denguir/student-teacher-anomaly-detection
Student–Teacher Anomaly Detection with Discriminative Latent Embeddings
This project helps quality control engineers automatically identify defects or anomalies in industrial products by analyzing images. You provide a dataset of normal, anomaly-free product images, and it outputs anomaly maps that highlight defective regions in new images. It's used by manufacturing and quality assurance professionals to detect flaws on a production line.
185 stars. No commits in the last 6 months.
Use this if you need to automatically spot surface defects or manufacturing anomalies on products using computer vision, especially when you primarily have images of 'good' products.
Not ideal if your anomalies are not visually detectable or if you lack a sufficient dataset of normal, defect-free images for training.
Stars
185
Forks
46
Language
Python
License
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Category
Last pushed
Nov 25, 2024
Commits (30d)
0
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