YOLOv10

Real-time end-to-end object detection model

CommonProductImageObject DetectionReal-time Performance

YOLOv10 is a next-generation object detection model that achieves high accuracy while maintaining real-time performance. Through optimized post-processing and model architecture, it reduces computational redundancy, improving efficiency and performance. YOLOv10 achieves state-of-the-art performance and efficiency across different model scales. For example, YOLOv10-S achieves 1.8x speed improvement compared to RT-DETR-R18 at similar AP, while reducing the number of parameters and FLOPs by 2.8x.

Unmatched dual assignment without non-maximum suppression (NMS)
achieving competitive performance and low inference latency.
Fully optimized YOLO components
significantly reducing computational cost and enhancing both efficiency and accuracy.
YOLOv10-S
M
B
L
and X models of different scales demonstrate exceptional performance on the COCO dataset.
Supports input images of various resolutions

YOLOv10 is primarily targeted towards researchers and developers in the computer vision field
especially those working on applications requiring real-time object detection in scenarios such as video surveillance
autonomous driving
and industrial automation. Its high efficiency and accuracy make it an ideal choice for these domains.

Real-time detection of abnormal behaviors in a video surveillance system.
Real-time identification of pedestrians and vehicles in autonomous driving vehicles.
Automatic detection of product quality issues on industrial production lines.

1. Install a Python environment and the required dependency libraries.
2. Clone the YOLOv10 GitHub repository to your local machine.
3. Download pre-trained models or trained checkpoints.
4. Prepare the images or video data to be detected.
5. Run the model for object detection and retrieve the detection results.

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