9.427361328125001e-05
    lr_scale: 0.07541889062500001
    maximize: False
    weight_decay: 0.0

Parameter Group 15
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_7_decay
    lr: 9.427361328125001e-05
    lr_scale: 0.07541889062500001
    maximize: False
    weight_decay: 0.05

Parameter Group 16
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_8_no_decay
    lr: 0.00014503632812500002
    lr_scale: 0.11602906250000002
    maximize: False
    weight_decay: 0.0

Parameter Group 17
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_8_decay
    lr: 0.00014503632812500002
    lr_scale: 0.11602906250000002
    maximize: False
    weight_decay: 0.05

Parameter Group 18
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_9_no_decay
    lr: 0.00022313281250000005
    lr_scale: 0.17850625000000003
    maximize: False
    weight_decay: 0.0

Parameter Group 19
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_9_decay
    lr: 0.00022313281250000005
    lr_scale: 0.17850625000000003
    maximize: False
    weight_decay: 0.05

Parameter Group 20
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_10_no_decay
    lr: 0.00034328125
    lr_scale: 0.274625
    maximize: False
    weight_decay: 0.0

Parameter Group 21
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_10_decay
    lr: 0.00034328125
    lr_scale: 0.274625
    maximize: False
    weight_decay: 0.05

Parameter Group 22
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_11_no_decay
    lr: 0.0005281250000000001
    lr_scale: 0.42250000000000004
    maximize: False
    weight_decay: 0.0

Parameter Group 23
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_11_decay
    lr: 0.0005281250000000001
    lr_scale: 0.42250000000000004
    maximize: False
    weight_decay: 0.05

Parameter Group 24
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_12_no_decay
    lr: 0.0008125000000000001
    lr_scale: 0.65
    maximize: False
    weight_decay: 0.0

Parameter Group 25
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_12_decay
    lr: 0.0008125000000000001
    lr_scale: 0.65
    maximize: False
    weight_decay: 0.05

Parameter Group 26
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_13_no_decay
    lr: 0.00125
    lr_scale: 1.0
    maximize: False
    weight_decay: 0.0

Parameter Group 27
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_13_decay
    lr: 0.00125
    lr_scale: 1.0
    maximize: False
    weight_decay: 0.05
)
[2025-04-02 20:44:27 simmim_finetune] (main_finetune.py 116): INFO number of params: 85763522
[2025-04-02 20:44:27 simmim_finetune] (utils.py 81): INFO All checkpoints founded in checkpoint/face: ['ckpt15.pth', 'ckpt10.pth', 'ckpt0.pth', 'ckpt25.pth', 'ckpt29.pth', 'ckpt20.pth', 'ckpt5.pth']
[2025-04-02 20:44:27 simmim_finetune] (utils.py 84): INFO The latest checkpoint founded: checkpoint/face/ckpt29.pth
[2025-04-02 20:44:27 simmim_finetune] (main_finetune.py 144): INFO auto resuming from checkpoint/face/ckpt29.pth
[2025-04-02 20:44:27 simmim_finetune] (utils.py 23): INFO >>>>>>>>>> Resuming from checkpoint/face/ckpt29.pth ..........
[2025-04-02 20:44:30 simmim_finetune] (utils.py 30): INFO <All keys matched successfully>
[2025-04-02 20:44:30 simmim_finetune] (utils.py 40): INFO => loaded successfully 'checkpoint/face/ckpt29.pth' (epoch 29)
[2025-04-02 20:44:43 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 12.666 (12.666)	Loss 1.1739 (1.1739)	Acc@1 49.219 (49.219)	Mem 2297MB
[2025-04-02 20:44:43 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 54.225
[2025-04-02 20:44:43 simmim_finetune] (main_finetune.py 151): INFO Accuracy of the network on the 142 test images: 54.2%
[2025-04-02 20:44:43 simmim_finetune] (main_finetune.py 161): INFO Start training
[2025-04-02 20:44:43 simmim_finetune] (main_finetune.py 177): INFO Training time 0:00:00
[2025-04-02 20:49:58 simmim_finetune] (main_finetune.py 375): INFO Full config saved to checkpoint/face/config.json
[2025-04-02 20:49:58 simmim_finetune] (main_finetune.py 378): INFO AMP_OPT_LEVEL: O0
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 1.0
  CUTMIX_MINMAX: null
  MIXUP: 0.8
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: ''
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  MASK_PATCH_SIZE: 32
  MASK_RATIO: 0.6
  NUM_WORKERS: 8
  PIN_MEMORY: true
  TRAIN_PATH: VBench-2.0_human_anomaly/dataset/hand_train.jsonl
  VAL_PATH: VBench-2.0_human_anomaly/dataset/hand_test.jsonl
EVAL_MODE: false
LOCAL_RANK: 0
LOSS:
  FOCAL: false
  FOCAL_ALPHA: 0.25
  FOCAL_GAMMA: 2.0
MODEL:
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NAME: simmim_finetune
  NUM_CLASSES: 2
  RESUME: ''
  SWIN:
    APE: false
    DEPTHS:
    - 2
    - 2
    - 6
    - 2
    EMBED_DIM: 96
    IN_CHANS: 3
    MLP_RATIO: 4.0
    NUM_HEADS:
    - 3
    - 6
    - 12
    - 24
    PATCH_NORM: true
    PATCH_SIZE: 4
    QKV_BIAS: true
    QK_SCALE: null
    WINDOW_SIZE: 7
  TYPE: vit
  VIT:
    DEPTH: 12
    EMBED_DIM: 768
    INIT_VALUES: 0.1
    IN_CHANS: 3
    MLP_RATIO: 4
    NUM_HEADS: 12
    PATCH_SIZE: 16
    QKV_BIAS: true
    USE_APE: false
    USE_MEAN_POOLING: true
    USE_RPB: true
    USE_SHARED_RPB: false
OUTPUT: checkpoint/face
PRETRAINED: pretrain/simmim_pretrain__vit_base__img224__800ep.pth
PRINT_FREQ: 2
SAVE_FREQ: 5
SEED: 0
TAG: simmim_finetune__vit_base__img224__800ep
TEST:
  CROP: true
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: true
  BASE_LR: 0.00125
  CLIP_GRAD: 5.0
  EPOCHS: 30
  LAYER_DECAY: 0.65
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    GAMMA: 0.1
    MULTISTEPS: []
    NAME: cosine
  MIN_LR: 2.5e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: adamw
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 3
  WARMUP_LR: 2.5e-07
  WEIGHT_DECAY: 0.05

[2025-04-02 20:49:58 simmim_finetune] (data_finetune.py 88): INFO Fine-tune data transform, is_train=True:
Compose(
    RandomResizedCropAndInterpolation(size=(224, 224), scale=(0.08, 1.0), ratio=(0.75, 1.3333), interpolation=PIL.Image.BICUBIC)
    RandomHorizontalFlip(p=0.5)
    <timm.data.auto_augment.RandAugment object at 0x7f2a5da5d0f0>
    ToTensor()
    Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250]))
    <timm.data.random_erasing.RandomErasing object at 0x7f2a5da5d120>
)
[2025-04-02 20:49:58 simmim_finetune] (data_finetune.py 88): INFO Fine-tune data transform, is_train=False:
Compose(
    Resize(size=256, interpolation=bicubic, max_size=None, antialias=True)
    CenterCrop(size=(224, 224))
    ToTensor()
    Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
)
[2025-04-02 20:49:58 simmim_finetune] (data_finetune.py 26): INFO Build dataset: train images = 39848, val images = 142
[2025-04-02 20:49:58 simmim_finetune] (main_finetune.py 102): INFO Creating model:vit/simmim_finetune
[2025-04-02 20:49:59 simmim_finetune] (main_finetune.py 105): INFO VisionTransformer(
  (patch_embed): PatchEmbed(
    (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
  )
  (pos_drop): Dropout(p=0.0, inplace=False)
  (blocks): ModuleList(
    (0): Block(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=False)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): Identity()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
    (1-11): 11 x Block(
      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (attn): Attention(
        (qkv): Linear(in_features=768, out_features=2304, bias=False)
        (attn_drop): Dropout(p=0.0, inplace=False)
        (proj): Linear(in_features=768, out_features=768, bias=True)
        (proj_drop): Dropout(p=0.0, inplace=False)
      )
      (drop_path): DropPath()
      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
      (mlp): Mlp(
        (fc1): Linear(in_features=768, out_features=3072, bias=True)
        (act): GELU(approximate='none')
        (fc2): Linear(in_features=3072, out_features=768, bias=True)
        (drop): Dropout(p=0.0, inplace=False)
      )
    )
  )
  (norm): Identity()
  (fc_norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
  (head): Linear(in_features=768, out_features=2, bias=True)
)
[2025-04-02 20:49:59 simmim_finetune] (optimizer.py 70): INFO >>>>>>>>>> Build Optimizer for Fine-tuning Stage
[2025-04-02 20:49:59 simmim_finetune] (optimizer.py 87): INFO No weight decay: {'cls_token', 'pos_embed'}
[2025-04-02 20:49:59 simmim_finetune] (optimizer.py 182): INFO Param groups = {
  "layer_0_no_decay": {
    "group_name": "layer_0_no_decay",
    "weight_decay": 0.0,
    "params": [
      "cls_token",
      "patch_embed.proj.bias"
    ],
    "lr": 4.621507363773394e-06,
    "lr_scale": 0.003697205891018715
  },
  "layer_0_decay": {
    "group_name": "layer_0_decay",
    "weight_decay": 0.05,
    "params": [
      "patch_embed.proj.weight"
    ],
    "lr": 4.621507363773394e-06,
    "lr_scale": 0.003697205891018715
  },
  "layer_1_no_decay": {
    "group_name": "layer_1_no_decay",
    "weight_decay": 0.0,
    "params": [
      "blocks.0.gamma_1",
      "blocks.0.gamma_2",
      "blocks.0.norm1.weight",
      "blocks.0.norm1.bias",
      "blocks.0.attn.q_bias",
      "blocks.0.attn.v_bias",
      "blocks.0.attn.proj.bias",
      "blocks.0.norm2.weight",
      "blocks.0.norm2.bias",
      "blocks.0.mlp.fc1.bias",
      "blocks.0.mlp.fc2.bias"
    ],
    "lr": 7.110011328882144e-06,
    "lr_scale": 0.005688009063105715
  },
  "layer_1_decay": {
    "group_name": "layer_1_decay",
    "weight_decay": 0.05,
    "params": [
      "blocks.0.attn.relative_position_bias_table",
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}
[2025-04-02 20:49:59 simmim_finetune] (optimizer.py 105): INFO AdamW (
Parameter Group 0
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_0_no_decay
    lr: 4.621507363773394e-06
    lr_scale: 0.003697205891018715
    maximize: False
    weight_decay: 0.0

Parameter Group 1
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_0_decay
    lr: 4.621507363773394e-06
    lr_scale: 0.003697205891018715
    maximize: False
    weight_decay: 0.05

Parameter Group 2
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_1_no_decay
    lr: 7.110011328882144e-06
    lr_scale: 0.005688009063105715
    maximize: False
    weight_decay: 0.0

Parameter Group 3
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_1_decay
    lr: 7.110011328882144e-06
    lr_scale: 0.005688009063105715
    maximize: False
    weight_decay: 0.05

Parameter Group 4
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_2_no_decay
    lr: 1.093847896751099e-05
    lr_scale: 0.008750783174008792
    maximize: False
    weight_decay: 0.0

Parameter Group 5
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_2_decay
    lr: 1.093847896751099e-05
    lr_scale: 0.008750783174008792
    maximize: False
    weight_decay: 0.05

Parameter Group 6
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_3_no_decay
    lr: 1.682842918078614e-05
    lr_scale: 0.013462743344628911
    maximize: False
    weight_decay: 0.0

Parameter Group 7
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_3_decay
    lr: 1.682842918078614e-05
    lr_scale: 0.013462743344628911
    maximize: False
    weight_decay: 0.05

Parameter Group 8
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_4_no_decay
    lr: 2.588989104736329e-05
    lr_scale: 0.02071191283789063
    maximize: False
    weight_decay: 0.0

Parameter Group 9
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_4_decay
    lr: 2.588989104736329e-05
    lr_scale: 0.02071191283789063
    maximize: False
    weight_decay: 0.05

Parameter Group 10
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_5_no_decay
    lr: 3.983060161132814e-05
    lr_scale: 0.03186448128906251
    maximize: False
    weight_decay: 0.0

Parameter Group 11
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_5_decay
    lr: 3.983060161132814e-05
    lr_scale: 0.03186448128906251
    maximize: False
    weight_decay: 0.05

Parameter Group 12
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_6_no_decay
    lr: 6.127784863281252e-05
    lr_scale: 0.049022278906250015
    maximize: False
    weight_decay: 0.0

Parameter Group 13
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_6_decay
    lr: 6.127784863281252e-05
    lr_scale: 0.049022278906250015
    maximize: False
    weight_decay: 0.05

Parameter Group 14
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_7_no_decay
    lr: 9.427361328125001e-05
    lr_scale: 0.07541889062500001
    maximize: False
    weight_decay: 0.0

Parameter Group 15
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_7_decay
    lr: 9.427361328125001e-05
    lr_scale: 0.07541889062500001
    maximize: False
    weight_decay: 0.05

Parameter Group 16
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_8_no_decay
    lr: 0.00014503632812500002
    lr_scale: 0.11602906250000002
    maximize: False
    weight_decay: 0.0

Parameter Group 17
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_8_decay
    lr: 0.00014503632812500002
    lr_scale: 0.11602906250000002
    maximize: False
    weight_decay: 0.05

Parameter Group 18
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_9_no_decay
    lr: 0.00022313281250000005
    lr_scale: 0.17850625000000003
    maximize: False
    weight_decay: 0.0

Parameter Group 19
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_9_decay
    lr: 0.00022313281250000005
    lr_scale: 0.17850625000000003
    maximize: False
    weight_decay: 0.05

Parameter Group 20
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_10_no_decay
    lr: 0.00034328125
    lr_scale: 0.274625
    maximize: False
    weight_decay: 0.0

Parameter Group 21
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_10_decay
    lr: 0.00034328125
    lr_scale: 0.274625
    maximize: False
    weight_decay: 0.05

Parameter Group 22
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_11_no_decay
    lr: 0.0005281250000000001
    lr_scale: 0.42250000000000004
    maximize: False
    weight_decay: 0.0

Parameter Group 23
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_11_decay
    lr: 0.0005281250000000001
    lr_scale: 0.42250000000000004
    maximize: False
    weight_decay: 0.05

Parameter Group 24
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_12_no_decay
    lr: 0.0008125000000000001
    lr_scale: 0.65
    maximize: False
    weight_decay: 0.0

Parameter Group 25
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_12_decay
    lr: 0.0008125000000000001
    lr_scale: 0.65
    maximize: False
    weight_decay: 0.05

Parameter Group 26
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_13_no_decay
    lr: 0.00125
    lr_scale: 1.0
    maximize: False
    weight_decay: 0.0

Parameter Group 27
    amsgrad: False
    betas: (0.9, 0.999)
    capturable: False
    differentiable: False
    eps: 1e-08
    foreach: None
    fused: None
    group_name: layer_13_decay
    lr: 0.00125
    lr_scale: 1.0
    maximize: False
    weight_decay: 0.05
)
[2025-04-02 20:49:59 simmim_finetune] (main_finetune.py 116): INFO number of params: 85763522
[2025-04-02 20:49:59 simmim_finetune] (utils.py 81): INFO All checkpoints founded in checkpoint/face: ['ckpt15.pth', 'ckpt10.pth', 'ckpt0.pth', 'ckpt25.pth', 'ckpt29.pth', 'ckpt20.pth', 'ckpt5.pth']
[2025-04-02 20:49:59 simmim_finetune] (utils.py 84): INFO The latest checkpoint founded: checkpoint/face/ckpt29.pth
[2025-04-02 20:49:59 simmim_finetune] (main_finetune.py 144): INFO auto resuming from checkpoint/face/ckpt29.pth
[2025-04-02 20:49:59 simmim_finetune] (utils.py 23): INFO >>>>>>>>>> Resuming from checkpoint/face/ckpt29.pth ..........
[2025-04-02 20:50:00 simmim_finetune] (utils.py 30): INFO <All keys matched successfully>
[2025-04-02 20:50:00 simmim_finetune] (utils.py 40): INFO => loaded successfully 'checkpoint/face/ckpt29.pth' (epoch 29)
[2025-04-02 20:50:08 simmim_finetune] (main_finetune.py 297): INFO Test: [0/2]	Time 7.754 (7.754)	Loss 1.1739 (1.1739)	Acc@1 49.219 (49.219)	Mem 2297MB
[2025-04-02 20:50:08 simmim_finetune] (main_finetune.py 304): INFO  * Acc@1 54.225
[2025-04-02 20:50:08 simmim_finetune] (main_finetune.py 151): INFO Accuracy of the network on the 142 test images: 54.2%
[2025-04-02 20:50:08 simmim_finetune] (main_finetune.py 161): INFO Start training
[2025-04-02 20:50:08 simmim_finetune] (main_finetune.py 177): INFO Training time 0:00:00
