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- _base_ = [
- '../_base_/datasets/coco_detection.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
- ]
- # model settings
- model = dict(
- type='FCOS',
- data_preprocessor=dict(
- type='DetDataPreprocessor',
- mean=[102.9801, 115.9465, 122.7717],
- std=[1.0, 1.0, 1.0],
- bgr_to_rgb=False,
- pad_size_divisor=32),
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=False),
- norm_eval=True,
- style='caffe',
- init_cfg=dict(
- type='Pretrained',
- checkpoint='open-mmlab://detectron/resnet50_caffe')),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- start_level=1,
- add_extra_convs='on_output', # use P5
- num_outs=5,
- relu_before_extra_convs=True),
- bbox_head=dict(
- type='FCOSHead',
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- strides=[8, 16, 32, 64, 128],
- loss_cls=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='IoULoss', loss_weight=1.0),
- loss_centerness=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
- # testing settings
- test_cfg=dict(
- nms_pre=1000,
- min_bbox_size=0,
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100))
- # learning rate
- param_scheduler = [
- dict(type='ConstantLR', factor=1.0 / 3, by_epoch=False, begin=0, end=500),
- dict(
- type='MultiStepLR',
- begin=0,
- end=12,
- by_epoch=True,
- milestones=[8, 11],
- gamma=0.1)
- ]
- # optimizer
- optim_wrapper = dict(
- optimizer=dict(lr=0.01),
- paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.),
- clip_grad=dict(max_norm=35, norm_type=2))
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