Yolov5g

What is Yolov5g ?

Yolov5g is an AI-powered object detection model designed to find and label objects within images. It belongs to the family of YOLO (You Only Look Once) models, known for their high efficiency and accuracy in real-time object detection tasks. Yolov5g is optimized for performance and ease of use, making it a popular choice for developers and researchers alike.

Features

• Real-Time Processing: Capable of detecting objects in real-time with high frame rates.
• Multiple Object Detection: Detects and labels multiple objects in a single image.
• Cross-Framework Compatibility: Works seamlessly with PyTorch, TensorFlow, and ONNX.
• Customizable: Allows users to train with custom datasets for specific use cases.
• Lightweight Architecture: Optimized for deployment on mobile and edge devices.
• Support for Various Input Sizes: Handles images of different resolutions and aspect ratios.

How to use Yolov5g ?

1. Clone the Repository: Start by cloning the Yolov5g repository from GitHub.
2. Install Dependencies: Install the required Python packages using pip install -r requirements.txt.
3. Run Detection: Use the detect.py script to run object detection on your images or video streams.
4. Specify Input: Provide the input source (image, video, or webcam) and optional parameters like confidence threshold.
5. View Results: The model will display or save the output with bounding boxes and class labels.

Frequently Asked Questions

What types of inputs does Yolov5g support?
Yolov5g supports images, videos, and webcam feeds. You can also process lists of images or video streams for batch detection.

Can I use Yolov5g for custom object detection?
Yes, Yolov5g can be trained on custom datasets. You need to prepare your dataset in the YOLO format and use the train.py script to fine-tune the model.

How do I improve detection speed?
To improve speed, use a GPU with CUDA support or optimize the model by reducing input resolution. You can also use a smaller model variant for faster inference.