Multi-Threaded YOLOv7 ONNX With OpenCV

Introduction

Multi-Threaded YOLOv7 ONNX With OpenCV is a GitHub repository that implements the YOLOv7 object detection model using ONNX for inference and leverages OpenCV for real-time video and image processing. It's designed to provide high-performance, real-time object detection, making it suitable for various computer vision applications.

Features

• Real-time object detection with YOLOv7 using ONNX.
• Multi-threaded inference for improved speed.
• Customizable for different YOLOv7 configurations and datasets.

Getting Started

Prerequisites

Before using this repository, make sure you have the following:

• Python 3.6+
• OpenCV
• NumPy
• ONNX
• ONNX Runtime (for optimized inference)
• Pre-trained YOLOv7 ONNX model weights (available from the official YOLOv7 repository)

Installation

1. Clone the repository:

   git clone https://github.com/SihabSahariar/Multi-Threaded-YOLOv7-ONNX-With-OpenCV.git
   cd Multi-Threaded-YOLOv7-ONNX-With-OpenCV
   

2. Install the required Python packages:

   pip install -r requirements.txt
   

3. Run the app

python app.py

Contributing

Contributions to this project are welcome! If you find a bug or have a feature request, please open an issue. If you would like to contribute code, please fork the repository and create a pull request.