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The NTU RGB-D (Nanyang Technological University's Red Blue Green and Depth information) dataset is a large dataset containing recordings of labeled human activities. [1] This dataset consists of 56,880 action samples containing 4 different modalities (RGB videos, depth map sequences, 3D skeletal data, infrared videos) of data for each sample.
80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0. Images manually segmented. 80 Images Aerial Classification, object detection 2013 [156] [157] J. Yuan et al. KIT AIS Data Set Multiple labeled training and evaluation datasets of aerial images of crowds. Images manually labeled to show paths of individuals through ...
0–9. 80 Million Tiny Images; C. Caltech 101; ... NTU RGB-D dataset; O. Overhead Imagery Research Data Set; T. Textures: A Photographic Album for Artists and ...
Full RGB version at 120×80-pixels for comparison (e.g. as a film scan, Foveon or pixel shift image might appear) Bryce Bayer 's patent (U.S. Patent No. 3,971,065 [ 6 ] ) in 1976 called the green photosensors luminance-sensitive elements and the red and blue ones chrominance-sensitive elements .
3.4 TB English text, 1.4 TB Chinese text, 1.1 TB Russian text, 595 MB German text, 431 MB French text, and data for 150+ languages (figures for version 23.01) JSON Lines [458] Natural Language Processing, Text Prediction 2021 [459] [460] Ortiz Suarez, Abadji, Sagot et al. OpenWebText An open-source recreation of the WebText corpus.
ImageNet (ILSVRC): 1 million color images of 1000 classes. Imagenet images are higher resolution, averaging 469x387 resolution. Street View House Numbers (SVHN): Approximately 600,000 images of 10 classes (digits 0–9). Also 32x32 color images. 80 million tiny images dataset: CIFAR-10 is a labeled subset of this dataset.
The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]