Identify objects in images with YOLOS model
Detect marine vessels in images
Detect objects in images and get bounding boxes
Upload images/videos to detect wildfires and smoke
Upload image to detect objects
Detect traffic signs in images
Detect objects in images
Identify objects in images with Transformers.js
Identify car damage in images
Identify and label objects in images
Detect objects in an image
Count objects in an image by drawing a region of interest
Upload image to detect objects
YOLOS (You Only Look Once) Object Detection is a state-of-the-art, real-time object detection system based on the YOLO family of models. It is designed to accurately identify and locate objects within images and videos by using a single neural network to predict bounding boxes and class probabilities. YOLOS is known for its high performance, efficiency, and ease of use, making it suitable for a wide range of applications.
• Real-Time Processing: YOLOS enables fast object detection, making it ideal for applications requiring quick responses.
• High Accuracy: The model delivers precise object recognition and location detection.
• Multi-Object Detection: It can identify multiple objects in a single image or frame.
• Customizable: Users can train and fine-tune the model for specific use cases.
• Cross-Platform Support: Compatible with various platforms and frameworks.
Example command:
python detect.py --source your_image.jpg --weights yolos.pt
What is YOLOS used for?
YOLOS is used for object detection tasks, such as identifying people, vehicles, animals, and other objects in images and videos.
How accurate is YOLOS?
YOLOS achieves high accuracy, often comparable to state-of-the-art models, though performance depends on the specific dataset and configuration.
Can I customize YOLOS for my application?
Yes, YOLOS can be fine-tuned using your own dataset to improve performance on specific tasks.
How do I improve detection accuracy?
You can improve accuracy by training YOLOS on more data, optimizing hyperparameters, or using more advanced variants like YOLOSv5 or YOLOSx.