YOLO-NAS-OnnxRuntime

This repo provides the C++ implementation of YOLO-NAS based on ONNXRuntime for performing object detection in real-time.

➤ Contents

1. Dependecies

2. Build

3. Examples

4. Demo

5. References

6. License

➤ Dependecies

• Python Install :

  Quick installation from SuperGradients package

    pip install super_gradients==3.1.1
  

• C++ Install :

  • OpenCV 4.x
  • ONNXRuntime 1.10+
  • OS: Tested on Ubuntu 20.04 and Apple Silicon (M1)
  • CUDA 11+ [Optional]

➤ Build

Rapidly build the project you can run the following commands:

# You can change ONNXRUNTIME_VERSION="x.x.x" in line 4.

./build.sh

If you want to use your own onnxruntime version, decompress the tgz library of onnxruntime :

onnxruntime repo: https://github.com/microsoft/onnxruntime/tags

tar zxvf onnxruntime-linux-x64-gpu-1.10.0.tgz

Next, don't forget to change ONNXRUNTIME_DIR cmake option:

mkdir build && cd build
cmake .. -D ONNXRUNTIME_DIR=path_to_onnxruntime -DCMAKE_BUILD_TYPE=Release
cmake --build .

➤ Examples

• Comvert Pytorch to ONNX model :

  # default coco model
  python3 convertPytorchToONNX.py --input_model yolo_nas_s --output_dir ./models --img-size 640 640 
  
  # your custom model
  python3 convertPytorchToONNX.py --input_model yolo_nas_s --output_dir ./models --img-size 640 640 --checkpoint_path <path-to-pth-model> --class_names <path-to-names-file>
  

  Description of all arguments:

  • --input_model : Type contains { yolo_nas_s / yolo_nas_m / yolo_nas_l }

  • --img-size : Set model input size (h, w)

  • --output_dir : Directory for saving files, none means using the same path as the input model

  • --checkpoint_path : The path with save the trained pth model

  • --class_names : The path to class names file

  
  YOLO-NAS ~0.5 mAP by official information:

  Model	mAP	Latency (ms)
  YOLO-NAS S	47.5	3.21
  YOLO-NAS M	51.55	5.85
  YOLO-NAS L	52.22	7.87
  YOLO-NAS S INT-8	47.03	2.36
  YOLO-NAS M INT-8	51.0	3.78
  YOLO-NAS L INT-8	52.1	4.78

• Run webcam source from CLI :

  ./demo --model_path ../models/yolo_nas_s.onnx --source 0 --class_names ../models/coco.names --gpu

• Run video source from CLI :

  ./demo --model_path ../models/yolo_nas_s.onnx --source ../demo/video.mp4 --class_names ../models/coco.names --gpu

• Run image source from CLI :

  ./demo --model_path ../models/yolo_nas_s.onnx --source ../demo/traffic.jpg --class_names ../models/coco.names --gpu

➤ Demo

➤ References

• YOLO-Nas repo: https://github.com/Deci-AI/super-gradients

➤ License

WiFi Analyzer is licensed under the GNU General Public License v3.0 (GPLv3).

GPLv3 License key requirements :

• Disclose Source
• License and Copyright Notice
• Same License
• State Changes