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yolov3 network slimming剪枝的一种实现
| Date | Stars |
|---|---|
| 2026-07-30 | 343 |
| 2026-08-11 | 343 |
| 2026-09-23 | 342 |
| 2026-11-04 | 342 |
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# yolov3-network-slimming [-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE) 将[Learning Efficient Convolutional Networks Through Network Slimming (ICCV 2017)](http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.html)应用在yolov3和yolov2上<bar> # 环境 pytorch 0.41 window 10 # 如何使用 1.对原始weights文件进行稀疏化训练 python sparsity_train.py -sr --s 0.0001 --image_folder coco.data --cfg yolov3.cfg --weights yolov3.weights 2.剪枝 python prune.py --cfg yolov3.cfg --weights checkpoints/yolov3_sparsity_100.weights --percent 0.3 3.对剪枝后的weights进行微调<bar> python sparsity_train.py --image_folder coco.data --cfg prune_yolov3.cfg --weights prune_yolov3.weights # 关于new_prune.py new_prune更新了算法，现在可以确保不会有某一层被减为0的情况发生，参考[RETHINKING THE SMALLER-NORM-LESSINFORMATIVE ASSUMPTION IN CHANNEL PRUNING OF CONVOLUTION LAYERS(ICLR 2018)](https://arxiv.org/abs/1802.00124?context=cs)对剪枝后bn层β系数进行了保留 # 待完成 coco测试
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0facc27cf89f3f85, topic:pruning, readme:pruning, readme:sparsity
matched fp:0facc27cf89f3f85, topic:pytorch
matched fp:0facc27cf89f3f85, topic:yolo