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- _base_ = 'ssd300_coco.py'
- input_size = 512
- model = dict(
- neck=dict(
- out_channels=(512, 1024, 512, 256, 256, 256, 256),
- level_strides=(2, 2, 2, 2, 1),
- level_paddings=(1, 1, 1, 1, 1),
- last_kernel_size=4),
- bbox_head=dict(
- in_channels=(512, 1024, 512, 256, 256, 256, 256),
- anchor_generator=dict(
- type='SSDAnchorGenerator',
- scale_major=False,
- input_size=input_size,
- basesize_ratio_range=(0.1, 0.9),
- strides=[8, 16, 32, 64, 128, 256, 512],
- ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]])))
- # dataset settings
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- 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(
- samples_per_gpu=8,
- workers_per_gpu=3,
- train=dict(
- _delete_=True,
- type='RepeatDataset',
- times=5,
- dataset=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline)),
- val=dict(pipeline=test_pipeline),
- test=dict(pipeline=test_pipeline))
- # optimizer
- optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
- optimizer_config = dict(_delete_=True)
- custom_hooks = [
- dict(type='NumClassCheckHook'),
- dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
- ]
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