unet_train#
Training parameters including batch size, epochs, learning rate, weight decay, loss function (dice or bce), number of workers, whether to use the GPU
batch_size: 1
epochs: 1
learning_rate: 0.01
weight_decay: 0.0001
loss_fn: bce
num_workers: 1
use_gpu: true
Threshold/interpolation to apply to image For interpolation options are: log2, log10 or linear
img_interpolate: log2
img_threshold: 0
Transformations to apply during training/test
train_transforms:
dtypeconv: null
erasing: null
h_flip: null
perspective: 0.2
rotation: 90
v_flip: null
test_transforms:
dtypeconv: null
Weights and bias parameters
wandb_dataset: c15_imgs
wandb_project: c15_img_unet_bce
The following is not generic, if you need to use this please raise an issue and tag @oubino
For two channel image, we visualise both channels then sum them together Need the name of the first and second channel in terms of the real concepts
channels:
- egfr
- ereg
Whether to sum the two channels
sum_chan: true