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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'
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