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# datasets. Framework for working with different datasets

The datasets module includes classes for working with different datasets: load data, evaluate different algorithms on them, contains benchmarks, etc.

It is planned to have:

> - basic: loading code for all datasets to help start work with them.
> - next stage: quick benchmarks for all datasets to show how to solve them using OpenCV and implement evaluation code.
> - finally: implement on OpenCV state-of-the-art algorithms, which solve these tasks.

## Action Recognition

> HMDB: A Large Human Motion Database [1]

> Sports-1M Dataset

## Face Recognition

> Adience

> Labeled Faces in the Wild [1]

## Gesture Recognition

> ChaLearn Looking at People

> Sheffield Kinect Gesture Dataset

## Human Pose Estimation

> HumanEva Dataset

> PARSE Dataset

## Image Registration

> Affine Covariant Regions Datasets

> Robot Data Set

## Image Segmentation

> The Berkeley Segmentation Dataset and Benchmark

> Weizmann Segmentation Evaluation Database

## Multiview Stereo Matching

> EPFL Multi-View Stereo

> Stereo – Middlebury Computer Vision

## Object Recognition

> ImageNet

> MNIST [2]

> SUN Database

## Pedestrian Detection

> Caltech Pedestrian Detection Benchmark [2]

## SLAM

> KITTI Vision Benchmark

> TUMindoor Dataset

## Text Recognition

> The Chars74K Dataset

> The Street View Text Dataset [1]

Footnotes

> [1] | (1, 2, 3) Benchmark implemented

> [2] | (1, 2) Not used in Vision Challenge

## Help and Feedback

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