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- _base_ = '../_base_/default_runtime.py'
- # model settings
- model = dict(
- type='YOLOV3',
- backbone=dict(
- type='Darknet',
- depth=53,
- out_indices=(3, 4, 5),
- init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
- neck=dict(
- type='YOLOV3Neck',
- num_scales=3,
- in_channels=[1024, 512, 256],
- out_channels=[512, 256, 128]),
- bbox_head=dict(
- type='YOLOV3Head',
- num_classes=80,
- in_channels=[512, 256, 128],
- out_channels=[1024, 512, 256],
- anchor_generator=dict(
- type='YOLOAnchorGenerator',
- base_sizes=[[(116, 90), (156, 198), (373, 326)],
- [(30, 61), (62, 45), (59, 119)],
- [(10, 13), (16, 30), (33, 23)]],
- strides=[32, 16, 8]),
- bbox_coder=dict(type='YOLOBBoxCoder'),
- featmap_strides=[32, 16, 8],
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0,
- reduction='sum'),
- loss_conf=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0,
- reduction='sum'),
- loss_xy=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=2.0,
- reduction='sum'),
- loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
- # training and testing settings
- train_cfg=dict(
- assigner=dict(
- type='GridAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0)),
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- conf_thr=0.005,
- nms=dict(type='nms', iou_threshold=0.45),
- max_per_img=100))
- # dataset settings
- dataset_type = 'CocoDataset'
- data_root = 'data/coco/'
- img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
- train_pipeline = [
- dict(type='LoadImageFromFile', to_float32=True),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='PhotoMetricDistortion'),
- dict(
- type='Expand',
- mean=img_norm_cfg['mean'],
- to_rgb=img_norm_cfg['to_rgb'],
- ratio_range=(1, 2)),
- dict(
- type='MinIoURandomCrop',
- min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
- min_crop_size=0.3),
- dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(608, 608),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
- ]
- data = dict(
- samples_per_gpu=8,
- workers_per_gpu=4,
- train=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_train2017.json',
- img_prefix=data_root + 'train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=data_root + 'annotations/instances_val2017.json',
- img_prefix=data_root + 'val2017/',
- pipeline=test_pipeline))
- # optimizer
- optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
- optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
- # learning policy
- lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=2000, # same as burn-in in darknet
- warmup_ratio=0.1,
- step=[218, 246])
- # runtime settings
- runner = dict(type='EpochBasedRunner', max_epochs=273)
- evaluation = dict(interval=1, metric=['bbox'])
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