{
  "fetched_at": "2026-10-24T16:24:03Z",
  "source_url": "https://www.kaggle.com/competitions/2021-ai-training/data",
  "pages": [
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    {
      "name": "Description",
      "content": "This is the midterm project of Introduction to AI Training 2021 by Adar lab\n\nIn this project , you are required to build a convolutional neural network for image classification, possibly with some advanced training tips.\n\nPlease notice that utilizing external data or \"pre-trained\" model for training is **prohibited**.\nPlease upload your prediction .csv to kaggle and check your score\n\n\n\n# Dataset Description\nImage Classification: Canadian Institute For Advanced Research\nTraining data: 50000 images\nTesting data: 10000 images\n\nPlease DO NOT use the testing data and other data for training, we will consider this as CHEATING.\n\n# Deadlines\nKaggle: 8/8  23:59 in UTC+8 (No late submission!)\n",
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    {
      "name": "Evaluation",
      "content": "<p>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 copied here:</p>\n<hr>\n<p>The evaluation metric for this competition is <a href=\"https://www.kaggle.com/wiki/MeanFScore\"> Mean F1-Score</a>. The F1 score, 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>\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: AuthorId and DuplicateAuthorIds. DuplicateAuthorIds should be a space-delimited list. Every AuthorId counts as his/her own duplicate, and every duplicate should be listed under each of its respective ids.&nbsp;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).</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>AuthorId,DuplicateAuthorIds<br>1,1<br></pre>",
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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\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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