megvii-research/ML-GCN

Health v2 · maintenance only

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1446 stars · 346 forks observed · 2026-08-28

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

A PyTorch implementation of ML-GCN, the CVPR 2019 paper 'Multi-Label Image Recognition with Graph Convolutional Networks'. It provides training and evaluation scripts with pretrained checkpoints for COCO, NUS-WIDE, and VOC 2007 multi-label image classification.

Use cases

• classify images with multiple labels
• reproduce ML-GCN results from CVPR 2019
• train a multi-label image recognition model on COCO or VOC
• use graph convolutional networks for label dependency modeling
• evaluate pretrained multi-label classification checkpoints
• build a research baseline for multi-label recognition

When to choose

• you need a reference implementation of ML-GCN for research or paper reproduction
• you want pretrained multi-label classification models for COCO or VOC 2007
• you are studying how GCNs can model label correlations in image recognition

When to avoid

• you need a production-ready, actively maintained image classification system
• you require a permissive license - the repository has no license
• you need modern PyTorch versions - the code targets torch 0.3.1
• you need general single-label image classification

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision computer-vision image-processing machine-learning deep-learning python pytorch multi-label-classification graph-convolutional-networks gcn cvpr-2019 research-code image-recognition

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