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- _base_ = [
- '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
- ]
- # dataset settings
- input_size = 300
- train_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Expand',
- mean={{_base_.model.data_preprocessor.mean}},
- to_rgb={{_base_.model.data_preprocessor.bgr_to_rgb}},
- ratio_range=(1, 4)),
- dict(
- type='MinIoURandomCrop',
- min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
- min_crop_size=0.3),
- dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
- dict(type='RandomFlip', prob=0.5),
- dict(
- type='PhotoMetricDistortion',
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18),
- dict(type='PackDetInputs')
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
- dict(type='Resize', scale=(input_size, input_size), keep_ratio=False),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
- ]
- train_dataloader = dict(
- batch_size=8,
- num_workers=2,
- batch_sampler=None,
- dataset=dict(
- _delete_=True,
- type='RepeatDataset',
- times=5,
- dataset=dict(
- type={{_base_.dataset_type}},
- data_root={{_base_.data_root}},
- ann_file='annotations/instances_train2017.json',
- data_prefix=dict(img='train2017/'),
- filter_cfg=dict(filter_empty_gt=True, min_size=32),
- pipeline=train_pipeline,
- backend_args={{_base_.backend_args}})))
- val_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
- test_dataloader = val_dataloader
- # optimizer
- optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4))
- custom_hooks = [
- dict(type='NumClassCheckHook'),
- dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
- ]
- # NOTE: `auto_scale_lr` is for automatically scaling LR,
- # USER SHOULD NOT CHANGE ITS VALUES.
- # base_batch_size = (8 GPUs) x (8 samples per GPU)
- auto_scale_lr = dict(base_batch_size=64)
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