{
  "fetched_at": "2026-10-20T05:09:05Z",
  "source_url": "https://www.kaggle.com/competitions/csc578hw3-f18/data",
  "pages": [
    {
      "name": "rules",
      "content": "\r\n\t<!-- Begin Base Rules -->\r\n    <h3>One account per participant</h3>\r\n    <p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\r\n    <h3>No private sharing outside teams</h3>\r\n    <p>\r\n        Privately sharing code or data outside of teams is not permitted.\r\n        It's okay to share code if made available to all participants on the forums.\r\n    </p>\r\n    <h3>Team Mergers</h3>\r\n    <p>Team mergers are not allowed in this competition.</p>\r\n\r\n    <h3>Team Limits</h3>\r\n    <p>There is no maximum team size.</p>\r\n    <h3>Submission Limits</h3>\r\n    <p>You may submit a maximum of 2 entries per day.</p>\r\n    <p>You may select up to 2 final submissions for judging.</p>\r\n    <h3>Competition Timeline</h3>\r\n    <p>Start Date: <strong>TBA</strong></p>\r\n    <p>Merger Deadline: <strong>None</strong></p>\r\n    <p>Entry Deadline: <strong>None</strong></p>\r\n    <p>End Date: <strong>2/1/2014 12:00 AM UTC</strong></p>\r\n\t<!-- End Base Rules -->\r\n<p>The rules page is a bit special: it will auto-update the text based on the parameters of the competition. The rules you need to add here are any additional rules relating to dataset usage. You may also put grading information here.</p>",
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    {
      "name": "data-description",
      "content": "<p>The dataset is <a href=\"https://www.cs.toronto.edu/~kriz/cifar.html\">CIFAR-10</a>.  It is packaged with Keras.  See the <a href=\"http://condor.depaul.edu/ntomuro/courses/578/assign/HW3.html\">assignment page</a> for more details.</p>",
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    {
      "name": "Description",
      "content": "<p>This is a Kaggle submission site for the HW#3.  Read the <a href=\"http://condor.depaul.edu/ntomuro/courses/578/assign/HW3.html\">assignment page</a> for instructions.</p>\n<p>\nThe competition closes at 10/29/2018 11:59 pm UTC (Coordinated Universal Time; 5 hours ahead of Chicago).</p>\n",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <b>multi-class logarithmic loss</b>. Each image has been labeled with one true class. For each image, you must submit a set of predicted probabilities (one for every image). The formula is then,\n</p>\n<p>\\[ LogLoss = -\\frac{1}{N} \\sum_{i=1}^{N}\\sum_{j=1}^{M} y_{ij}\\cdot \\text{ln}(p_{ij})\\]</p>\n\nwhere N is the number of images in the test set, M is the number of image class labels and ln is the natural logarithm.  y_ij is 1 if observation *i* belongs to class *j* and 0 otherwise, and p_ij is the predicted probability that observation *i* belongs to class *j*.\n\n<p>The LogLoss values are to be minimized.  Smaller values, towards 0, indicate better performance (because LogLoss is always non-negative).</p>\n<h2>Submission Format</h2>\n<p>For each image in the test set, you must predict the probability it belongs to each class. The file should contain a header and have the following format (.csv):</p>\n<pre>id,cat0,cat1,cat2,cat3,...,cat9\n1,0.5,0,0,0,0,0.1,0,0.4,0,0\n2,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1\n3,0,0.15,0,0,0,0.85,0,0,0,0</pre>",
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