1. Tutorial Introduction

YOLOv13 is an object detection model proposed in June 2025 by a joint research team from Tsinghua University, Taiyuan University of Technology, Xi'an Jiaotong University, and other universities. Building upon the advantages of real-time detection from the YOLO series, this model introduces a series of new mechanisms, including hypergraph enhancement, higher-order semantic modeling, and lightweight structure reconstruction. It achieves comprehensive leadership on mainstream datasets such as MS COCO and Pascal VOC, demonstrating stronger generalization ability and practical deployment. The related paper is titled "YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception".

This tutorial uses a single RTX 5090 card as the resource.

2. Project Examples

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3. Operation steps

1. After starting the container, click the API address to enter the Web interface

2. Usage steps

If "Bad Gateway" is displayed, it means the model is initializing. Since the model is large, please wait about 2-3 minutes and refresh the page.

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Parameter Description

• Models: yolov13n.pt (nano), yolov13s.pt (small), yolov13l.pt (large), yolov13x.pt (extra large). Larger models generally have higher accuracy (mAP), but also higher parameter count, computational cost (FLOPs), and longer inference time.
• Confidence Threshold: Confidence threshold.
• IoU Threshold: Intersection over Union (IoU) threshold, used in NMS.
• Max detections per image: The maximum number of detection boxes per image.

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Citation Information

The citation information for this project is as follows:

@article{yolov13,
  title={YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception},
  author={Lei, Mengqi and Li, Siqi and Wu, Yihong and et al.},
  journal={arXiv preprint arXiv:2506.17733},
  year={2025}
}