Identify objects in images or videos
Detect objects in uploaded videos or live streams
Identify and track faces in videos
Detect objects in a video stream
Generate a video with stick figures tracking human poses
用于学习，验证识别效果
Detect objects in real-time from your webcam
Detect objects in real-time video streams
Detect objects in a video and image using YOLOv5.
Detect objects and track body movements in real-time
Analyze images and videos to identify objects
Analyze video for object detection and counting
Paligemma2 Detection with Supervision
YOLOv10 OBB is a state-of-the-art object detection model designed for identifying objects in images and videos. YOLO (You Only Look Once) is a popular architecture for real-time object detection, and the "OBB" refers to its capability to detect oriented bounding boxes, enabling the model to predict rotated bounding boxes for objects. This feature significantly improves accuracy for objects captured at angles or in non-axis-aligned orientations.
• Oriented Bounding Boxes: Detects objects with rotated bounding boxes for more accurate representation.
• High Accuracy: Delivers robust performance on benchmark datasets for object detection.
• Real-Time Detection: Optimized for fast inference, making it suitable for real-time applications.
• Video Tracking: Supports object tracking across frames in video streams.
• Multi-Object Detection: Capable of detecting and tracking multiple objects simultaneously.
• Speed Optimizations: Enhanced efficiency for deployment on edge devices or resource-constrained environments.
What is the main difference between YOLOv10 OBB and standard YOLO?
YOLOv10 OBB introduces oriented bounding boxes, which provide more accurate object detection for rotated objects, unlike the standard axis-aligned boxes.
Can YOLOv10 OBB handle video inputs?
Yes, YOLOv10 OBB supports video inputs and can track objects across frames, making it suitable for video analysis tasks.
How do I improve detection accuracy for specific objects?
You can fine-tune the model on a custom dataset containing your target objects or adjust the confidence threshold for better accuracy based on your use case.