duan8/yolov4/README.md
2021-04-26 12:29:44 +00:00

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# yolov4
The Pytorch implementation is from [ultralytics/yolov3 archive branch](https://github.com/ultralytics/yolov3/tree/archive). It can load yolov4.cfg and yolov4.weights(from AlexeyAB/darknet).
## 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
## How to run
1. generate yolov4.wts from pytorch implementation with yolov4.cfg and yolov4.weights, or download .wts from model zoo
```
git clone https://github.com/wang-xinyu/tensorrtx.git
git clone -b archive https://github.com/ultralytics/yolov3.git
// download yolov4.weights from https://github.com/AlexeyAB/darknet#pre-trained-models
cp {tensorrtx}/yolov4/gen_wts.py {ultralytics/yolov3/}
cd {ultralytics/yolov3/}
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 {tensorrtx}/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
<p align="center">
<img src="https://user-images.githubusercontent.com/15235574/80863728-cbd3a780-8cb0-11ea-8640-7983bb41c354.jpg">
</p>
<p align="center">
<img src="https://user-images.githubusercontent.com/15235574/80863730-cfffc500-8cb0-11ea-810e-94d693e71d80.jpg">
</p>
## More Information
See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)