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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.
The dataset is labeled with semantic labels for 32 semantic classes. over 700 images Images Object recognition and classification 2008 [58] [59] [60] Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, Roberto Cipolla RailSem19 RailSem19 is a dataset for understanding scenes for vision systems on railways. The dataset is labeled semanticly and ...
Download QR code; Print/export Download as PDF; ... NTU RGB-D dataset; O. Overhead Imagery Research Data Set; T.
A dataset for NLP and climate change media researchers The dataset is made up of a number of data artifacts (JSON, JSONL & CSV text files & SQLite database) Climate news DB, Project's GitHub repository [394] ADGEfficiency Climatext Climatext is a dataset for sentence-based climate change topic detection. HF dataset [395] University of Zurich ...
The suite contained tools that enable designers to create game lighting, character animation, low level path finding, high-level AI and advanced user interfaces. [2] On July 12, 2017, Autodesk removed Scaleform, Beast, HumanIK, and Navigation from their online store, and announced the ending of support for the products.
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. 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.
MrBeast is the ringmaster for a “Squid Game”-style winner-take-all battle for $5 million in his forthcoming “Beast Games” show for Amazon’s Prime Video. “Beast Games” will premiere ...
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]