# YOLOv5 TensorRTx inference code base for [ultralytics/yolov5](https://github.com/ultralytics/yolov5). ## Contributors ## Different versions of yolov5 Currently, we support yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.0 - For yolov5 v7.0, download .pt from [yolov5 release v7.0](https://github.com/ultralytics/yolov5/releases/tag/v7.0), `git clone -b v7.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v7.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v7.0/yolov5) - For yolov5 v6.2, download .pt from [yolov5 release v6.2](https://github.com/ultralytics/yolov5/releases/tag/v6.2), `git clone -b v6.2 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v6.2 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v6.2](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v6.2/yolov5) - For yolov5 v6.0, download .pt from [yolov5 release v6.0](https://github.com/ultralytics/yolov5/releases/tag/v6.0), `git clone -b v6.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v6.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v6.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v6.0/yolov5). - For yolov5 v5.0, download .pt from [yolov5 release v5.0](https://github.com/ultralytics/yolov5/releases/tag/v5.0), `git clone -b v5.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v5.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v5.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v5.0/yolov5). - For yolov5 v4.0, download .pt from [yolov5 release v4.0](https://github.com/ultralytics/yolov5/releases/tag/v4.0), `git clone -b v4.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v4.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v4.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v4.0/yolov5). - For yolov5 v3.1, download .pt from [yolov5 release v3.1](https://github.com/ultralytics/yolov5/releases/tag/v3.1), `git clone -b v3.1 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v3.1 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v3.1](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v3.1/yolov5). - For yolov5 v3.0, download .pt from [yolov5 release v3.0](https://github.com/ultralytics/yolov5/releases/tag/v3.0), `git clone -b v3.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v3.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v3.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v3.0/yolov5). - For yolov5 v2.0, download .pt from [yolov5 release v2.0](https://github.com/ultralytics/yolov5/releases/tag/v2.0), `git clone -b v2.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v2.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v2.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v2.0/yolov5). - For yolov5 v1.0, download .pt from [yolov5 release v1.0](https://github.com/ultralytics/yolov5/releases/tag/v1.0), `git clone -b v1.0 https://github.com/ultralytics/yolov5.git` and `git clone -b yolov5-v1.0 https://github.com/wang-xinyu/tensorrtx.git`, then follow how-to-run in [tensorrtx/yolov5-v1.0](https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v1.0/yolov5). ## Config - Choose the YOLOv5 sub-model n/s/m/l/x/n6/s6/m6/l6/x6 from command line arguments. - Other configs please check [src/config.h](src/config.h) ## Build and Run ### Detection 1. generate .wts from pytorch with .pt, or download .wts from model zoo ``` git clone -b v7.0 https://github.com/ultralytics/yolov5.git git clone -b yolov5-v7.0 https://github.com/wang-xinyu/tensorrtx.git cd yolov5/ wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt cp [PATH-TO-TENSORRTX]/yolov5/gen_wts.py . python gen_wts.py -w yolov5s.pt -o yolov5s.wts # A file 'yolov5s.wts' will be generated. ``` 2. build tensorrtx/yolov5 and run ``` cd [PATH-TO-TENSORRTX]/yolov5/ # Update kNumClass in src/config.h if your model is trained on custom dataset mkdir build cd build cp [PATH-TO-ultralytics-yolov5]/yolov5s.wts . cmake .. make ./yolov5_det -s [.wts] [.engine] [n/s/m/l/x/n6/s6/m6/l6/x6 or c/c6 gd gw] // serialize model to plan file ./yolov5_det -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed. # For example yolov5s ./yolov5_det -s yolov5s.wts yolov5s.engine s ./yolov5_det -d yolov5s.engine ../images # For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml ./yolov5_det -s yolov5_custom.wts yolov5.engine c 0.17 0.25 ./yolov5_det -d yolov5.engine ../images ``` 3. Check the images generated, _zidane.jpg and _bus.jpg 4. Optional, load and run the tensorrt model in Python ``` // Install python-tensorrt, pycuda, etc. // Ensure the yolov5s.engine and libmyplugins.so have been built python yolov5_det_trt.py // Another version of python script, which is using CUDA Python instead of pycuda. python yolov5_det_trt_cuda_python.py ```

### Classification ``` # Download ImageNet labels wget https://github.com/joannzhang00/ImageNet-dataset-classes-labels/blob/main/imagenet_classes.txt # Build and serialize TensorRT engine ./yolov5_cls -s yolov5s-cls.wts yolov5s-cls.engine s # Run inference ./yolov5_cls -d yolov5s-cls.engine ../images ``` ### Instance Segmentation ``` # Build and serialize TensorRT engine ./yolov5_seg -s yolov5s-seg.wts yolov5s-seg.engine s # Download the labels file wget -O coco.txt https://raw.githubusercontent.com/amikelive/coco-labels/master/coco-labels-2014_2017.txt # Run inference with labels file ./yolov5_seg -d yolov5s-seg.engine ../images coco.txt ```

# 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 yolov5/build 3. set the macro `USE_INT8` in src/config.h and make 4. serialize the model and test ## More Information See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)