cfg_dev.yml

maintainer: Mohamed Amine Dhiab

device: cpu #device on which you want to train the data, use cpu if no nvidia cuda driver is available.

run_name: dev_densenet_3

job_type: dev

img_dir: ./data/raw/image # directory to image data

mask_dir: ./data/raw/image_mask # directory to image masks

label_dir: ./data/raw/labels # directory to image labels

pipeline:
    - name: process # process pipe used to prepare the data
      train_fraction: 0.9 # proportion of the data to be used for training
      validation_fraction: 0.1 # proportion of the data to be used for validation
      preprocess : 
        crop : # crop your data to create square images using the result, you need to know the minimum height / width
          type : all # cut the whole image into smaller cropped images of size (height x width)
          height : 430
          width : 430
      is_augment: true # augmentation will be executed if this is true
    - name: train # train pipe used to training the model
      train_cfg:
          with_augmentation: true # if set to true, data in the '.../augmented' folder will be used for training
          img_size: (384,384)
          epochs: 10
          batch_size: 8
          learning_rate: 0.0003
          lr_decay: 0.1
          lamda: 1
          optimizer: adam
          attention_threshold: 0.1
          save_path: ./weights/model_densenet_1.pth
    # - name: test # test pipe used to run inference
    #   test_data: ./data/test.tsv
    #   test_cfg:
    #       img_size: (512,512)
    #       batch_size: 2
    #       weights: ./weights/model_densenet_1.pth

with_wandb: false

multi_gpus: false # not supported yet

model_cfg: densenet.yml

random_seed: 2022