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_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='FOVEA', |
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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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num_outs=5, |
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add_extra_convs='on_input'), |
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bbox_head=dict( |
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type='FoveaHead', |
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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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strides=[8, 16, 32, 64, 128], |
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base_edge_list=[16, 32, 64, 128, 256], |
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scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), |
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sigma=0.4, |
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with_deform=False, |
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loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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gamma=1.50, |
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alpha=0.4, |
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loss_weight=1.0), |
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loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), |
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|
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train_cfg=dict(), |
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test_cfg=dict( |
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nms_pre=1000, |
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score_thr=0.05, |
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nms=dict(type='nms', iou_threshold=0.5), |
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max_per_img=100)) |
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data = dict(samples_per_gpu=4, workers_per_gpu=4) |
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|
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optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) |
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|
