Kitti Trencsenyi

These datasets are applied for machine learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.

Image data

These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.

Facial recognition

In computer vision, face images have been used extensively to develop facial recognition systems, face detection, and many other projects that use images of faces.

Action recognition

Object detection and recognition

Handwriting and character recognition

Aerial images

Other images

Text data

These datasets consist primarily of text for tasks such as natural language processing, sentiment analysis, translation, and cluster analysis.

Reviews

News articles

Messages

Twitter and tweets

Dialogues

Other text

Sound data

These datasets consist of sounds and sound features used for tasks such as speech recognition and speech synthesis.

Speech

Music

Other sounds

Signal data

Datasets containing electric signal information requiring some sort of signal processing for further analysis.

Electrical

Motion-tracking

Other signals

Physical data

Datasets from physical systems.

High-energy physics

Systems

Astronomy

Earth science

Other physical

Biological data

Datasets from biological systems.

Human

Animal

Fungi

Plant

Microbe

Drug Discovery

Anomaly data

Question Answering data

This section includes datasets that deals with structured data.

Multivariate data

Datasets consisting of rows of observations and columns of attributes characterizing those observations. Typically used for regression analysis or classification but other types of algorithms can also be used. This section includes datasets that do not fit in the above categories.

Financial

Weather

Census

Transit

Internet

Games

Other multivariate

Curated repositories of datasets

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

* OpenML: Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms.
* PMLB: A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms. Provides classification and regression datasets in a standardized format that are accessible through a Python API.
*Metatext NLP: https://metatext.io/datasets web repository maintained by community, containing nearly 1000 benchmark datasets, and counting. Provides many tasks from classification to QA, and various languages from English, Portuguese to Arabic.
*Appen: Off The Shelf and Open Source Datasets hosted and maintained by the company. These biological, image, physical, question answering, signal, sound, text, and video resources number over 250 and can be applied to over 25 different use cases.

See also

* Comparison of deep learning software
* List of manual image annotation tools
* List of biological databases

References

{{Differentiable computing