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docs/en/models/yolov10.md
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@ -6,7 +6,7 @@ keywords: YOLOv10, real-time object detection, NMS-free, deep learning, Tsinghua
# YOLOv10: Real-Time End-to-End Object Detection
YOLOv10, built on the [Ultralytics](https://ultralytics.com) [Python package](https://pypi.org/project/ultralytics/) by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
YOLOv10, built on the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/) by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
![YOLOv10 consistent dual assignment for NMS-free training](https://github.com/ultralytics/docs/releases/download/0/yolov10-consistent-dual-assignment.avif)
@ -223,7 +223,7 @@ YOLOv10 sets a new standard in real-time object detection by addressing the shor
## Citations and Acknowledgements
We would like to acknowledge the YOLOv10 authors from [Tsinghua University](https://www.tsinghua.edu.cn/en/) for their extensive research and significant contributions to the [Ultralytics](https://ultralytics.com) framework:
We would like to acknowledge the YOLOv10 authors from [Tsinghua University](https://www.tsinghua.edu.cn/en/) for their extensive research and significant contributions to the [Ultralytics](https://www.ultralytics.com/) framework:
!!! Quote ""