OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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41 lines
1.4 KiB
41 lines
1.4 KiB
_base_ = './retinanet_r50_fpn_1x_coco.py' |
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model = dict( |
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backbone=dict( |
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norm_cfg=dict(requires_grad=False), |
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norm_eval=True, |
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style='caffe', |
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init_cfg=dict( |
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type='Pretrained', |
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checkpoint='open-mmlab://detectron2/resnet50_caffe'))) |
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# use caffe img_norm |
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img_norm_cfg = dict( |
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) |
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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(type='Resize', img_scale=(1333, 800), 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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data = dict( |
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train=dict(pipeline=train_pipeline), |
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val=dict(pipeline=test_pipeline), |
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test=dict(pipeline=test_pipeline))
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