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- _base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages.py']
- model = dict(
- roi_head=dict(bbox_head=dict(num_classes=500)),
- test_cfg=dict(rcnn=dict(score_thr=0.01)))
- # dataset settings
- dataset_type = 'OpenImagesChallengeDataset'
- train_dataloader = dict(
- dataset=dict(
- type=dataset_type,
- ann_file='challenge2019/challenge-2019-train-detection-bbox.txt',
- label_file='challenge2019/cls-label-description.csv',
- hierarchy_file='challenge2019/class_label_tree.np',
- meta_file='challenge2019/challenge-2019-train-metas.pkl'))
- val_dataloader = dict(
- dataset=dict(
- type=dataset_type,
- ann_file='challenge2019/challenge-2019-validation-detection-bbox.txt',
- data_prefix=dict(img='OpenImages/'),
- label_file='challenge2019/cls-label-description.csv',
- hierarchy_file='challenge2019/class_label_tree.np',
- meta_file='challenge2019/challenge-2019-validation-metas.pkl',
- image_level_ann_file='challenge2019/challenge-2019-validation-'
- 'detection-human-imagelabels.csv'))
- test_dataloader = dict(
- dataset=dict(
- type=dataset_type,
- ann_file='challenge2019/challenge-2019-validation-detection-bbox.txt',
- label_file='challenge2019/cls-label-description.csv',
- hierarchy_file='challenge2019/class_label_tree.np',
- meta_file='challenge2019/challenge-2019-validation-metas.pkl',
- image_level_ann_file='challenge2019/challenge-2019-validation-'
- 'detection-human-imagelabels.csv'))
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (32 GPUs) x (2 samples per GPU)
- auto_scale_lr = dict(base_batch_size=64)
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