Xiaohao-Xu/Customizable-VLM

[CSCWD 2025, Best Student Paper] Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

29
/ 100
Experimental

This project helps quality control engineers and manufacturing line managers automatically spot defects in products using visual and textual descriptions. You provide images of items, along with any relevant contextual information or historical data, and it identifies unusual patterns or anomalies, flagging potential defects or errors. It's designed for professionals overseeing production lines or performing detailed product inspections.

No commits in the last 6 months.

Use this if you need to automate the detection of unusual or faulty items on a production line using both visual inspection and descriptive text.

Not ideal if you're looking for a simple, out-of-the-box solution without any programming or API key setup.

quality-control manufacturing-inspection defect-detection visual-inspection production-monitoring
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 4 / 25

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Stars

29

Forks

1

Language

Python

License

MIT

Last pushed

May 24, 2025

Commits (30d)

0

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