DEIMv2/configs/deimv2/deimv2_hgnetv2_n_coco.yml at main · NikhilSandy/DEIMv2 · GitHub

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__include__: [
  '../dataset/coco_detection.yml',
  '../runtime.yml',
  '../base/dataloader.yml',
  '../base/optimizer.yml',
  '../base/deimv2.yml'
]

output_dir: ./outputs/deimv2_hgnetv2_n_coco

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

  version: 'dfine'

DEIMTransformer:
  feat_channels: [128, 128]
  feat_strides: [16, 32]
  hidden_dim: 128
  num_levels: 2
  num_points: [6, 6]

  num_layers: 3
  eval_idx: -1

  # FFN
  dim_feedforward: 512

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 + 12

## Our LR-Scheduler
flat_epoch: 7800    # 4 + epoch // 2, e.g., 40 = 4 + 72 / 2
no_aug_epoch: 12
lr_gamma: 1.0

## Our DataAug
train_dataloader:
  dataset:
    transforms:
      policy:
        epoch: [4, 78, 148]   # list

  collate_fn:
    ema_restart_decay: 0.9999
    base_size_repeat: ~
    mixup_epochs: [4, 78]
    stop_epoch: 148
    copyblend_prob: 0.4
    copyblend_epochs: [4, 78]   # CP half
    area_threshold: 100
    num_objects: 3
    with_expand: True
    expand_ratios: [0.1, 0.25]

DEIMCriterion:
  matcher:
    # new matcher
    change_matcher: True
    iou_order_alpha: 4.0
    matcher_change_epoch: 136