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yolov5 pruning

时间: 2023-10-28 19:55:12 浏览: 245

基于剪枝后的yolov5模型进行知识蒸馏训练python源码+项目说明.zip

【资源说明】 1、该资源包括项目的全部源码，下载可以直接使用！ 2、本项目适合作为计算机、数学、电子信息等专业的课程设计、期末大作业和毕设项目，作为参考资料学习借鉴。 3、本资源作为“参考资料”如果需要实现其他功能，需要能看懂代码，并且热爱钻研，自行调试。基于剪枝后的yolov5模型进行知识蒸馏训练python源码+项目说明.zip 本项目实现功能如下： 1.训练自己的数据集 2.对任意卷积层进行剪枝 3.剪枝后的训练 4.剪枝后的模型预测 5.利用知识蒸馏对剪枝后模型进行训练 ``` 环境说明： gitpython>=3.1.30 matplotlib>=3.3 numpy>=1.18.5 opencv-python>=4.1.1 Pillow>=7.1.2 psutil # system resources PyYAML>=5.3.1 requests>=2.23.0 scipy>=1.4.1 thop>=0.1.1 # FLOPs computation torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended) torchvision>=0.8.1 tqdm>=4.64.0 ultralytics>=8.0.100 torch_pruning==0.2.7 pandas>=1.1.4 seaborn>=0.11.0 ``` ......

Yolov5 pruning is the process of reducing the size of a Yolov5 object detection model by removing unnecessary or redundant parameters. This is done to improve the model's performance and reduce its memory footprint, making it more efficient and faster to run. Pruning can be done using various techniques, such as weight pruning, filter pruning, and structured pruning. These techniques identify and remove the parameters that contribute the least to the model's accuracy, while preserving its overall performance. The pruned model can then be fine-tuned to improve its accuracy further.

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