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. Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png
The Freiburg Forest dataset was collected using a Viona autonomous mobile robot platform equipped with cameras for capturing multi-spectral and multi-modal images. The dataset may be used for evaluation of different perception algorithms for segmentation, detection, classification, etc. All scenes were recorded at 20 Hz with a camera resolution of 1024x768 pixels. The data was collected on three different days to have enough variability in lighting conditions as shadows and sun angles play a crucial role in the quality of acquired images. The robot traversed about 4.7 km each day. The dataset creators provide manually annotated pixel-wise ground truth segmentation masks for 6 classes: Obstacle, Trail, Sky, Grass, Vegetation, and Void. Source: http://deepscene.cs.uni-freiburg.de/ Image Source: http://deepscene.cs.uni-freiburg.de/
The eSports Sensors dataset contains sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected includes: * Hand/head/chair movements. * Heart rate. * Muscle activity. * Gaze movement on the monitor. * Galvanic skin response(GSR). * Electroencephalography (EEG). * Mouse and keyboard activity. * Facial skin temperature. * Environmental data. The data were collected for one team of 5 people simultaneously. In-game logs and meta information for each match are also provided for each match. Source: https://github.com/smerdov/eSports_Sensors_Dataset
