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- _base_ = [
- '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
- ]
- # model settings
- input_size = 300
- model = dict(
- bbox_head=dict(
- type='SSDHead',
- anchor_generator=dict(
- type='LegacySSDAnchorGenerator',
- scale_major=False,
- input_size=input_size,
- basesize_ratio_range=(0.15, 0.9),
- strides=[8, 16, 32, 64, 100, 300],
- ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
- bbox_coder=dict(
- type='LegacyDeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.1, 0.1, 0.2, 0.2])))
- # 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=(300, 300), 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=(300, 300),
- 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)
- dist_params = dict(backend='nccl', port=29555)
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