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.
- Real-time object detection with YOLOv7 using ONNX.
- Multi-threaded inference for improved speed.
- Customizable for different YOLOv7 configurations and datasets.
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)
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Clone the repository:
git clone https://github.com/SihabSahariar/Multi-Threaded-YOLOv7-ONNX-With-OpenCV.git cd Multi-Threaded-YOLOv7-ONNX-With-OpenCV
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Install the required Python packages:
pip install -r requirements.txt
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Run the app
python app.py
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.