Defect detection with computer vision
Integrating Anomalib with MVTec AD and PaDiM for computer vision anomaly detection
Computer vision anomaly detection plays a vital role in quality control across various industries, enabling early identification of process defects and inconsistencies. In this template, we show a project that uses the PaDiM (Patch Distribution Modeling) model to detect anomalies in the MVTecAD (MVTec Anomaly Detection) dataset. We leverage Anomalib, a comprehensive deep-learning library designed for state-of-the-art anomaly detection algorithms, to bridge the gap between the MVTec AD dataset and the PaDiM model.
Why PaDiM, MVTechAD, and Anomalib?
- PaDiM: PaDiM, which stands for Patch Distribution Modeling, is a deep learning based model designed specifically for anomaly detection in images. Unlike traditional approaches, PaDiM leverages the power of deep learning to automatically learn and model the distribution of patches within an image. PaDiM divides the input image into smaller patches, allowing the model to focus on local patterns and details. For each patch, the model extracts features using a pre-trained convolutional neural network (CNN). These features capture the underlying structure and characteristics of the image patches. PaDiM models the distribution of these features for normal images. During inference, the model compares the feature distribution of the test image patches against the learned distribution. Significant deviations indicate anomalies.The model assigns an anomaly score to each patch based on the degree of deviation from the normal distribution. These scores are aggregated to provide an overall anomaly score for the entire image.
- MVTech AD: A comprehensive benchmark dataset specifically designed for industrial anomaly detection tasks. Released by MVTec Software GmbH, it provides a variety of high-resolution images across different categories, each depicting either a normal or anomalous condition. This dataset is widely used for evaluating the performance of anomaly detection algorithms due to its diversity and realistic depiction of industrial scenarios. The dataset includes 15 different object and texture categories, such as bottles, hazelnuts, leather, metal, and wood. Each category contains numerous images of both normal and defective items. Anomalies include various types of defects like scratches, dents, and contaminations.Detailed annotations are provided, marking the exact locations and types of defects in the images. This allows for precise evaluation of detection models.
- Anomalib: Anomalib is an open-source library specifically designed to serve as a hub for state-of-the-art anomaly detection algorithms. It offers a range of pre-implemented models, datasets, and utilities that make it easier for researchers and practitioners to build, train, and deploy anomaly detection systems. Anomalib abstracts away many of the complexities associated with setting up and configuring anomaly detection models, allowing users to focus on their specific application needs. Anomalib functions directly with the MVTecAD dataset.
This project demonstrates how to develop and deploy an anomaly detection system using the PaDiM model and Anomalib library. By following the steps outlined in the Jupyter notebook, users can acquire and preprocess the MVTecAD dataset, configure and train the PaDiM model, and deploy the trained model as an API for real-time anomaly detection. Learn, with Domino, how to quickly configure and initiate training without needing to implement the model architecture from scratch. Find value quicker for use cases related to computer vision!