|
_base_ = [ |
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'../_base_/datasets/coco_detection.py', |
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' |
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] |
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model = dict( |
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type='GFL', |
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backbone=dict( |
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type='ResNet', |
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depth=50, |
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num_stages=4, |
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out_indices=(0, 1, 2, 3), |
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frozen_stages=1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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norm_eval=True, |
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style='pytorch', |
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), |
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neck=dict( |
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type='FPN', |
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in_channels=[256, 512, 1024, 2048], |
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out_channels=256, |
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start_level=1, |
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add_extra_convs='on_output', |
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num_outs=5), |
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bbox_head=dict( |
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type='GFLHead', |
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num_classes=80, |
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in_channels=256, |
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stacked_convs=4, |
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feat_channels=256, |
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anchor_generator=dict( |
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type='AnchorGenerator', |
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ratios=[1.0], |
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octave_base_scale=8, |
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scales_per_octave=1, |
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strides=[8, 16, 32, 64, 128]), |
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loss_cls=dict( |
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type='QualityFocalLoss', |
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use_sigmoid=True, |
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beta=2.0, |
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loss_weight=1.0), |
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loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), |
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reg_max=16, |
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loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), |
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|
|
train_cfg=dict( |
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assigner=dict(type='ATSSAssigner', topk=9), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False), |
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test_cfg=dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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nms=dict(type='nms', iou_threshold=0.6), |
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max_per_img=100)) |
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|
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optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) |
|
|
