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Running
on
Zero
Running
on
Zero
| _base_ = [ | |
| '../_base_/datasets/coco_detection.py', | |
| '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' | |
| ] | |
| # model settings | |
| model = dict( | |
| type='FOVEA', | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| bgr_to_rgb=True, | |
| 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=True), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | |
| neck=dict( | |
| type='FPN', | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| start_level=1, | |
| num_outs=5, | |
| add_extra_convs='on_input'), | |
| bbox_head=dict( | |
| type='FoveaHead', | |
| num_classes=80, | |
| in_channels=256, | |
| stacked_convs=4, | |
| feat_channels=256, | |
| strides=[8, 16, 32, 64, 128], | |
| base_edge_list=[16, 32, 64, 128, 256], | |
| scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), | |
| sigma=0.4, | |
| with_deform=False, | |
| loss_cls=dict( | |
| type='FocalLoss', | |
| use_sigmoid=True, | |
| gamma=1.50, | |
| alpha=0.4, | |
| loss_weight=1.0), | |
| loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), | |
| # training and testing settings | |
| train_cfg=dict(), | |
| test_cfg=dict( | |
| nms_pre=1000, | |
| score_thr=0.05, | |
| nms=dict(type='nms', iou_threshold=0.5), | |
| max_per_img=100)) | |
| train_dataloader = dict(batch_size=4, num_workers=4) | |
| # optimizer | |
| optim_wrapper = dict( | |
| optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)) | |