dennishnf/unsupervised-anomaly-detection
This repository describes the implementation of an unsupervised anomaly detector using the Anomalib library.
This tool helps quality control inspectors identify defective items on a production line by analyzing images. You provide a set of images of 'normal', defect-free products, and it learns to spot unusual patterns. The output is an indication of which new products deviate from the norm, helping you catch manufacturing flaws quickly. It's designed for someone managing or working in industrial quality assurance.
No commits in the last 6 months.
Use this if you need to automatically detect visual anomalies or defects in manufactured goods, particularly on items like metal parts, without manually setting rules for what constitutes a 'defect'.
Not ideal if your anomalies are subtle, non-visual, or if you have very little data for what 'normal' looks like.
Stars
28
Forks
7
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 06, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/dennishnf/unsupervised-anomaly-detection"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
elki-project/elki
ELKI Data Mining Toolkit
raphaelvallat/antropy
AntroPy: entropy and complexity of (EEG) time-series in Python
Minqi824/ADBench
Official Implement of "ADBench: Anomaly Detection Benchmark", NeurIPS 2022.