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  "source_url": "https://www.kaggle.com/competitions/cuny-csi-fashion-mnist/data",
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      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training set</li>\n<li><strong>test.csv</strong> - the test set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>Id</strong> - Case ID</li>\n<li><strong>Category</strong> - Class labels</li>\n<li><strong>PixelNNN</strong> - Grayscale 28x28 image pixels</li>\n</ul>\n<p>\nThe pixels are ordered so that <code>imshow(data[j,:].reshape((28,28)))</code> displays the image properly.\n</p>",
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      "name": "Description",
      "content": "# Context\n\nFashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.\n\nThe original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. \"If it doesn't work on MNIST, it won't work at all\", they said. \"Well, if it does work on MNIST, it may still fail on others.\"\n\n# Content\n\nEach image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. The training and test data sets have 785 columns. The first column consists of the class labels (see above), and represents the article of clothing. The rest of the columns contain the pixel-values of the associated image.\n\nTo locate a pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27. The pixel is located on row i and column j of a 28 x 28 matrix.\nFor example, pixel31 indicates the pixel that is in the fourth column from the left, and the second row from the top, as in the ascii-diagram below. \n\n## Labels\n\nEach training and test example is assigned to one of the following labels:\n\n* 0 T-shirt/top\n* 1 Trouser\n* 2 Pullover\n* 3 Dress\n* 4 Coat\n* 5 Sandal\n* 6 Shirt\n* 7 Sneaker\n* 8 Bag\n* 9 Ankle boot \n\nTL;DR\n\n* Each row is a separate image\n* Column 1 is the class label.\n* Remaining columns are pixel numbers (784 total).\n* Each value is the darkness of the pixel (1 to 255)\n\n\n## Acknowledgements\n\nOriginal dataset was downloaded from https://github.com/zalandoresearch/fashion-mnist\n\nDataset was converted to CSV with this script: https://pjreddie.com/projects/mnist-in-csv/\n\n## License\n\nThe MIT License (MIT) Copyright © [2017] Zalando SE, https://tech.zalando.com\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n",
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    {
      "name": "Evaluation",
      "content": "Submissions will be scored by macro-averaged accuracy.\n\nSubmissions should have a no header and a single columns containing the predicted clothing class.",
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