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Image classification in Galaxy with MNIST handwritten digits dataset

Kaivan Kamali


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{
  "description": "<p>Credit:&nbsp;Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. &quot;Gradient-based learning applied to document recognition.&quot;&nbsp;Proceedings of the IEEE, 86(11):2278-2324, November 1998<br>\n<br>\nThis is a subset of MNIST handwritten digits dataset (http://yann.lecun.com/exdb/mnist/). Training data of composed of 12,000 images of digits 0 to 9. Test data is composed of 6,000 images of digits 0 to 9 (Original dataset has 60,000 training and 10,000 testing images. We are using a subset for a Galaxy tutorial, so the training is not too computationally intensive). Images are grayscale and 28 by 28 pixels. Each pixel has a value between 0 and 255 (0 for color black, 255 for color white, and all other values for different shades of gray).&nbsp; &nbsp;</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Penn State University", 
      "@type": "Person", 
      "name": "Kaivan Kamali"
    }
  ], 
  "url": "https://zenodo.org/record/4697906", 
  "datePublished": "2021-04-16", 
  "keywords": [
    "Galaxy", 
    "MNIST", 
    "Classification"
  ], 
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  "identifier": "https://doi.org/10.5281/zenodo.4697906", 
  "@id": "https://doi.org/10.5281/zenodo.4697906", 
  "@type": "Dataset", 
  "name": "Image classification in Galaxy with MNIST handwritten digits dataset"
}
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