Generic YOLO Models Trained on COCO
Identify benthic supercategories in images
Detect objects in images
Perform small object detection in images
Find objects in your images
Detect objects in images
Upload an image to detect objects
Track objects in live stream or uploaded videos
Detect objects in an image
Identify segments in an image using a Detectron2 model
Upload image to detect objects
Detect objects in an uploaded image
Identify objects in images using URLs or uploads
Yolos is a collection of generic YOLO (You Only Look Once) models trained on the COCO (Common Objects in Context) dataset. It is designed for object detection tasks, enabling users to identify and locate objects within images. Yolos is known for its high accuracy and real-time processing capabilities, making it a popular choice for applications requiring efficient object recognition.
• Real-time Object Detection: Yolos processes images quickly, detecting objects in real-time.
• COCO Dataset Compatibility: Trained on the COCO dataset, Yolos can detect 80 different object categories.
• Ease of Use: Pre-trained models simplify integration into various projects.
• Cross-Platform Support: Compatible with multiple platforms and frameworks.
• Open Source: Accessible for customization and improvement by the community.
What platforms does Yolos support?
Yolos is compatible with multiple platforms, including Windows, Linux, and macOS, and can be integrated into frameworks like TensorFlow, PyTorch, or Darknet.
How accurate is Yolos?
Yolos achieves high accuracy on the COCO dataset, with state-of-the-art performance for object detection tasks. Accuracy may vary based on the specific model version used.
Can Yolos be used for real-time applications?
Yes, Yolos is optimized for real-time object detection, making it suitable for applications like surveillance, autonomous vehicles, and live video analysis.