Identify and label objects in images
Identify objects in your image
Detect objects in images and return details
Upload an image to detect objects
Identify objects in real-time video feed
Detect objects in anime images
Run object detection on videos
Identify objects in images
Detect objects in images
Identify objects in images
Detect objects in images using drag-and-drop
Detect traffic signs in uploaded images
Analyze images and videos to detect objects
Yolov5g is an advanced object detection model that builds upon the popular YOLO (You Only Look Once) series. It is designed for real-time object detection, offering high accuracy and fast processing speeds. Yolov5g is particularly noted for its balance between performance and computational efficiency, making it suitable for a wide range of applications.
• State-of-the-art performance: Yolov5g achieves impressive accuracy on benchmark datasets like COCO.
• Real-time processing: Optimized for fast inference, enabling real-time object detection in video streams and images.
• Support for multiple models: Compatible with various YOLOv5 architectures, allowing flexibility in model selection based on specific needs.
• Multi-scale prediction: Enhanced detection accuracy across objects of varying sizes.
• Ease of use: Simple integration with existing projects, ensuring developers can quickly deploy the model.
pip install -r requirements.txt to install all necessary packages.python detect.py --source [input_path] to process images or videos.wget https://github.com/ultralytics/YOLOv5/releases/download/v5.x.x/yolov5x6.pt.What makes Yolov5g different from other YOLO models?
Yolov5g is an extension of the YOLOv5 series, offering improved performance and additional features like multi-scale prediction without significant computational overhead.
Can Yolov5g be used for custom object detection?
Yes, Yolov5g supports custom training. Users can fine-tune the model using their own datasets for specific use cases.
Is Yolov5g compatible with all platforms?
Yolov5g is primarily designed for Python and PyTorch. While it can run on most platforms supporting PyTorch, optimized performance is typically seen on GPU-enabled systems.