{
  "fetched_at": "2026-10-19T10:54:05Z",
  "source_url": "https://www.kaggle.com/competitions/ece597697-sp2023/data",
  "pages": [
    {
      "name": "data-description",
      "content": "Some things to keep in mind:\n\n1. Know how to load the data. The label column indicates the label for each image\n\n2. Each row in test and train combined is a unique image with a unique ID\n\n3. Use the train.csv to generate your models and validation.csv for testing\n\n4. Use the test to generate your submission.csv file\n\nThe labels of test.csv are hidden from you all. The goal is to generate a submission.csv that is like sample.csv but with correct labels",
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      "name": "Description",
      "content": "This is a dataset consisting of a training set of 60,000 examples and a test set of 10000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes\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.\n\nLabels\nEach training and test example is assigned to one of the following labels:\n\n0 T-shirt/top\n1 Trouser\n2 Pullover\n3 Dress\n4 Coat\n5 Sandal\n6 Shirt\n7 Sneaker\n8 Bag\n9 Ankle boot\n\nTL;DR\n\nEach row is a separate image\nColumn 1 is the class label.\nRemaining columns are pixel numbers (784 total).\nEach value is the darkness of the pixel (1 to 255)",
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    {
      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have included below:\n\n---\n\nThe evaluation metric for this competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n\n## Submission Format\n\n**For every author in the dataset**, submission files should contain two columns: `AuthorId` and `DuplicateAuthorIds`. `DuplicateAuthorIds` should be a space-delimited list. Every `AuthorId` counts as its own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list `A,A B C`, `B,B A C`, `C,C A B`.\n\n\nThe file should contain a header and have the following format:\n\n\n```\nAuthorId,DuplicateAuthorIds\n1,1\n```",
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      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
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}