YOLO-NAS-OnnxRuntime (FP32/FP16/INT8)

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

➤ Contents

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]

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 :

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 .

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
- --half : Convert fp32 to fp16 model.
  
  [ Custom Model Args ]
- --checkpoint_path : The path with save the trained pth model
- --class_names : The path to class names file
  
  [ PTQ Args ]
- --int8 : Conver to int8.
- --calib_image_dir : if None will use dynamic quantization.
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

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

GPLv3 License key requirements :