DBNet

The Pytorch implementation is DBNet.

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How to Run

• 1. generate .wts

  Download code and model from DBNet and config your environments.

  Go to filetools/predict.py, set --save_wts as True, then run, the DBNet.wts will be generated.

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

• 2. cmake and make
  Copy

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

For windows

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

Todo

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

  c++Copy

  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) 
  

  c++Copy

  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 640 x 640 directly, while the pytorch side is using letterbox method.