95 datasets found

Geospatial datasets are diverse, naturally spatiotemporal, and inherently multimodal (composed of two or more distinct signal types or modalities) e.g., satellite/aerial imagery...

Anomaly detection in crowded scenes

The dataset is a collection of user messages, commands, time, and user information from the Twitch Plays Pokemon game.

This dataset is used for time-series anomaly detection. It contains multivariate time-series data from various sources, including industrial machines and sensors.

The KolektorSDD2 dataset is a dataset containing 3,335 color images of defective production items, with defects localized using pixel-wise masks.

The N-BaIoT dataset is a large dataset of network traffic traces from various IoT devices, including botnets.

The Spot-the-difference self-supervised pre-training for anomaly detection and segmentation dataset.

The CICIDS2017 dataset was collected 2 years after the UNSW NB-15 data. The collection took place for a week. On each day attacks such as: Brute Force FTP, Brute Force SSH, DoS,...

The SARAS dataset is a dataset for anomaly detection in robotic-assisted surgery. It contains 18 videos of radical prostatectomy procedures performed on artificial anatomies.

A dataset of synthetic data generated for gravitational wave detection, consisting of 400,000 noise samples and 64,000 signal samples.

Sewer-ML is a multi-label sewer defect classification dataset and benchmark.

Towards total recall in industrial anomaly detection.

The UEA archive is a collection of multivariate time series datasets where classification is of importance.

The UCR archive is a collection of univariate time series datasets where classification is of importance.

The proposed network is implemented in PyTorch using a single Tesla V100-DGXS-32GB GPU of an NVIDIA DGX station. For the ResNet-50 part, PyTorch default ImageNet pretrained...

LesionPaste is a one-shot anomaly detection framework for medical images that utilizes true anomalies from a single sample and synthesizes artificial anomalous samples.

The authors used 18 well-maintained business KPIs from a large Internet company. All KPIs have an interval of 1 minute between two observations.

The DAGM dataset is a dataset for unsupervised anomaly detection and localization.

The KDD99 dataset is an updated version of the DARPA98 dataset, processed by the tcpdump portion.

The MIRAI-RGU dataset includes normal traffic from Internet of the Things (IoT) Internet Protocol (IP) cameras and ten classes of malicious traffic from the Mirai botnet malware,...