training.yaml

imgaug: # write the desired values, and append the probability, e.g. [val1, val2, probability] or null if that augmentation won't be used.
  num_aug: 3 # number of augmentations that is randomly chosen from list at each iteration.
  fliplr: 0.5 # 0.5
  scale: [0.95, 1.05] # [0.95, 1.05]
  brightness: [-10, 10] # [-10, 10]
  saturation: [-10, 10] # [-10, 10] 
  hue: [-10, 10] # [-10, 10]
  add_grayscale: [0, 0.2] # [0, 0.2]
  motion_blur: [3, 5] # [3, 5]
  contrast: [0.8, 1.2] # [0.8, 1.2]
  translate: [[-0.1, 0.1], [-0.1, 0.1]] # [[-0.1, 0.1], [-0.1, 0.1]]
  rotate: [-5, 5] # [-5, 5]
  shear: [-5, 5] # [-5, 5]

copy_paste: # write the desired values or null if that augmentation won't be used.
  bboxes_memory: 20
  pasted_bbox_number: 4
  augment_box: True
  box_augments:
    fliplr: 1.0 # 0.5
    brightness: [-10, 10] # [-10, 10]
    saturation: null #[-10, 10] # [-10, 10]
    add_grayscale: [0, 0.2] # [0, 0.2]
    hue: null #[-10, 10] # [-10, 10]
    contrast: null #[0.8, 1.2] # [0.8, 1.2]
    motion_blur: null # [3, 5] # [3, 5]
    threshold: 0.5 # if the intersextion of area of labels and pasted bboxes is exceeds the threshold, that bbox is simply dropped out.

cutout:
  fill_type: 1 # the index of ['gaussian_noise', 'random_color', 'white', 'black', 'gray']
  percentages: [0.15, 0.2, 0.25] # number of percentage is the number of cutout boxes, the value of percentage is the percentage of cutout boxes wrt the image size
  threshold: 0.5 # if the intersextion of area of labels and cutout bboxes is exceeds the threshold, that bbox is simply dropped out.

training:
  image_path: '/workspaces/object-detection/datasets/coco128/images/train2017'
  annotation_path: '/workspaces/object-detection/datasets/coco128_train.json'
  image_size: 480
  normalize: True
  augmentations: ['imgaug', 'cutout']
  batch_size: 1
  shuffle: False
  num_workers: 0
  drop_last: True
  optimizer: 'adam' # 'adam', 'adamw', 'sgd' or 'asgd'
  lr_scheduler: 'cosine' # 'cosine', 'multistep_lr', 'cosine_annealing'
  learning_rate: 0.001
  val_frequency: 1
  epochs: 500

validation:
  image_path: '/workspaces/object-detection/datasets/coco128/images/train2017'
  annotation_path: '/workspaces/object-detection/datasets/coco128_train.json'
  batch_size: 1
  shuffle: False
  num_workers: 0
  drop_last: True