Detect objects in an image
Detect objects in images and get details
Detect objects in images and highlight them
Identify objects in images and generate detailed data
Upload an image to detect objects
Identify labels in an image with a score threshold
Identify objects in your images using labels
Upload images to detect objects
Upload an image to detect objects
Detect objects in images and return coordinates
Detect objects in images
Find and highlight characters in images
Run object detection on videos
YOLOv10 is the latest iteration in the YOLO (You Only Look Once) series of real-time object detection models. It is designed to detect objects in images with high accuracy and speed, making it suitable for a wide range of applications such as surveillance, autonomous vehicles, and image analysis. YOLOv10 builds upon its predecessors by introducing improvements in architecture, efficiency, and performance, ensuring better detection capabilities while maintaining its signature speed.
• State-of-the-Art Detection: YOLOv10 offers improved object detection accuracy compared to previous versions. • Faster Inference Speeds: Optimized architecture enables real-time detection with reduced latency. • Efficient Architecture: Introduction of new backbone and neck networks for better feature extraction. • Multi-Scale Predictions: Enhanced handling of objects of varying sizes for more accurate detection. • Cross-Platform Support: Compatible with multiple platforms, including mobile and edge devices. • Open Source: Available for free use and modification, fostering community contributions. • Model Optimization: Includes techniques like quantization and knowledge distillation for lightweight deployment.
pip install -r requirements.txt
git clone https://github.com/ultralytics/yolov10.git
cd yolov10
python detect.py --source 0 # For webcam input
# Or
python detect.py --source image.jpg # For image input
python detect.py --weights yolov10n.pt --config yolov10n.yaml
What makes YOLOv10 better than previous versions?
YOLOv10 introduces architectural improvements such as enhanced backbone networks and better feature aggregation, leading to higher accuracy and faster inference speeds compared to earlier models.
Can YOLOv10 be used on mobile devices?
Yes, YOLOv10 supports mobile and edge deployment through optimized models and techniques like quantization, enabling it to run efficiently on resource-constrained devices.
Where can I find the YOLOv10 model and documentation?
The YOLOv10 model and its documentation are available on the official repository: GitHub - ultralytics/yolov10.