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- _base_ = 'ssd300_voc0712.py'
- input_size = 512
- model = dict(
- bbox_head=dict(
- in_channels=(512, 1024, 512, 256, 256, 256, 256),
- anchor_generator=dict(
- input_size=input_size,
- strides=[8, 16, 32, 64, 128, 256, 512],
- basesize_ratio_range=(0.15, 0.9),
- ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]))))
- img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
- train_pipeline = [
- dict(type='LoadImageFromFile', to_float32=True),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PhotoMetricDistortion',
- brightness_delta=32,
- contrast_range=(0.5, 1.5),
- saturation_range=(0.5, 1.5),
- hue_delta=18),
- dict(
- type='Expand',
- mean=img_norm_cfg['mean'],
- to_rgb=img_norm_cfg['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', img_scale=(512, 512), keep_ratio=False),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(512, 512),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=False),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
- ]
- data = dict(
- train=dict(dataset=dict(pipeline=train_pipeline)),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
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