beit_large_p16_384.yaml

epochs: 300
output_dir: output_dir

model:
    name: BeitWrapper
    architecture:
        name: Beit
        img_size: 384
        embed_dim: 1024
        patch_size: 16
        depth: 24
        num_heads: 16
        mlp_ratio: 4
        use_abs_pos_emb: False
        use_rel_pos_bias: True
        init_values: 0.00001
    head:
        name: BeitClsHead
        num_classes: 1000
        in_channels: 1024

dataloader:
  train:
    num_workers: 8
    sampler:
      batch_size: 128
      shuffle: true
      drop_last: True
    dataset:
      name: ImageNet
      dataroot: data/ILSVRC2012/train/
      return_label: True
      transforms:
        - name: ToRGB
        - name: RandomResizedCrop
          size: 224
          scale: [0.75, 1.]
          ratio: [1., 1.]
          interpolation: 'bicubic'
        - name: Transpose
        - name: NormalizeImage
          scale: 1.0/255.0
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]

lr_scheduler:
  name: CosineWarmup
  learning_rate: 0.003
  T_max: 93835
  warmup_steps: 10000
  start_lr: 0.00003
  end_lr: 0.003

optimizer:
  name: AdamW
  beta1: 0.9
  beta2: 0.999
  weight_decay: 0.3
  grad_clip:
    name: global_norm
    value: 1.0


log_config:
    name: LogHook
    interval: 10

custom_config:
  - name: EvaluateHook