Omkar2118/datasets huggingface dataset:
VisA
Full name: Visual Anomaly Dataset

The VisA dataset contains 12 subsets corresponding to 12 different objects as shown in the above figure. There are 10,821 images with 9,621 normal and 1,200 anomalous samples. Four subsets are different types of printed circuit boards (PCB) with relatively complex structures containing transistors, capacitors, chips, etc. For the case of multiple instances in a view, we collect four subsets: Capsules, Candles, Macaroni1 and Macaroni2. Instances in Capsules and Macaroni2 largely differ in locations and poses. Moreover, we collect four subsets including Cashew, Chewing gum, Fryum and Pipe fryum, where objects are roughly aligned. The anomalous images contain various flaws, including surface defects such as scratches, dents, color spots or crack, and structural defects like misplacement or missing parts.

Introducing paper: SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
Uncertainty and Concept Drift
Full name: On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence

AI-based digital twins are at the leading edge of theIndustry 4.0 revolution, which are technologically empowered bythe Internet of Things and real-time data analysis. Information
collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to
non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of thechange (drift) is crucial for updating the model’s knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model’s confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this manuscript is to confirm and expose solidly the connection between the model’s confidence in its output and the presence of a concept drift, showcasing it experimentally and advocating for a major consideration of uncertainty estimation in comparative studies to be reported in the future.

Introducing paper: On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence