An Effective Approach for Image Anomaly Detection
Utilize Anomalib from Intel OpenVinoToolkit to benchmark, develop, and deploy deep learning based image anomaly detection
Detecting image anomalies is even more difficult than detecting anomalies in structured datasets or from time series data. It is partly because visual features are more difficult to capture than numerical features in structured datasets. That is where Deep Learning (DL) techniques come in, as deep learning models can perform auto-feature extraction for unstructured data like images.
To facilitate the task of doing image anomaly detection, Intel OpenVino has introduced Anomalib, which is a DL framework that provides state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. This framework provides many ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a collection of tools that accelerates the development and implementation of custom DL models. This framework has a strong focus on unsupervised image-based anomaly detection, where the objective is to identify outliers in images, or anomalous pixel regions within images in a dataset. This framework is well maintained by the developer (https://openvinotoolkit.github.io/anomalib/) and…
