yolov4

yolov4

The Pytorch implementation is from ultralytics/yolov3. It can load yolov4.cfg and yolov4.weights(from AlexeyAB/darknet).

Following tricks are used in this yolov4:

• Three yololayer are implemented in one plugin to improve speed, codes derived from lewes6369/TensorRT-Yolov3
• Mish activation, implemented in a plugin.
• Batchnorm layer, implemented by scale layer.

Excute:

1. generate yolov4.wts from pytorch implementation with yolov4.cfg and yolov4.weights

git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov3.git
// download yolov4.weights from https://github.com/AlexeyAB/darknet#pre-trained-models
cd yolov3
cp ../tensorrtx/yolov4/gen_wts.py .
python gen_wts.py yolov4.weights
// a file 'yolov4.wts' will be generated.
// the master branch of yolov3 should work, if not, you can checkout be87b41aa2fe59be8e62f4b488052b24ad0bd450

2. put yolov4.wts into ./yolov4, build and run

mv yolov4.wts ../tensorrtx/yolov4/
cd ../tensorrtx/yolov4
mkdir build
cd build
cmake ..
make
sudo ./yolov4 -s             // serialize model to plan file i.e. 'yolov4.engine'
sudo ./yolov4 -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

Config

• Input shape INPUT_H, INPUT_W defined in yololayer.h
• Number of classes CLASS_NUM defined in yololayer.h
• FP16/FP32 can be selected by the macro USE_FP16 in yolov4.cpp
• GPU id can be selected by the macro DEVICE in yolov4.cpp
• NMS thresh NMS_THRESH in yolov4.cpp
• bbox confidence threshold BBOX_CONF_THRESH in yolov4.cpp
• BATCH_SIZE in yolov4.cpp

More Information

See the readme in home page.