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fold_3.yaml
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fold_3.yaml
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logging:
mode: online
project: # Adapt
notes: CellViT-SAM-H-SOTA
log_comment: CellViT-SAM-H-SOTA-Fold-3
tags:
- "Fold-3"
- "SOTA"
- "SAM-H"
wandb_dir: ./results
log_dir: ./results/PanNuke/SAM/SAM-H
level: Debug
group: CellViT-SAM-H-SOTA
random_seed: 19
gpu: 0
data:
dataset: PanNuke
dataset_path: /data/pwojcik/SimMIM/pannuke
train_folds:
- 2
- 3
val_folds:
- 1
num_nuclei_classes: 6
num_tissue_classes: 19
model:
backbone: SAM-H
pretrained_encoder: ./models/pretrained/SAM/sam_vit_h.pth # Adapt to your path
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: 16
epochs: 130
optimizer: AdamW
early_stopping_patience: 130
scheduler:
scheduler_type: exponential
hyperparameters:
gamma: 0.85
optimizer_hyperparameter:
betas:
- 0.85
- 0.95
lr: 0.0003
weight_decay: 0.0001
unfreeze_epoch: 25
sampling_gamma: 0.85
sampling_strategy: cell+tissue
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"