This project is a small yet powerful command-line interface (CLI) application designed for AI-based anomaly detection, specifically tailored for industrial use. It leverages autoencoders to identify outliers by training exclusively on images of "good" samples.
The application trains a model on the provided images of normal (good) samples, learning their key features and patterns. When supplied with new sample images, the application reconstructs each image using the trained deep learning model and calculates the difference between the original and reconstructed images. Based on this difference, it classifies the sample as either "good" or an "outlier.
Good for quality control in manufacturing and similar industries.
Automatically generates reports that include tables and visualizations of the detected anomalies.
Offers training metrics and detection statistics for performance evaluation.
User-friendly CLI ensures ease of integration into existing workflows.