tensorrt-yolov9

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tensorrt-yolov9

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Cpp and python implementation of YOLOv9 using TensorRT API

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TensorRT-YOLOv9
===========================

[![python](https://img.shields.io/badge/python-3.10.12-green)](https://www.python.org/downloads/release/python-31012/)
[![cuda](https://img.shields.io/badge/cuda-11.6-green)](https://developer.nvidia.com/cuda-downloads)
[![trt](https://img.shields.io/badge/TRT-8.6-green)](https://developer.nvidia.com/tensorrt)
[![mit](https://img.shields.io/badge/license-MIT-blue)](https://github.com/spacewalk01/TensorRT-YOLOv9/tree/main?tab=MIT-1-ov-file#readme)

<div align="left">
 
This repo hosts a C++ and python implementation of the [YOLOv9](https://github.com/WongKinYiu/yolov9) state of the art object detection model, leveraging the TensorRT API for efficient real-time inference.
<p align="center" margin: 0 auto;>
  <img src="assets/traffic.gif" width="360px" />
  <img src="assets/parkinglot.gif" width="360px" /> 
</p>

## 🚀 Usage

#### Python

``` shell
cd <this project path>/python
python yolov9_trt.py --engine yolov9-c.engine --data images --outdir output
```

#### C++

``` shell
cd <this project path>/build/release

# infer an image
yolov9-tensorrt.exe yolov9-c.engine test.jpg
# infer a folder(images)
yolov9-tensorrt.exe yolov9-c.engine data
# infer a video
yolov9-tensorrt.exe yolov9-c.engine test.mp4 # the video path
```

## 🛠️ Build

#### Python

The following command will install tensorrt for python:

``` shell
cd <tensorrt installation path>/python
pip install cuda-python
pip install tensorrt-8.6.0-cp310-none-win_amd64.whl
pip install opencv-python
```

#### C++

Refer to our [docs/INSTALL.md](https://github.com/spacewalk01/tensorrt-yolov9/blob/main/docs/INSTALL.md) for detailed installation instructions.

- Cuda preprocessing: [main branch](https://github.com/spacewalk01/TensorRT-YOLOv9/tree/main)
- Cpu preprocessing: [cpu-preprocessing branch](https://github.com/spacewalk01/TensorRT-YOLOv9/tree/cpu_preprocessing)
  
## 🖥️ Requirement
   - TensorRT
   - CUDA, CudaNN
   - C++ compiler with C++17 or higher support
   - CMake 3.14 or higher
   - OpenCV

## 🌱 Examples

Parameters for inference:

``` 
Confidence threshold : 0.2
NMS threshold : 0.3
Model : yolov9-e (with FP32)
```

Inference result:

<p align="center" margin: 0 auto;>
  <img src="assets/street_o.jpg" /> 
</p>
Original image: https://www.flickr.com/photos/nicolelee/19041780

## 👏 Acknowledgement

This project is based on the following awesome projects:
- [YOLOv9](https://github.com/WongKinYiu/yolov9) - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information.
- [TensorRT](https://github.com/NVIDIA/TensorRT/tree/release/8.6/samples) - TensorRT samples and api documentation.
- [TensorRTx](https://github.com/wang-xinyu/tensorrtx) - Implementation of popular deep learning networks with TensorRT network definition API.

## 🔗 See also
- [Yolov9-Bytetrack](https://github.com/spacewalk01/yolov9-bytetrack-tensorrt) - Integration of YOLOv9 with ByteTracker using the TensorRT API.

tensorrt-yolov9

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