The Pytorch implementation is ultralytics/yolov5.
Currently, we support yolov5 v1.0(yolov5s only), v2.0 and v3.0.
- For yolov5 v3.0, please visit yolov5 release v3.0, and use the latest commit of this repo.
- For yolov5 v2.0, please visit yolov5 release v2.0, and checkout commit '7cd092d' of this repo.
- For yolov5 v1.0, please visit yolov5 release v1.0, and checkout commit '0504551' of this repo.
- Choose the model s/m/l/x by
NETmacro in yolov5.cpp - Input shape defined in yololayer.h
- Number of classes defined in yololayer.h
- FP16/FP32 can be selected by the macro in yolov5.cpp
- GPU id can be selected by the macro in yolov5.cpp
- NMS thresh in yolov5.cpp
- BBox confidence thresh in yolov5.cpp
- Batch size in yolov5.cpp
1. generate yolov5s.wts from pytorch with yolov5s.pt
git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov5.git
// download its weights 'yolov5s.pt'
// copy tensorrtx/yolov5/gen_wts.py into ultralytics/yolov5
// ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py
// go to ultralytics/yolov5
python gen_wts.py
// a file 'yolov5s.wts' will be generated.
2. build tensorrtx/yolov5 and run
// put yolov5s.wts into tensorrtx/yolov5
// go to tensorrtx/yolov5
// ensure the macro NET in yolov5.cpp is s
mkdir build
cd build
cmake ..
make
sudo ./yolov5 -s // serialize model to plan file i.e. 'yolov5s.engine'
sudo ./yolov5 -d ../samples // deserialize plan file and run inference, the images in samples will be processed.
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 yolov5s.engine and libmyplugins.so have been built
python yolov5_trt.py
See the readme in home page.