The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png

Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST. Source: Generative Probabilistic Novelty Detection with Adversarial Autoencoders Image Source: https://github.com/zalandoresearch/fashion-mnist

The STL-10 is an image dataset derived from ImageNet and popularly used to evaluate algorithms of unsupervised feature learning or self-taught learning. Besides 100,000 unlabeled images, it contains 13,000 labeled images from 10 object classes (such as birds, cats, trucks), among which 5,000 images are partitioned for training while the remaining 8,000 images for testing. All the images are color images with 96×96 pixels in size. Source: Unsupervised Feature Learning with C-SVDDNet Image Source: https://cs.stanford.edu/~acoates/stl10/

The Caltech101 dataset contains images from 101 object categories (e.g., “helicopter”, “elephant” and “chair” etc.) and a background category that contains the images not from the 101 object categories. For each object category, there are about 40 to 800 images, while most classes have about 50 images. The resolution of the image is roughly about 300×200 pixels. Source: Simple and Efficient Learning using Privileged Information

MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects. There are two common metrics: Detection AUROC and Segmentation (or pixelwise) AUROC Detection (or, classification) methods output single float (anomaly score) per input test image. Segmentation methods output anomaly probability for each pixel. "To assess segmentation performance, we evaluate the relative per-region overlap of the segmentation with the ground truth. To get an additional performance measure that is independent of the determined threshold, we compute the area under the receiver operating characteristic curve (ROC AUC). We define the true positive rate as the percentage of pixels that were correctly classified as anomalous" [1] Later segmentation metric was improved to balance regions with small and large area, see PRO-AUC and other in [2] Source: MVTEC ANOMALY DETECTION DATASET Image Source: https://www.mvtec.com/company/research/datasets/mvtec-ad/ [1] Paul Bergmann et al, "MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection" [2] Bergmann, P., Batzner, K., Fauser, M. et al. The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. Int J Comput Vis (2021). https://doi.org/10.1007/s11263-020-01400-4

Soil Moisture Active Passive (SMAP) dataset is a dataset of soil samples and telemetry information using the Mars rover by NASA. Originally published in https://arxiv.org/abs/1802.04431 and used for the unsupervised anomaly detection task in time series data. Later it was used in many popular anomaly detection methods and benchmarks that distribute it in their repositories e.g., https://github.com/OpsPAI/MTAD

The Reuters-21578 dataset is a collection of documents with news articles. The original corpus has 10,369 documents and a vocabulary of 29,930 words. Source: Topic Model Based Multi-Label Classification from the Crowd

Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII) is a sound dataset of industrial machine sounds. Source: MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

MVTec Logical Constraints Anomaly Detection (MVTec LOCO AD) dataset is intended for the evaluation of unsupervised anomaly localization algorithms. The dataset includes both structural and logical anomalies. It contains 3644 images from five different categories inspired by real-world industrial inspection scenarios. Structural anomalies appear as scratches, dents, or contaminations in the manufactured products. Logical anomalies violate underlying constraints, e.g., a permissible object being present in an invalid location or a required object not being present at all. The dataset also includes pixel-precise ground truth data for each anomalous region. Source: THE MVTEC LOGICAL CONSTRAINTS ANOMALY DETECTION DATASET (MVTEC LOCO AD)

The dataset is constructed from images of defective production items that were provided and annotated by Kolektor Group d.o.o.. The images were captured in a controlled industrial environment in a real-world case. The dataset consists of 399 images at 500 x ~1250 px in size. Please cite our paper published in the Journal of Intelligent Manufacturing when using this dataset: @article{Tabernik2019JIM, author = {Tabernik, Domen and {\v{S}}ela, Samo and Skvar{\v{c}}, Jure and Sko{\v{c}}aj, Danijel}, journal = {Journal of Intelligent Manufacturing}, title = {{Segmentation-Based Deep-Learning Approach for Surface-Defect Detection}}, year = {2019}, month = {May}, day = {15}, issn={1572-8145}, doi={10.1007/s10845-019-01476-x} }

KolektorSDD2 is a surface-defect detection dataset with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem. The dataset consists of: * 356 images with visible defects * 2979 images without any defect * image sizes of approximately 230 x 630 pixels * train set with 246 positive and 2085 negative images * test set with 110 positive and 894 negative images * several different types of defects (scratches, minor spots, surface imperfections, etc.)

ToyADMOS dataset is a machine operating sounds dataset of approximately 540 hours of normal machine operating sounds and over 12,000 samples of anomalous sounds collected with four microphones at a 48kHz sampling rate, prepared by Yuma Koizumi and members in NTT Media Intelligence Laboratories. The ToyADMOS dataset is designed for anomaly detection in machine operating sounds (ADMOS) research. It is designed for three tasks of ADMOS: product inspection (toy car), fault diagnosis for fixed machine (toy conveyor), and fault diagnosis for moving machine (toy train). Source: https://github.com/YumaKoizumi/ToyADMOS-dataset

Unlike previous datasets that focus on detecting the diversity of defect categories (like MVTec AD and VisA), AeBAD is centered on the diversity of domains within the same data category. The aim of AeBAD is to automatically detect abnormalities in the blades of aero-engines, ensuring their stable operation. AeBAD consists of two sub-datasets: the single-blade dataset (AeBAD-S) and the video anomaly detection of blades (AeBAD-V). AeBAD-S comprises images of single blades of different scales, with a primary feature being that the samples are not aligned. Furthermore, there is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. AeBAD-V, on the other hand, includes videos of blades assembled on the blisks of aero-engines, with the aim of detecting blade anomalies during blisk rotation. A distinctive feature of AeBAD-V is that the shooting view in the test set differs from that in the training set.

a dataset of time-series anomaly detection

CHAD: Charlotte Anomaly Dataset CHAD is high-resolution, multi-camera dataset for surveillance video anomaly detection. It includes bounding box, Re-ID, and pose annotations, as well as frame-level anomaly labels, dividing all frames into two groups of anomalous or normal. You can find the paper with all the details in the following link: CHAD: Charlotte Anomaly Dataset. Please refer to the page of the dataset for more information.

UBI-Fights - Concerning a specific anomaly detection and still providing a wide diversity in fighting scenarios, the UBI-Fights dataset is a unique new large-scale dataset of 80 hours of video fully annotated at the frame level. Consisting of 1000 videos, where 216 videos contain a fight event, and 784 are normal daily life situations. All unnecessary video segments (e.g., video introductions, news, etc.) that could disturb the learning process were removed.

AnoShift is a large-scale anomaly detection benchmark, which focuses on splitting the test data based on its temporal distance to the training set, introducing three testing splits: IID, NEAR, and FAR. This testing scenario proves to capture the in-time performance degradation of anomaly detection methods for classical to masked language models. AnoShift benchmark aims to enable a better estimate of the anomaly detection model’s performance, under natural distribution shifts that occur over time in the input, closer to the real-world performance, leading to more robust anomaly detection algorithms. The benchmark is based on the Kyoto-2016 dataset (https://www.takakura.com/Kyoto_data/).

This is a synthetic dataset for defect detection on textured surfaces. It was originally created for a competition at the 2007 symposium of the DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., the German chapter of the International Association for Pattern Recognition). The competition was hosted together with the GNSS (German Chapter of the European Neural Network Society). After the competition, the dataset has been used as a test dataset in multiple projects and research papers. It is publicly available from the University of Heidelberg website (Heidelberg Collaboratory for Image Processing). The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour.

The original dataset for "ECG5000" is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record "chf07". It was originally published in "Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23)". The data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. This dataset was originally used in paper "A general framework for never-ending learning from time series streams", DAMI 29(6). After that, 5,000 heartbeats were randomly selected. The patient has severe congestive heart failure and the class values were obtained by automated annotation

TIMo (Time-of-Flight Indoor Monitoring) is a dataset of infrared and depth videos intended for the use in Anomaly Detection and Person Detection/People Counting. It features more than 1,500 sequences for anomaly detection, which sum up to more than 500,000 individual frames. For person detection the dataset contains more than than 240 sequences. The data was captured using a Microsoft Azure Kinect RGB-D camera. In addition, we provide annotations of anomalous frame ranges for use with anomaly detection and bounding boxes and segmentation masks for use with person detection. The data was captured in parts from a tilted view and a top-down perspective.

This failure dataset contains information on the events collected in the OpenStack cloud computing platform during three different campaigns of fault-injection experiments performed with three different workloads.

MIAD contains more than 100K high-resolution color images in various outdoor industrial scenarios, designed for unsupervised anomaly detection. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. Source: MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection Image Source: https://arxiv.org/pdf/2211.13968v2.pdf

Multi-pose Anomaly Detection (MAD) dataset, which represents the first attempt to evaluate the performance of pose-agnostic anomaly detection. The MAD dataset containing 4,000+ highresolution multi-pose views RGB images with camera/pose information of 20 shape-complexed LEGO animal toys for training, as well as 7,000+ simulation and real-world collected RGB images (without camera/pose information) with pixel-precise ground truth annotations for three types of anomalies in test sets. Note that MAD has been further divided into MAD-Sim and MAD-Real for simulation-to-reality studies to bridge the gap between academic research and the demands of industrial manufacturing.

The code to create the dataset is available here. The dataset used in the paper is available on github - Maker - Categorical - The brand of the vehicle. - GenModel - Categorical - The model of the vehicle. - Color - Categorical - Colour of the vehicle. - Reg_Year - Categorical - Year of Registration. - Body_Type - Categorical - Eg. SUV, Convertible. - Runned_Miles - Numerical - Distance covered by the vehicle. - Engin_Size - Categorical - Size of engine. - GearBox - Categorical - Automatic, Manual. - FuelType - Categorical - Petrol, Diesel. - Price - Numerical - Price of vehicle. - Seat_num - Numerical - Number of seats. - Door_num - Numerical - Number of Doors. - issue - Categorical - Type of damage. - issue_id - Categorical - Specific damage. - repair_complexity - Categorical - Difficulty to repair the vehicle. - repair_hours - Numerical - Time required to finish the job. - repair_cost - Numerical - Cost of repair. Other attributes are not used for evaluation in this work. breakdown_date and repair_date were added with the idea of inserting anomalies based on the number of days required to repair the vehicle.

The ISP-AD Dataset is a large-scale anomaly detection dataset, representing a real-world industrial use case. It contains 312,674 fault-free and 246,375 defective samples, including 245,664 synthetic defects and 711 real defects collected on the factory floor. Designed to advance research in unsupervised, self-supervised, and supervised anomaly detection, ISP-AD serves as a benchmark for evaluating defect detection methods under realistic industrial conditions.

This dataset aims to provide a color dataset with real industrial fabric defect gathered in a visiting machine with several industrial cameras. It has been designed with the same nomenclature as MVTEC AD dataset

The PRONTO heterogeneous benchmark dataset is based on an industrial-scale multiphase flow facility. It includes data from heterogeneous sources, including process measurements, alarm records, high frequency ultrasonic flow and pressure measurements, an operation log and video recordings. The study collected data from various operational conditions with and without induced faults to generate a multi-rate, multi-modal dataset. The dataset is suitable for developing and validating algorithms for fault detection and diagnosis (FDD) and data fusion.

The SPOTS-10 dataset is an extensive collection of grayscale images showcasing diverse patterns found in ten animal species. Specifically, SPOTS-10 contains 50,000 32 x 32 grayscale images, divided into ten categories, with 5,000 images per category. The training set comprises 40,000 images, while the test set contains 10,000 images. The SPOTS-10 dataset is freely available on the project GitHub page: https://github.com/amotica/spots-10 by cloning the repository.