duan8/yolov5

yolov5

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 or use the latest commit.
• For yolov5 v1.0, please visit yolov5 release v1.0, and checkout commit '0504551' of this repo.

Config

• Choose the model s/m/l/x by NET macro 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

How to Run, yolov5s as example

1. generate yolov5s.wts from pytorch implementation 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'
cd yolov5
cp ../tensorrtx/yolov5/gen_wts.py .
// ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py
python gen_wts.py
// a file 'yolov5s.wts' will be generated.

2. put yolov5s.wts into yolov5, build and run

mv yolov5s.wts ../tensorrtx/yolov5/
cd ../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

More Information

See the readme in home page.