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Information about this dataset's format is available in the HuggingFace dataset card and the project's website. The dataset can be downloaded here, and the rejected data here. 2016 [343] Paperno et al. FLAN A re-preprocessed version of the FLAN dataset with updates since the original FLAN dataset was released is available in Hugging Face: test data
Original PNG files, sorted per camera and then per acquisition. MATLAB datafiles with one 16384 times 5000 matrix per camera per acquisition. 30,000 Images and .mat files Authentication 2012 [183] S. Voloshynovskiy, et al. PharmaPack Dataset 1,000 unique classes with 54 images per class.
The LabelMe project provides a set of tools for using the LabelMe dataset from Matlab. Since research is often done in Matlab, this allows the integration of the dataset with existing tools in computer vision. The entire dataset can be downloaded and used offline, or the toolbox allows dynamic downloading of content on demand.
The GDF format is often used in brain–computer interface research. [4] [5] [6] However, since GDF provides a superset of features from many different file formats, it could be also used for many other domains. The free and open source software BioSig library provides implementations for reading and writing of GDF in GNU Octave/MATLAB and C ...
^ The current default format is binary. ^ The "classic" format is plain text, and an XML format is also supported. ^ Theoretically possible due to abstraction, but no implementation is included. ^ The primary format is binary, but text and JSON formats are available. [8] [9]
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.
mzXML is a XML (eXtensible Markup Language) based common file format for proteomics mass spectrometric data. [9] [10] This format was developed at the Seattle Proteome Center/Institute for Systems Biology while the HUPO-PSI was trying to specify the standardized mzData format, and is still in use in the proteomics community.
The Caltech 101 data set was used to train and test several computer vision recognition and classification algorithms. The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning, [ 4 ] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes.