iMoonLab/yolov13

Health v2 · maintenance only

Adoption not part of the score

1702 stars · 177 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of YOLOv13, a real-time object detection model family (Nano to X-Large) featuring Hypergraph-based Adaptive Correlation Enhancement (HyperACE). It supports training, validation, prediction, and model export for deployment on edge and mobile platforms.

Use cases

• train a custom YOLOv13 object detection model on my own dataset
• run real-time object detection on images and video
• export YOLOv13 weights to ONNX, RKNN, or Ascend OM for edge deployment
• benchmark YOLOv13 against other YOLO versions on MS COCO
• deploy object detection on Android with ncnn
• serve detections via a FastAPI REST API

When to choose

• you need state-of-the-art real-time object detection with high-order feature correlation modeling
• you want official weights, training code, and export tooling for the YOLOv13 paper
• you are deploying detection models to edge devices like Rockchip NPUs or Android

When to avoid

• you need a permissively licensed model for commercial closed-source products (AGPL-3.0)
• you need a long-established, battle-tested detector like YOLOv8 instead of a newly released architecture
• your project does not involve object detection

Facets

library · maturity active

machine-learning computer-vision image-processing computer-vision deep-learning machine-learning python windows yolo object-detection hypergraph-learning real-time-inference pytorch model-training model-export gpu linux macos

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