YOLO Object Detection V9 (with pre-trained models New V9)

Overview

This repository implements YOLO (You Only Look Once) object detection algorithm tailored for video analysis. YOLO is a state-of-the-art, real-time object detection system that can detect multiple objects in an image or frame in one pass.

Features

• Video Object Detection: Detect objects in videos using the YOLO algorithm.
• Real-Time Processing: Fast and efficient processing for real-time or near real-time applications.
• Pre-trained Models: Support for multiple pre-trained YOLO models for various object detection tasks.
• Easy Integration: Simple interface for integrating YOLO into video processing pipelines.

Installation

1. Clone the repository:

   git clone https://github.com/anjanbudige/yolo-object-detection.git

2. Install dependencies:

   use colab or anaconda to open python notebook file (pynb file)

   git clone https://github.com/WongKinYiu/yolov9
   cd yolov9
   pip install -r requirements.txt

3. Download pre-trained YOLO weights and configuration files. Below we have given pretrained models, you can use them for object detection

Usage

To perform object detection on a video, follow these steps:

1. Ensure dependencies are installed and pre-trained model files are downloaded.

2. Run the detection script:

   python detect_video.py --weights "model.pt file" --conf "0.1 to 1.0" --source path/to/input/video.mp4 --device cpu

   • Replace path/to/input/video.mp4 with the path to your input video file.
   • Provide paths to YOLO weights, configuration, and class files.

3. Adjust parameters as needed.

Pre-trained Models

This repository includes links to pre-trained YOLO weights and configuration files for various YOLO versions:

• YOLOV9 c Converted: Download
• YOLOV9 e Converted: Download
• YOLOV9 c: Download
• YOLOV9 e: Download
• Gelan c: Download
• Gelan e: Download

Contributing

Contributions are welcome! If you have any ideas, enhancements, or bug fixes, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.