dfine_hgnetv2_n_coco.yml

__include__: [
  '../dataset/coco_detection.yml',
  '../runtime.yml',
  './include/dataloader.yml',
  './include/optimizer.yml',
  './include/dfine_hgnetv2.yml',
]

output_dir: ./output/dfine_hgnetv2_n_coco


DFINE:
  backbone: HGNetv2

HGNetv2:
  name: 'B0'
  return_idx: [2, 3]
  freeze_at: -1
  freeze_norm: False
  use_lab: True


HybridEncoder:
  in_channels: [512, 1024]
  feat_strides: [16, 32]

  # intra
  hidden_dim: 128
  use_encoder_idx: [1]
  dim_feedforward: 512

  # cross
  expansion: 0.34
  depth_mult: 0.5


DFINETransformer:
  feat_channels: [128, 128]
  feat_strides: [16, 32]
  hidden_dim: 128
  dim_feedforward: 512
  num_levels: 2

  num_layers: 3
  eval_idx: -1

  num_points: [6, 6]

optimizer:
  type: AdamW
  params:
    -
      params: '^(?=.*backbone)(?!.*norm|bn).*$'
      lr: 0.0004
    -
      params: '^(?=.*backbone)(?=.*norm|bn).*$'
      lr: 0.0004
      weight_decay: 0.
    -
      params: '^(?=.*(?:encoder|decoder))(?=.*(?:norm|bn|bias)).*$'
      weight_decay: 0.

  lr: 0.0008
  betas: [0.9, 0.999]
  weight_decay: 0.0001


# Increase to search for the optimal ema
epoches: 160 # 148 + 4n
train_dataloader:
  total_batch_size: 128
  dataset:
    transforms:
      policy:
        epoch: 148
  collate_fn:
    stop_epoch: 148
    ema_restart_decay: 0.9999
    base_size_repeat: ~

val_dataloader:
  total_batch_size: 256