tensorrtx 官网可下

# yolov8 The Pytorch implementation is [ultralytics/yolov8](https://github.com/ultralytics/ultralytics/tree/main/ultralytics). The tensorrt code is derived from [xiaocao-tian/yolov8_tensorrt](https://github.com/xiaocao-tian/yolov8_tensorrt) ## Contributors <a href="https://github.com/xiaocao-tian"><img src="https://avatars.githubusercontent.com/u/65889782?v=4?s=48" width="40px;" alt=""/></a> <a href="https://github.com/lindsayshuo"><img src="https://avatars.githubusercontent.com/u/45239466?v=4?s=48" width="40px;" alt=""/></a> <a href="https://github.com/xinsuinizhuan"><img src="https://avatars.githubusercontent.com/u/40679769?v=4?s=48" width="40px;" alt=""/></a> ## Requirements - TensorRT 8.0+ - OpenCV 3.4.0+ ## Different versions of yolov8 Currently, we support yolov8 - For yolov8 , download .pt from [https://github.com/ultralytics/assets/releases](https://github.com/ultralytics/assets/releases), then follow how-to-run in current page. ## Config - Choose the model n/s/m/l/x from command line arguments. - Check more configs in [include/config.h](./include/config.h) ## How to Run, yolov8n as example 1. generate .wts from pytorch with .pt, or download .wts from model zoo ``` // download https://github.com/ultralytics/assets/releases/yolov8n.pt cp {tensorrtx}/yolov8/gen_wts.py {ultralytics}/ultralytics cd {ultralytics}/ultralytics python gen_wts.py // a file 'yolov8n.wts' will be generated. ``` 2. build tensorrtx/yolov8 and run ``` cd {tensorrtx}/yolov8/ // update kNumClass in config.h if your model is trained on custom dataset mkdir build cd build cp {ultralytics}/ultralytics/yolov8.wts {tensorrtx}/yolov8/build cmake .. make sudo ./yolov8 -s [.wts] [.engine] [n/s/m/l/x] // serialize model to plan file sudo ./yolov8 -d [.engine] [image folder] [c/g] // deserialize and run inference, the images in [image folder] will be processed. // For example yolov8 sudo ./yolov8 -s yolov8n.wts yolov8.engine n sudo ./yolov8 -d yolov8n.engine ../images c //cpu postprocess sudo ./yolov8 -d yolov8n.engine ../images g //gpu postprocess ``` 3. check the images generated, as follows. _zidane.jpg and _bus.jpg 4. optional, load and run the tensorrt model in python ``` // install python-tensorrt, pycuda, etc. // ensure the yolov8n.engine and libmyplugins.so have been built python yolov8_trt.py ``` # INT8 Quantization 1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh 2. unzip it in yolov8/build 3. set the macro `USE_INT8` in config.h and make 4. serialize the model and test <p align="center"> <img src="https://user-images.githubusercontent.com/15235574/78247927-4d9fac00-751e-11ea-8b1b-704a0aeb3fcf.jpg" height="360px;"> </p> ## More Information See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)