OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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76 lines
2.4 KiB
76 lines
2.4 KiB
_base_ = '../_base_/default_runtime.py' |
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# dataset settings |
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dataset_type = 'CocoDataset' |
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data_root = 'data/coco/' |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], |
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# multiscale_mode='range' |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', with_bbox=True), |
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dict( |
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type='Resize', |
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img_scale=[(1333, 640), (1333, 800)], |
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multiscale_mode='range', |
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keep_ratio=True), |
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dict(type='RandomFlip', flip_ratio=0.5), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(1333, 800), |
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='Pad', size_divisor=32), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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# Use RepeatDataset to speed up training |
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data = dict( |
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samples_per_gpu=2, |
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workers_per_gpu=2, |
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train=dict( |
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type='RepeatDataset', |
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times=3, |
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dataset=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_train2017.json', |
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img_prefix=data_root + 'train2017/', |
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pipeline=train_pipeline)), |
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val=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_val2017.json', |
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img_prefix=data_root + 'val2017/', |
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pipeline=test_pipeline), |
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test=dict( |
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type=dataset_type, |
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ann_file=data_root + 'annotations/instances_val2017.json', |
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img_prefix=data_root + 'val2017/', |
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pipeline=test_pipeline)) |
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evaluation = dict(interval=1, metric='bbox') |
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# optimizer |
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optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) |
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optimizer_config = dict(grad_clip=None) |
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# learning policy |
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# Experiments show that using step=[9, 11] has higher performance |
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lr_config = dict( |
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policy='step', |
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warmup='linear', |
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warmup_iters=500, |
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warmup_ratio=0.001, |
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step=[9, 11]) |
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runner = dict(type='EpochBasedRunner', max_epochs=12)
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