YOLOX-TensorRT in C++

As YOLOX models are easy to convert to tensorrt using torch2trt gitrepo, our C++ demo does not include the model converting or constructing like other tenorrt demos.

Step 1: Prepare serialized engine file

Follow the trt python demo README to convert and save the serialized engine file.

Check the ‘model_trt.engine’ file generated from Step 1, which will be automatically saved at the current demo dir.

Step 2: build the demo

Please follow the TensorRT Installation Guide to install TensorRT.

And you should set the TensorRT path and CUDA path in CMakeLists.txt.

If you train your custom dataset, you may need to modify the value of num_class.

const int num_class = 80;

Install opencv with sudo apt-get install libopencv-dev (we don’t need a higher version of opencv like v3.3+).

build the demo:

mkdir build
cd build
cmake ..

Then run the demo:

./yolox ../model_trt.engine -i ../../../../assets/dog.jpg


./yolox <path/to/your/engine_file> -i <path/to/image>