YOLO-NAS-OpenVino-cpp

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YOLO-NAS-OpenVino-cpp

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C++ implementation of YOLO-NAS utilizing OpenVINO backend

YOLO-NAS-OpenVino-cpp 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# YOLO-NAS-OpenVino.cpp

YOLO-NAS is a state-of-the-art object detector by [Deci
AI](https://github.com/Deci-AI/super-gradients). This
project implements the YOLO-NAS object detector in C++ with
an OpenVINO backend to speed up inference performance.

## Features

* Supports both **image** and **video** inference.
* **Faster** inference speeds.

## Getting Started

The following instructions demonstrates how to build this
project on a Windows system and Linux systems supported by OpenVINO.

### Prerequisites

* **CMake v3.8+** - found at
[https://cmake.org/](https://cmake.org/)

* **MSVC 2017++ (Windows Build)** - MinGW will not work on Windows Build as OpenVINO
libraries are not compatible with MinGW.

* **GNU (Linux Build)** - tested on v11.4.0.

* **OpenVINO Toolkit** - tested on 2022.1. Download
[here](https://storage.openvinotoolkit.org/repositories/openvino/packages/).

* **OpenCV v4.0+** - tested on v4.7. Download
[here](https://github.com/opencv/opencv/releases/).

## Building the project

1. Set the `OpenCV_DIR` environment variable to point to
your `../../opencv/build` directory.
2. Set the `OpenVINO_DIR` environment variable to point to
your `../../openvino/runtime/cmake` directory.
3. Run the following build commands:

a. [Windows] VS Developer Command Prompt:

```bash
cd \d <yolo-nas-openvino-cpp-directory>
cmake -S. -Bbuild -DCMAKE_BUILD_TYPE=Release
cd build

MSBuild yolo-nas-openvino-cpp.sln /property:Configuration=Release
```

b. [Linux] Bash:

```bash
cd <yolo-nas-openvino-cpp-directory>
cmake -S. -Bbuild -DCMAKE_BUILD_TYPE=Release
cd build

make
```

4. The compiled `.exe` will be inside the `Release` folder for Windows build, while the executable will be in root folder for Linux build.

## Inference

1. Export the ONNX file:
```python
from super_gradients.training import models

model = models.get("yolo_nas_s", pretrained_weights="coco")
model.eval()
model.prep_model_for_conversion(input_size=(1, 3, 640, 640))
models.convert_to_onnx(model=model, prep_model_for_conversion_kwargs={"input_size":(1, 3, 640, 640)}, out_path="yolo_nas_s.onnx")
```

2. Convert the ONNX model to OpenVINO IR:
```bash
mo --input_model yolo_nas_s.onnx -s 255 --reverse_input_channels
```

3. To run the inference, execute the following command:
```bash
yolo-nas-openvino-cpp --model <OPENVINO_IR_XML_PATH> [-i <IMAGE_PATH> | -v <VIDEO_PATH>] [--imgsz IMAGE_SIZE] [--gpu] [--iou-thresh IOU_THRESHOLD] [--score-thresh CONFIDENCE_THRESHOLD]
```

## Benchmarks

The following benchmarks were done on Google Colab using Intel® Xeon® Processor E5-2699 v4 @ 2.20GHz with 2 vCPUs.

| **Backend**               | **Latency** | **FPS** | **Implementation**                                                        |
|---------------------------|-------------|---------|---------------------------------------------------------------------------|
| PyTorch                   | 867.02ms    | 1.15    | Native (`model.predict()` in `super_gradients`)                           |
| ONNX C++ (via OpenCV DNN) | 962.27ms    | 1.04    | [Hyuotu](https://github.com/Hyuto/yolo-nas-onnx/tree/master/yolo-nas-cpp) |
| ONNX Python               | 626.37ms    | 1.59    | [Hyuotu](https://github.com/Hyuto/yolo-nas-onnx/tree/master/yolo-nas-py)  |
| OpenVINO C++              | 628.04ms    | 1.59    | [Y-T-G](https://github.com/Y-T-G/yolo-nas-openvino-cpp)                   |

## Authors

* **Mohammed Yasin** - [@Y-T-G](https://github.com/Y-T-G)

## Acknowledgements

Thanks to [@Hyuto](https://github.com/Hyuto) for his work on
[ONNX
implementation](https://github.com/Hyuto/yolo-nas-onnx/tree/master/yolo-nas-cpp) of
YOLO-NAS in C++ which was utilized in this project.

## License

This project is licensed under the
[MIT](https://mit-license.org/) License - see the
[LICENSE](LICENSE) file for details.

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