binary_cb55_sauvola_cleaned_dataset1_unet.yaml

# @package _global_

# to execute this experiment run:
# python run.py +experiment=exp_example_full

defaults:
    - /mode: experiment.yaml
    - /plugins: null
    - /task: semantic_segmentation_RGB.yaml
    - /loss: crossentropyloss.yaml
    - /metric:
          - iou.yaml
          - precision.yaml
          - recall.yaml
          - f1_score.yaml
    - /model/backbone: unet.yaml
    - /model/header: unet_segmentation.yaml
    - /optimizer: adam.yaml
    - /callbacks:
          - check_compatibility.yaml
          - model_checkpoint.yaml
          - watch_model_wandb.yaml
    - /logger:
          - wandb.yaml # set logger here or use command line (e.g. `python run.py logger=wandb`)
          - csv.yaml
    - _self_

# we override default configurations with nulls to prevent them from loading at all
# instead we define all modules and their paths directly in this config,
# so everything is stored in one place for more readibility

name: "binary_cb55_sauvola_cleaned_unet_loss_no_weights_50epoch"

train: True
test: False
predict: False

trainer:
    _target_: pytorch_lightning.Trainer
    accelerator: 'gpu'
    devices: -1
    strategy: 'ddp_find_unused_parameters_false'
    min_epochs: 1
    max_epochs: 50
    precision: 16
    check_val_every_n_epoch: 1

task:
    confusion_matrix_log_every_n_epoch: 10
    confusion_matrix_val: True
    confusion_matrix_test: True

datamodule:
    _target_: src.datamodules.RGB.datamodule.DataModuleRGB

    data_dir: /net/research-hisdoc/datasets/self-supervised/CB55/binary_cleaned/sauvola/dataset_1
    num_workers: 4
    batch_size: 1
    shuffle: True
    drop_last: True
    data_folder_name: data
    gt_folder_name: gt
    train_folder_name: train
    val_folder_name: val

optimizer:
    lr: 1e-3
    betas: [0.9, 0.999]
    eps: 1e-5

model:
    header:
        features: 64

callbacks:
    model_checkpoint:
        monitor: "val/crossentropyloss"
        mode: "min"
        filename: ${checkpoint_folder_name}cb55-binary-unet
#    watch_model
#        log_freq: 1000

logger:
    wandb:
        project: icdar
        name: ${name}
        tags: ["best_model", "binary", "sauvola_cleaned" , "pre-training", "unet", "dasaset1", "CB55-ssl-set", "100-epochs"]
        group: 'pre-training-50ep'