dbnet

DBNet

The Pytorch implementation is DBNet.

How to Run

• 1. generate .wts

  Download code and model from DBNet and config your environments.

  In tools/predict.py, set save_wts as True, and run, the .wts will be generated.

  onnx can also be exported, just need to set onnx as True.

• 2. cmake and make

  mkdir build
  cd build
  cmake ..
  make
  sudo ./dbnet -s             // serialize model to plan file i.e. 'DBNet.engine'
  sudo ./dbnet -d  ../samples // deserialize plan file and run inference, the images in samples will be processed.
  

For windows

https://github.com/BaofengZan/DBNet-TensorRT

Todo

• 1. In common.hpp, the following two functions can be merged.

ILayer* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname, bool bias = true) 

ILayer* convBnLeaky2(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname, bool bias = true)

• 2. The postprocess method here should be optimized, which is a little different from pytorch side.
• 3. The input image here is resized to 640x640 directly, while the pytorch side is using letterbox method.