grounding_dino_swin-t_pretrain_obj365_goldg.py

_base_ = 'grounding_dino_swin-t_pretrain_obj365.py'

o365v1_od_dataset = dict(
    type='ODVGDataset',
    data_root='data/objects365v1/',
    ann_file='o365v1_train_odvg.json',
    label_map_file='o365v1_label_map.json',
    data_prefix=dict(img='train/'),
    filter_cfg=dict(filter_empty_gt=False),
    pipeline=_base_.train_pipeline,
    return_classes=True,
    backend_args=None,
)

flickr30k_dataset = dict(
    type='ODVGDataset',
    data_root='data/flickr30k_entities/',
    ann_file='final_flickr_separateGT_train_vg.json',
    label_map_file=None,
    data_prefix=dict(img='flickr30k_images/'),
    filter_cfg=dict(filter_empty_gt=False),
    pipeline=_base_.train_pipeline,
    return_classes=True,
    backend_args=None)

gqa_dataset = dict(
    type='ODVGDataset',
    data_root='data/gqa/',
    ann_file='final_mixed_train_no_coco_vg.json',
    label_map_file=None,
    data_prefix=dict(img='images/'),
    filter_cfg=dict(filter_empty_gt=False),
    pipeline=_base_.train_pipeline,
    return_classes=True,
    backend_args=None)

train_dataloader = dict(
    dataset=dict(datasets=[o365v1_od_dataset, flickr30k_dataset, gqa_dataset]))