NOTICE: WEIGHTS LICENSING

No pretrained weight files are distributed in this directory or anywhere
else in the source tree. The conversion scripts in this directory are
part of the LibreYOLO source repository and are covered by the root
MIT License.

Pretrained LibreYOLO weights are published separately on Hugging Face
under the LibreYOLO organization:

    https://huggingface.co/LibreYOLO

Each Hugging Face model repository ships its own LICENSE, NOTICE (where
applicable), and attribution reflecting the license of the upstream
project the weights were derived from. Per-family summary:

    YOLOX    (LibreYOLOX{n,t,s,m,l,x})                 Apache-2.0
             upstream: Megvii-BaseDetection/YOLOX

    YOLO9    (LibreYOLO9{t,s,m,c})                     MIT
             upstream: MultimediaTechLab/YOLO

    YOLOv1   (LibreYOLO1{t,b})                         public domain
    YOLOv2   (LibreYOLO2{t,b})                         public domain
    YOLOv3   (LibreYOLO3{t,b,spp})                     public domain
    YOLOv4   (LibreYOLO4{t,b})                         public domain
             upstream: pjreddie/darknet (v1/v2/v3), AlexeyAB/darknet (v4)
             Darknet is public domain ("YOLO LICENSE"); both the .cfg
             architectures and the pretrained .weights carry no license
             obligations. Convert with weights/convert_darknet_weights.py.
             YOLOv1 is Pascal VOC (20 classes), fixed 448. Its pretrained
             yolov1.weights was pulled from pjreddie.com/media/files/ (now
             archived only on the Wayback Machine); the tiny-yolov1 .weights
             was overwritten upstream by the YOLOv2 release and is not
             recoverable, so LibreYOLO1t ships as code only (bring your own
             tiny-yolov1.weights to convert).

    YOLOv7   (LibreYOLO7b)                             MIT
             upstream: MultimediaTechLab/YOLO (Kin-Yiu Wong & Hao-Tang Tsui)
             The authors' MIT re-release, not WongKinYiu/yolov7 (GPL-3.0).
             Convert v7.pt with weights/convert_yolo7_weights.py.

    RF-DETR  (LibreRFDETR{n,s,m,l})                    Apache-2.0
             upstream: roboflow/rf-detr
             (backbone: facebookresearch/dinov2, Apache-2.0)

    RT-DETR  (LibreRTDETR{r18,r34,r50,r50m,r101})      Apache-2.0
             upstream: lyuwenyu/RT-DETR
             (backbone: PaddlePaddle/PaddleClas ResNet_vd, Apache-2.0)

    EoMT     (LibreEoMTl-sem, LibreEoMTl-seg,          MIT
              LibreEoMTl-seg-1280, LibreEoMT{s,b,l}-panoptic)
             upstream: tue-mps/eomt
             (backbone: facebookresearch/dinov2, Apache-2.0)
             Panoptic checkpoints load as first-class task="panoptic"
             (133 COCO classes: 80 things + 53 stuff).

    MobileNetV4 (LibreMobileNetV4{s,m,l}-cls)          Apache-2.0
             upstream: huggingface/pytorch-image-models (timm), ImageNet-1k

    ConvNeXt (LibreConvNeXt{t,s,b}-cls)                Apache-2.0 (weights)
             upstream: timm fb_in1k; arch facebookresearch/ConvNeXt (MIT)
             (ConvNeXt-V2 fcmae weights are CC-BY-NC and are NOT used)

    EfficientNetV2 (LibreEfficientNetV2{b0,b1,b2,b3}-cls)  Apache-2.0
             upstream: timm (.in1k); arch google/automl

    ResNet   (LibreResNet{18,34,50,101}-cls)           Apache-2.0
             upstream: timm (a1_in1k "ResNet Strikes Back" recipe), ImageNet-1k

    CLIP     (LibreCLIP{b32,b16}-cls)                  MIT (weights)
             upstream: mlfoundations/open_clip LAION-2B checkpoints;
             see libreyolo/models/clip/NOTICE.md for the LAION
             data-provenance note.

    SigLIP2  (LibreSigLIP2{b16,so400m}-cls)            Apache-2.0
             upstream: google/siglip2-base-patch16-256 and
             google/siglip2-so400m-patch14-384 (Hugging Face).
             Convert with weights/convert_siglip2_weights.py.

    OMDet-Turbo (LibreOMDetTurbot)                     Apache-2.0
             upstream: omlab/omdet-turbo-swin-tiny-hf
             pinned revision:
               7fe93cecfb770c4d76cf71163956221249cab566
             LibreYOLO mirror revision:
               d569ff1da5668156f07b5859246be435804fed2d
             Model, configuration, processor, and tokenizer assets are
             byte-identical to the pinned upstream snapshot; no learned
             parameters were changed. model.safetensors SHA-256:
               439d1575d7e237ad565ed6969ea2a2dfcedf2086155e9ca3ac96cd6180a48cfd

    OV-DEIM  (LibreOVDEIM{s,m,l})                      CC BY-NC 4.0
             upstream: wleilei/OV-DEIM; converted and rehosted with
             attribution as the upstream MODEL_LICENSE permits;
             licensing confirmed by the author (wleilei/OV-DEIM#4).
             The text tower is apple/MobileCLIP-B-LT-OpenCLIP with
             tensors unmodified; the Apple license text ships in the
             weight repositories.
             Convert with weights/convert_ovdeim_weights.py.

    Depth Anything 3 (LibreDepthAnything3l-depth)      Apache-2.0
             upstream: depth-anything/DA3MONO-LARGE
             pinned revision: f465978e618db8cc79c83b8bbf24964857db1875
             Tensors are unchanged; conversion strips the outer model.
             prefix and adds LibreYOLO checkpoint metadata. Metric and
             any-view checkpoints are not hosted. CC-BY-NC-4.0
             Large/Giant/Nested checkpoints are explicitly excluded.

    NAFNet   (LibreNAFNet{s,l}-restore)                no bundled weights
             upstream code: megvii-research/NAFNet (MIT)
             Some published GoPro-trained checkpoint files do not carry an
             explicit standalone weights license. Convert only weights that
             you have the right to use and redistribute. The SIDD denoising
             weights are trained on SIDD (MIT-licensed dataset).

    RealESRGAN (LibreRealESRGAN{x4,x2,x4t}-restore)    no bundled weights
             upstream code: xinntao/Real-ESRGAN (BSD-3-Clause),
             architecture lineage XPixelGroup/BasicSR (Apache-2.0)
             Released generator weights (RealESRGAN_x4plus, RealESRGAN_x2plus,
             realesr-general-x4v3) are BSD-3-Clause; mirrored as converted
             checkpoints with provenance in the model cards.

    SwinIR  (LibreSwinIR{s,m,l}-restore)               Apache-2.0
             upstream: JingyunLiang/SwinIR
             Official SwinIR-S x4 lightweight and SwinIR-M/L x4 real-world
             checkpoints. Convert with weights/convert_swinir_weights.py.

    BiRefNet (LibreBiRefNet{t,l}-matte)                see per-size note
             upstream: ZhengPeng7/BiRefNet (code MIT)
             l (general): Hugging Face weights tagged MIT -> rehosted under
               the LibreYOLO org (LibreYOLO/LibreBiRefNetl-matte).
             t (lite): the BiRefNet_lite HF repo shows an MIT badge in its
               model card but has no explicit license metadata (no YAML
               license field, no LICENSE file). LibreYOLO does NOT rehost the
               lite weights pending an explicit license confirmation; the
               converter and a ready-to-run upload script are provided so the
               maintainer can host them once the tag is confirmed. Users may
               still convert BiRefNet_lite locally at their own discretion.

    SegFormer (LibreSegformer{b0,b1,b2,b3,b4,b5}-sem)  NON-COMMERCIAL weights
             upstream code: huggingface/transformers modeling_segformer.py
             (Apache-2.0); weights: NVIDIA's ADE20K checkpoints
             (nvidia/segformer-b0..b5-finetuned-ade-*, b5 = 640-640) under
             the NVIDIA Source Code License. That license allows
             redistribution (license copy + attribution must travel with the
             weights) but restricts USE to research or evaluation only, and
             the restriction is viral through derivative works. Hosted with a
             loud non-commercial notice printed before auto-download, like the
             VisDrone research-preview weights. NOT covered by LibreYOLO's
             permissive license. Train from scratch for unrestricted use.

    PP-OCRv5 (LibrePPOCR{t,l}-ocr)                     Apache-2.0
             upstream: PaddlePaddle/PaddleOCR (code and released
             weights Apache-2.0). Converted from the official
             PP-OCRv5_{mobile,server}_{det,rec} training checkpoints;
             exact source files and SHA-256 hashes are recorded on the
             Hugging Face model cards and in
             weights/convert_ppocr_weights.py. The PP-OCRv5 recognition
             dictionary ships inside each checkpoint as charset
             metadata (Apache-2.0, attribution in
             libreyolo/models/ppocr/NOTICE).

    SAM 3    (facebook/sam3 snapshot; not rehosted)   custom SAM License
             upstream: facebook/sam3 on Hugging Face. Access is gated and
             requires accepting Meta's terms. LibreYOLO downloads the weights
             directly and does not distribute them under the MIT license.

    EdgeTAM (LibreYOLO/LibreEdgeTAM)                   Apache-2.0
             Converted from facebook/EdgeTAM revision
             14d7ecc48c656b94e5184519f698cd5386c5a2bf (edgetam.pt
             SHA-256 ed2d4850b8792c239689b043c47046ec239b6e808a3d9b6ae676c803fd8780df).
             The official EdgeTAM repository explicitly licenses both code
             and checkpoints under Apache-2.0. Conversion uses the pinned
             Apache-2.0 Transformers mapping at commit
             bd37c453544e83eb875ed3608980a1660376007a and is verified against
             yonigozlan/EdgeTAM-hf revision
             c266ce53b3fc00f0f495b583f6a116c4e57f53bb.
             Hash-pinned configuration and processor files are copied from
             that Apache-2.0-declared reference; learned tensors are converted
             independently from the official checkpoint and checked exactly.

    PicoSAM3 (LibreYOLO/LibrePicoSAM3)                 Apache-2.0
             Converted and rehosted by LibreYOLO. Upstream sources:
             upstream code commit:
               1b03949e43472953bb0021685c7fc3f5fdf48fde
             upstream weights (pietrobonazzi/picosam3) revision:
               af49e4322b6b7cf448499fee5c073d4576f59444
             Only PicoSAM3_SAM3_student_best.pt matches the advertised
             PicoSAM3 architecture. The epoch-1 files are not mirrored or
             converted because they contain the older PicoSAM2 network.
             Distilled by upstream from SAM 2.1 and SAM 3; teacher weights
             are not included or redistributed.

The LICENSE and NOTICE files on each Hugging Face model card are the
authoritative source for redistribution terms. Users converting
third-party checkpoints locally are responsible for complying with the
upstream license of those checkpoints.

Third-party SOURCE CODE bundled inside the LibreYOLO Python package
(separate from weights) is disclosed in the top-level NOTICE file.
Notably, the DEIMv2 family bundles DINOv3 backbone code under Meta's
non-OSI DINOv3 License Agreement; see NOTICE and
libreyolo/models/deimv2/engine/backbone/dinov3/LICENSE.md.
