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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.
~60 million Images, text Object recognition, scene recognition 2015 [20] [21] [22] Yu et al. Open Images A Large set of images listed as having CC BY 2.0 license with image-level labels and bounding boxes spanning thousands of classes. Image-level labels, Bounding boxes 9,178,275 Images, text Classification, Object recognition 2017 (V7 : 2022) [23]
NTU RGB-D dataset; O. ... This page was last edited on 5 May 2023, at 16:13 (UTC). Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; ...
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 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. Filtered through license detection and deduplication. 6 TB, 51.76B files (prior to deduplication); 3 TB, 5.28B files (after). 358 programming languages. Parquet Language modeling, autocompletion, program synthesis. 2022 [402] [403]
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 .
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Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works. CIFAR-10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009. When the dataset was created, students were paid to label all of the images. [5]