pretraining_parameters.yaml

model:
  prompt_encoder:
    name: lam_no_vit
    checkpoint: checkpoints/sam_vit_h_4b8939.pth
    use_sam_checkpoint: True
    class_attention: True
  hidden_size: 256
  pe_in_channels: 256
  clip_in_channels: 1024
dataset:
  train:
    name: coco
    instances_path: /leonardo_scratch/large/userexternal/nfanelli/annotations/instances_train2017.json
    emb_dir: &emb_dir /leonardo_scratch/large/userexternal/nfanelli/embeddings
    clip_emb_dir: &clip_emb_dir /leonardo_scratch/large/userexternal/rscaring/embeddings
    img_dir: &img_dir /leonardo_scratch/large/userexternal/nfanelli/train_val_2017
    num_examples: &num_examples 8
    do_subsample: False
    add_box_noise: True
    max_points_annotations: 50
    max_points_per_annotation: 10
    load_gts: False
  val:
    name: coco
    instances_path: /leonardo_scratch/large/userexternal/nfanelli/annotations/instances_val2017.json
    emb_dir: *emb_dir
    clip_emb_dir: *clip_emb_dir
    num_examples: *num_examples
    img_dir: *img_dir
    do_subsample: False
    add_box_noise: False
    max_points_annotations: 50
    max_points_per_annotation: 10
    load_gts: False
dataloader:
  num_workers: 8
  batch_size: 4
  drop_last: False
  shuffle: True
criterion:
  t: .7
  norm: True
optimizer:
  lr: 1e-4
scheduler:
  patience: 2
  cooldown: 2
  factor: 0.1
early_stopping:
  out_dir: /leonardo_work/IscrC_PENELOPE/raffaele/LabelAnything/data/experiment/pretrain_pe
  patience: 10
train_loop:
  num_epochs: 300
  min_num_examples: 5
  max_num_examples: 20