Update README.md

yolov4/README.md

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-# yolov4 working in progress
+# yolov4
 
+The Pytorch implementation is from [ultralytics/yolov3](https://github.com/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](https://github.com/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
+```
+
+<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>