YOLOv8-distillation yolov8剪枝+蒸馏

YOLOv8 轻量化并且提升精度 !

Prepare the environment

1. Install CUDA follow CUDA official website.

   🚀 RECOMMENDED CUDA >= 11.4

2. Install TensorRT follow TensorRT official website.

   🚀 RECOMMENDED TensorRT >= 8.4

3. Install python requirements.

   pip install -r requirements.txt

4. Install ultralytics package for ONNX export or TensorRT API building.

   pip install ultralytics

5. Prepare your own PyTorch weight such as yolov8s.pt.

NOTICE:

Please use the latest CUDA and TensorRT, so that you can achieve the fastest speed !

If you have to use a lower version of CUDA and TensorRT, please read the relevant issues carefully !

Normal Usage

python train_distillation.py

提示

1.准备好配置环境

2.准备好（训练好的）老师模型和（训练好的）学生模型

3.更改写在类Distillation_loss的 channels_s和channels_t，将通道数改成自己模型的通道数

csdn上有详细教程，链接：https://blog.csdn.net/W_extend/article/details/140902235?spm=1001.2014.3001.5502