coco:
  dataset: 'coco'
  data_path: '/workspace_dataset/dataset_vqa'
  label_path: '/workspace_dataset/dataset_experts'
  experts: ['depth', 'normal', 'seg_coco', 'edge', 'obj_detection', 'ocr_detection']  # 'none' for PrismerZ
  image_resolution: 480
  prismer_model: 'prismer_base'  # 'prismer-large' for Prismer(Z)-Large
  freeze: 'freeze_vision'

  batch_size_train: 4  # for 8 * 8 nodes [effective batch-size: 256]
  batch_size_test: 8
  init_lr: 5e-5
  weight_decay: 0.05
  min_lr: 0
  max_epoch: 3

  prefix: 'A picture of'  # use prefix for fine-tuning or no pre-fix '' for zero-shot experiments

nocaps:
  dataset: 'nocaps'
  data_path: '/workspace_dataset/dataset_vqa'
  label_path: '/workspace_dataset/dataset_experts'
  experts: ['depth', 'normal', 'seg_coco', 'edge', 'obj_detection', 'ocr_detection']  # 'none' for PrismerZ

  image_resolution: 480
  prismer_model: 'prismer_base'  # 'prismer-large' for Prismer(Z)-Large
  freeze: 'freeze_vision'

  batch_size_train: 4  # for 8 * 8 nodes [effective batch-size: 256]
  batch_size_test: 8
  init_lr: 5e-5
  weight_decay: 0.05
  min_lr: 0
  max_epoch: 3

  prefix: 'A picture of' # use prefix for fine-tuning or no pre-fix '' for zero-shot experiments

demo:
  dataset: 'demo'
  data_path: 'helpers'
  label_path: 'helpers/labels'
  experts: ['depth', 'normal', 'seg_coco', 'edge', 'obj_detection', 'ocr_detection']  # 'none' for PrismerZ

  image_resolution: 480
  prismer_model: 'prismer_base'  # 'prismer-large' for Prismer(Z)-Large
  freeze: 'freeze_vision'

  prefix: 'A picture of'