UNI training code

[saghiralfasly]

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Thank you for making UNI publically available.

Can you make the exact complete training code used for training UNI publicly available?

[Richarizardd]

Hi @saghiralfasly - As the training code for UNI is developed using DINOv2 (available at: https://github.com/facebookresearch/dinov2), we did not feel it was necessary to rehost the DINOv2 codebase in this repository. The training configuration used for UNI (which adapts the VIT-L/16 short run that trains for 125K iterations) is also found in Supplementary Data Table 5. Was there a specific implementation that was described unclear?

[saghiralfasly]

Thank you, @Richarizardd! It's good to know that you adapted VIT-L/16 short. If you still keep the auto-generated config.yaml file in the final UNI output directory that is similar to this, it would be appreciated if you could provide it.

  WEIGHTS: ''
compute_precision:
  grad_scaler: true
  teacher:
    backbone:
      sharding_strategy: SHARD_GRAD_OP
      mixed_precision:
        param_dtype: fp16
        reduce_dtype: fp16
        buffer_dtype: fp32
    dino_head:
      sharding_strategy: SHARD_GRAD_OP
      mixed_precision:
        param_dtype: fp16
        reduce_dtype: fp16
        buffer_dtype: fp32
    ibot_head:
      sharding_strategy: SHARD_GRAD_OP
      mixed_precision:
        param_dtype: fp16
        reduce_dtype: fp16
        buffer_dtype: fp32
  student:
    backbone:
      sharding_strategy: SHARD_GRAD_OP
      mixed_precision:
        param_dtype: fp16
        reduce_dtype: fp16
        buffer_dtype: fp32
    dino_head:
      sharding_strategy: SHARD_GRAD_OP
      mixed_precision:
        param_dtype: fp16
        reduce_dtype: fp32
        buffer_dtype: fp32
    ibot_head:
      sharding_strategy: SHARD_GRAD_OP
      mixed_precision:
        param_dtype: fp16
        reduce_dtype: fp32
        buffer_dtype: fp32
dino:
  loss_weight: 1.0
  head_n_prototypes: 65536
  head_bottleneck_dim: 256
  head_nlayers: 3
  head_hidden_dim: 2048
  koleo_loss_weight: 0.1
ibot:
  loss_weight: 1.0
  mask_sample_probability: 0.5
  mask_ratio_min_max:
  - 0.1
  - 0.5
  separate_head: false
  head_n_prototypes: 65536
  head_bottleneck_dim: 256
  head_nlayers: 3
  head_hidden_dim: 2048
train:
  batch_size_per_gpu: 64
  dataset_path: HistoDataset
  output_dir: /home/path/to/output/vitl16
  saveckp_freq: 20
  seed: 0
  num_workers: 10
  OFFICIAL_EPOCH_LENGTH: 1250
  cache_dataset: true
  centering: centering
student:
  arch: vit_large
  patch_size: 16
  drop_path_rate: 0.3
  layerscale: 1.0e-05
  drop_path_uniform: true
  pretrained_weights: ''
  ffn_layer: mlp
  block_chunks: 4
  qkv_bias: true
  proj_bias: true
  ffn_bias: true
  num_register_tokens: 0
  interpolate_antialias: false
  interpolate_offset: 0.1
teacher:
  momentum_teacher: 0.992
  final_momentum_teacher: 1
  warmup_teacher_temp: 0.04
  teacher_temp: 0.07
  warmup_teacher_temp_epochs: 30
optim:
  epochs: 100
  weight_decay: 0.04
  weight_decay_end: 0.4
  base_lr: 0.004
  lr: 0.001
  warmup_epochs: 10
  min_lr: 1.0e-06
  clip_grad: 3.0
  freeze_last_layer_epochs: 1
  scaling_rule: sqrt_wrt_1024
  patch_embed_lr_mult: 0.2
  layerwise_decay: 0.9
  adamw_beta1: 0.9
  adamw_beta2: 0.999
crops:
  global_crops_scale:
  - 0.32
  - 1.0
  local_crops_number: 8
  local_crops_scale:
  - 0.05
  - 0.32
  global_crops_size: 224
  local_crops_size: 96
evaluation:
  eval_period_iterations: 12500