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15 dataset results for Unsupervised Anomaly Detection

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.

5,858 PAPERS • 49 BENCHMARKS

Fashion-MNIST

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.

2,114 PAPERS • 17 BENCHMARKS

STL-10 (Self-Taught Learning 10)

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.

698 PAPERS • 17 BENCHMARKS

Caltech-101

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.

389 PAPERS • 6 BENCHMARKS

MVTecAD (MVTEC ANOMALY DETECTION DATASET)

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.

140 PAPERS • 2 BENCHMARKS

Reuters-21578

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.

50 PAPERS • 3 BENCHMARKS

MIMII

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

24 PAPERS • NO BENCHMARKS YET

ToyADMOS

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).

10 PAPERS • NO BENCHMARKS YET

KolektorSDD (Kolektor Surface-Defect Dataset)

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.

9 PAPERS • 3 BENCHMARKS

UBI-Fights (Abnormal Event Detection Dataset)

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.

7 PAPERS • 2 BENCHMARKS

KolektorSDD2 (Kolektor Surface-Defect Dataset 2)

KolektorSDD2 is a surface-defect detection dataset with over 3000 images containing several types of defects, obtained while addressing a real-world industrial problem.

5 PAPERS • 3 BENCHMARKS

AnoShift (AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection)

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.

1 PAPER • 2 BENCHMARKS

Failure-Dataset-OpenStack

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.

1 PAPER • NO BENCHMARKS YET

MIAD

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.

1 PAPER • NO BENCHMARKS YET

Vehicle Claims

The code to create the dataset is available here. The dataset used in the paper is available on github

1 PAPER • 2 BENCHMARKS

Datasets

15 dataset results for Unsupervised Anomaly Detection