iMoonLab/Hyper-YOLOv1.1

The source code of IEEE TPAMI 2025 "Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation".

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Emerging

This project offers an advanced object detection system that helps computer vision engineers more accurately identify and locate multiple objects within images. It takes raw image datasets and outputs bounding boxes and labels for detected objects, improving upon existing YOLO models. This is ideal for machine learning engineers, computer vision researchers, and AI developers working on visual recognition tasks.

119 stars. No commits in the last 6 months.

Use this if you need to perform state-of-the-art object detection and want to leverage recent advancements in hypergraph computation for improved accuracy in your visual AI applications.

Not ideal if you are looking for a simple, off-the-shelf object detection tool without the need for model training or if you lack experience with deep learning frameworks and data preparation for computer vision.

Object Detection Computer Vision Deep Learning Image Analysis Visual AI
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 16 / 25

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Stars

119

Forks

17

Language

Python

License

GPL-3.0

Last pushed

Dec 16, 2024

Commits (30d)

0

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