60 lines
2.2 KiB
Markdown
60 lines
2.2 KiB
Markdown
# yolov4
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The Pytorch implementation is from [ultralytics/yolov3](https://github.com/ultralytics/yolov3). It can load yolov4.cfg and yolov4.weights(from AlexeyAB/darknet).
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Following tricks are used in this yolov4:
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- Three yololayer are implemented in one plugin to improve speed, codes derived from [lewes6369/TensorRT-Yolov3](https://github.com/lewes6369/TensorRT-Yolov3)
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- Mish activation, implemented in a plugin.
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- Batchnorm layer, implemented by scale layer.
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## Excute:
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```
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1. generate yolov4.wts from pytorch implementation with yolov4.cfg and yolov4.weights, or download .wts from model zoo
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git clone https://github.com/wang-xinyu/tensorrtx.git
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git clone https://github.com/ultralytics/yolov3.git
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// download yolov4.weights from https://github.com/AlexeyAB/darknet#pre-trained-models
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cd yolov3
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cp ../tensorrtx/yolov4/gen_wts.py .
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python gen_wts.py yolov4.weights
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// a file 'yolov4.wts' will be generated.
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// the master branch of yolov3 should work, if not, you can checkout be87b41aa2fe59be8e62f4b488052b24ad0bd450
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2. put yolov4.wts into ./yolov4, build and run
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mv yolov4.wts ../tensorrtx/yolov4/
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cd ../tensorrtx/yolov4
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mkdir build
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cd build
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cmake ..
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make
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sudo ./yolov4 -s // serialize model to plan file i.e. 'yolov4.engine'
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sudo ./yolov4 -d ../../yolov3-spp/samples // deserialize plan file and run inference, the images in samples will be processed.
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3. check the images generated, as follows. _zidane.jpg and _bus.jpg
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```
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/80863728-cbd3a780-8cb0-11ea-8640-7983bb41c354.jpg">
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</p>
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/80863730-cfffc500-8cb0-11ea-810e-94d693e71d80.jpg">
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</p>
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## Config
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- Input shape `INPUT_H`, `INPUT_W` defined in yololayer.h
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- Number of classes `CLASS_NUM` defined in yololayer.h
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- FP16/FP32 can be selected by the macro `USE_FP16` in yolov4.cpp
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- GPU id can be selected by the macro `DEVICE` in yolov4.cpp
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- NMS thresh `NMS_THRESH` in yolov4.cpp
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- bbox confidence threshold `BBOX_CONF_THRESH` in yolov4.cpp
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- `BATCH_SIZE` in yolov4.cpp
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## More Information
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See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)
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