Yolov5g

What is Yolov5g ?

Yolov5g is an object detection model designed to find and label objects in images efficiently. It is a variant of the YOLOv5 family, optimized for performance and accuracy in real-world applications. Yolov5g is widely used for tasks like surveillance, autonomous systems, and image analysis due to its speed and reliability.

Features

• Real-time detection: Process images in milliseconds for quick object identification.
• Multiple object detection: Detect and label multiple objects in a single image.
• High accuracy and speed balance: Optimized for both accuracy and performance, making it suitable for resource-constrained environments.
• Customizable: Choose from different model sizes (small, medium, or larger) based on your specific needs.
• Versatile input support: Works with images and video streams.
• Flowchart-friendly: Easy integration into workflows for automated image processing.
• Cross-platform compatibility: Runs seamlessly on Windows, macOS, and Linux systems.

How to use Yolov5g ?

1. Install the requirements: Clone the repository and install the necessary dependencies using pip install -r requirements.txt.
2. Download the model weights: Select the appropriate model (e.g., small, medium) for your use case.
3. Run detection: Use the command python detect.py --source [image/path] for images or python detect.py --source [video/path] for video streams.
4. View results: The detected objects will be labeled and displayed in the output folder.
5. Custom training: Optionally, train the model on your dataset by modifying the configuration and running python train.py.

Frequently Asked Questions

What is the difference between Yolov5g and Yolov5?
Yolov5g is an optimized version of Yolov5, offering improved performance and better balance between accuracy and speed.

How do I install Yolov5g?
Clone the repository and install the dependencies using the provided requirements.txt file. Ensure you have Python and PyTorch installed.

Can I use Yolov5g for real-time video analysis?
Yes, Yolov5g supports real-time video analysis. Use the --source parameter with a video file or camera input for live detection.