{
  "fetched_at": "2026-10-24T22:47:14Z",
  "source_url": "https://www.kaggle.com/competitions/final-competition-mate06/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<p>Here are describe all files included in dataset and the format of each one.</p>\n<h2>Descriptions</h2>\n<ul>\n<li><strong>Folder train</strong> - the training set of cats and dogs</li>\n<li><strong>Folder validation </strong> - the test validation of cats and dogs</li>\n<li><strong>Folder test</strong> - the test set without annotations of cats and dogs</li>\n<li><strong>sampleSubmissionPets.csv</strong> - a sample submission file in the correct format</li>\n<li><strong>Readme.txt</strong> - supplemental information about the files</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>id</strong> - the identification of the image</li>\n<li><strong>label</strong> -the label to a given class (0-cat or 1-dog)</li>\n</ul>",
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    {
      "name": "Description",
      "content": "<p>This is a competition intended to evaluate student's skills about CNN theory teaching in MATE06-VCRP. All necessary details and tools are described in the Laboratory Practice, which must have been concluded before any submission to this competition.</p>",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation pages describes how submissions will be scored and how students should format their submissions.</p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F-Score</a>. The F1 score (also F-score or F-measure), commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives (tp) to all predicted positives (tp + fp). Recall is the ratio of true positives to all actual positives (tp + fn). The F1 score is given by:</p>\n<p>\\[ F1 = 2\\frac{p \\cdot r}{p+r}\\ \\ \\mathrm{where}\\ \\ p = \\frac{tp}{tp+fp},\\ \\ r = \\frac{tp}{tp+fn} \\]</p>\n<p>The 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.</p>",
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    },
    {
      "name": "rules",
      "content": "\n<h3>One account per participant</h3>\n<p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.</p>\n<h3>No private sharing outside teams</h3>\n<p>\n    Privately sharing code or data outside of teams is not permitted.\n    It's okay to share code if made available to all participants on the forums.\n</p>\n<h3>Team Individual</h3>\n<p>More than one team member not allowed in this competition.</p>\n\n <h3>Submission Limits</h3>\n<p>You may submit a maximum of 2 entries per day.</p>\n<p>You may select up to 1 final submissions for judging.</p>\n<h3>Competition Timeline</h3>\n<p>Start Date: <strong>May 06, 2021</strong></p>\n<p>End Date: <strong>June 05, 2021 11:59 PM UTC</strong></p>",
      "isPublished": false,
      "hasIsPublished": false,
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}