PaDiM Anomaly Detection Tool

PaDiM anomaly detection with a Qt5 GUI.

pytorch   matplotlib   pillow   ·   anomaly detection   machine learning   study

Here’s a project description draft for your PaDiM Anomaly Detection tool using Qt5:

Overview: Introducing the PaDiM Anomaly Detection Tool, a powerful software application designed to identify anomalies in visual data using advanced machine learning techniques. Built with Qt5 for a smooth and intuitive user experience, this tool provides real-time anomaly detection, making it ideal for quality control, surveillance, and industrial monitoring. PaDiM (Patch Distribution Modeling) is a state-of-the-art method for unsupervised anomaly detection, allowing it to perform effectively even without a labeled dataset of anomalies.

Demo of the PaDiM Anomaly Detection Tool: from launch to real-time anomaly detection, showcasing intuitive Qt5 interface and visual highlights.

🚀 How It Works

The PaDiM Anomaly Detection Tool utilizes the PaDiM algorithm, which leverages pre-trained convolutional neural networks to model feature distributions in normal data patches. By comparing each patch of a new input image to this baseline, the tool identifies regions with unusual patterns, highlighting anomalies in real-time.

Here’s a breakdown of the core functionality:

  • Feature Extraction: Extracts relevant features from input images using a pre-trained CNN, providing a foundation for anomaly detection.

  • Distribution Modeling: Models the patch distribution for typical (non-anomalous) data, establishing a standard for comparison.

  • Real-Time Anomaly Detection: Identifies deviations in new data against the normal distribution model, flagging anomalous regions in the image.

🧩 Key Features

  1. Interactive GUI: Built with Qt5, the user interface is both visually appealing and highly responsive, allowing users to navigate and manage anomaly detection tasks seamlessly.

  2. Real-Time Processing: Provides fast, real-time anomaly detection for immediate feedback on newly processed images, suitable for high-stakes monitoring applications.

  3. Visual Anomaly Highlights: Clearly marks detected anomalies within images, providing an intuitive visual reference for detected irregularities.

💡 Applications

Ideal for sectors such as manufacturing and medical imaging, the PaDiM Anomaly Detection Tool offers a highly effective solution for any setting requiring robust anomaly detection without the need for labeled anomaly datasets.

💻 Check out the code here: GitHub Repository