The Pytorch implementation is ultralytics/yolov3. It provides two trained weights of yolov3, yolov3.weights and yolov3.pt
This branch is using tensorrt7 API, there is also a yolov3 implementation using tensorrt4 API, go to branch trt4/yolov3, which is using ayooshkathuria/pytorch-yolo-v3.
1. generate yolov3.wts from pytorch implementation with yolov3.cfg and yolov3.weights
git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov3.git
// download its weights 'yolov3.pt' or 'yolov3.weights'
cd yolov3
cp ../tensorrtx/yolov3/gen_wts.py .
python gen_wts.py yolov3.weights
// a file 'yolov3.wts' will be generated.
// the master branch of yolov3 should work, if not, you can checkout cf7a4d31d37788023a9186a1a143a2dab0275ead
2. put yolov3.wts into tensorrtx/yolov3, build and run
mv yolov3.wts ../tensorrtx/yolov3/
cd ../tensorrtx/yolov3
mkdir build
cd build
cmake ..
make
sudo ./yolov3 -s // serialize model to plan file i.e. 'yolov3.engine'
sudo ./yolov3 -d ../../yolov3-spp/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
- Input shape defined in yololayer.h
- Number of classes defined in yololayer.h
- FP16/FP32 can be selected by the macro in yolov3.cpp
- GPU id can be selected by the macro in yolov3.cpp
- NMS thresh in yolov3.cpp
- BBox confidence thresh in yolov3.cpp
See the readme in home page.