The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.


This datset contains 2000  images of size 256 X256. The dataset is created by captuirng photos using mobile phone. This dataset is applicable for two classes namely water and wet surface.


This dataset can be used for two classes such as water and wet surface.


The following datasets contains the results of an image analsyis conducted on 48 samples. The samples were prepared to study the effect of the printing strategy on the deposition on an Ag-nanoparticle ink on Kapton. The raster superposition, the splat superposition, the number of layers, and deposition strategy were used as process factors. The area of the printed pattern has been used as yield.


The 3DLSC-COVID datset  includes a total of  1,805 3D chest CT scans with more than 570,000 CT slices were collected from 2 standard CT scanners of Liyuan Hospital, i.e.,  UIH uCT 510 and GE Optima CT600.  Among all CT scans, there were 794 positive cases of COVID-19, which were further confirmed by clinical symptoms and RT-PCR from January 16 to April 16, 2020.


YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.



Database Structure

- YonseiStressImageDatabase

         - Subject Number (01~50)

                  - Data acquisition phase

                    (Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview)

                           - Data (*.npy, the filename is set to the time the data was acquired; YYYYMMDD_hhmmss_ms)


In the case 'Non-native_Language_Interview' data of subject 26, it was not acquired due to equipment problems.


Citing YonseiStressImageDatabase

If you use YonseiStressImageDatabase in a scientific publication, we would appreciate references to the following paper:

Now Reviewing.


Usage Policy

Copyright © 2019 AI Hub, Inc.,

AI data provided by AI Hub was built as part of a business National Information Society Agency's 'Intelligent information industry infrastructure construction project' in Korea, and the ownership of this database belongs to National Information Society Agency.

Specialized field AI data was built for artificial intelligence technology development and prototype production and can be used for research purposes in various fields such as intelligent services and chatbots.



Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, this dataset enables research on new optical spectrum anomaly detection schemes that exploit computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals.


The dataset contains a set of folders, each one representing one normal/anomalous case.

Within each folder, a number of .mat files contain the raw data collected from VPITransmissionMaker. The images folder contains the rendered constellation diagrams.

To render your own constellation diagrams, check the "generate_plots.m" file in the root folder.

More information on how to use in the GitHub repository.


Restored audio file using one-dimensional laser speckle image, which is voice of a male counting from zero to nine in English


Original voice file of a male counting from zero to nine in English


Given the difficulty to handle planetary data we provide downloadable files in PNG format from the missions Chang'E-3 and Chang'E-4. In addition to a set of scripts to do the conversion given a different PDS4 Dataset.


Please see Readme inside ZIP files for more information about the provided data and scripts. 


Animal recognition is an active research topic in recent years. Horse’s recognition is an important task in the world and  in  order  to  promote  horse’s  recognition  research,  the  Tunisian  Research  Groups  in  Intelligent  Machines  of University of Sfax (REGIM of Sfax) will provide the Tunisian Horses DataBase of Regim Lab’2015 (THoDBRL’2015) freely of charge to mainly horses’ face recognition researchers and to increase total of researches done to enhance animal recognition. This Database is used in [1].


Download Zip file and extract it.