config.yaml

CUDA_VISIBLE_DEVICES: 3
logging:
  log_dir: /data5/ziweicui/cellvit256-unireplknet-n
  mode: online
  project: Cell-Segmentation
  notes: CellViT-256
  log_comment: CellViT-256-resnet50-tiny
  tags:
  - Fold-1
  - ViT256
  wandb_dir: /data5/ziweicui/UniRepLKNet-optimizerconfig-unetdecoder-inputconv/results
  level: Debug
  group: CellViT256
  run_id: anifw9ux
  wandb_file: anifw9ux
random_seed: 19
gpu: 0
data:
  dataset: PanNuke
  dataset_path: /data5/ziweicui/cellvit-png
  train_folds:
  - 0
  val_folds:
  - 1
  test_folds:
  - 2
  num_nuclei_classes: 6
  num_tissue_classes: 19
model:
  backbone: default
  pretrained_encoder: /data5/ziweicui/semi_supervised_resnet50-08389792.pth
  shared_skip_connections: true
loss:
  nuclei_binary_map:
    focaltverskyloss:
      loss_fn: FocalTverskyLoss
      weight: 1
    dice:
      loss_fn: dice_loss
      weight: 1
  hv_map:
    mse:
      loss_fn: mse_loss_maps
      weight: 2.5
    msge:
      loss_fn: msge_loss_maps
      weight: 8
  nuclei_type_map:
    bce:
      loss_fn: xentropy_loss
      weight: 0.5
    dice:
      loss_fn: dice_loss
      weight: 0.2
    mcfocaltverskyloss:
      loss_fn: MCFocalTverskyLoss
      weight: 0.5
      args:
        num_classes: 6
  tissue_types:
    ce:
      loss_fn: CrossEntropyLoss
      weight: 0.1
training:
  drop_rate: 0
  attn_drop_rate: 0.1
  drop_path_rate: 0.1
  batch_size: 32
  epochs: 130
  optimizer: AdamW
  early_stopping_patience: 130
  scheduler:
    scheduler_type: cosine
    hyperparameters:
      #gamma: 0.85
      eta_min: 1e-5
  optimizer_hyperparameter:
    # betas:
    # - 0.85
    # - 0.95
    #lr: 0.004
    opt_lower: 'AdamW'
    lr: 0.0008
    opt_betas: [0.85,0.95]
    weight_decay: 0.05
    opt_eps: 0.00000008
  unfreeze_epoch: 25
  sampling_gamma: 0.85
  sampling_strategy: cell+tissue
  mixed_precision: true
transformations:
  randomrotate90:
    p: 0.5
  horizontalflip:
    p: 0.5
  verticalflip:
    p: 0.5
  downscale:
    p: 0.15
    scale: 0.5
  blur:
    p: 0.2
    blur_limit: 10
  gaussnoise:
    p: 0.25
    var_limit: 50
  colorjitter:
    p: 0.2
    scale_setting: 0.25
    scale_color: 0.1
  superpixels:
    p: 0.1
  zoomblur:
    p: 0.1
  randomsizedcrop:
    p: 0.1
  elastictransform:
    p: 0.2
  normalize:
    mean:
    - 0.5
    - 0.5
    - 0.5
    std:
    - 0.5
    - 0.5
    - 0.5
eval_checkpoint: latest_checkpoint.pth
dataset_config:
  tissue_types:
    Adrenal_gland: 0
    Bile-duct: 1
    Bladder: 2
    Breast: 3
    Cervix: 4
    Colon: 5
    Esophagus: 6
    HeadNeck: 7
    Kidney: 8
    Liver: 9
    Lung: 10
    Ovarian: 11
    Pancreatic: 12
    Prostate: 13
    Skin: 14
    Stomach: 15
    Testis: 16
    Thyroid: 17
    Uterus: 18
  nuclei_types:
    Background: 0
    Neoplastic: 1
    Inflammatory: 2
    Connective: 3
    Dead: 4
    Epithelial: 5
run_sweep: false
agent: null