{
  "fetched_at": "2026-10-19T12:32:50Z",
  "source_url": "https://www.kaggle.com/competitions/109-2-machine-learning/data",
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      "content": "\n<p>The training and testing images are 256x256 gray images. There are 6 classes (0 ~ 5) in this dataset, which are labeled sequentially as 0, 1, 2, 3, 4, 5.<p>\n<h2>File descriptions</h2>\n<ul>\n\n<li><strong>train</strong> - training image dataset</li>\n<li><strong>val</strong> - validation image dataset</li>\n<li><strong>test</strong> - testing image dataset</li>\n<li><strong>Submission.csv</strong> - a sample submission file in the correct format</li>\n</ul>",
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      "name": "Description",
      "content": "<p>In this homework, we will train a model for classifying image taken by AOI. This dataset includes six image classes, and there are training and validation dataset. We will evaluate your model with testing dataset.</p>\n<p>You are free to use any neural network structure, any DL libary like TF, Keras, Pytorch, or any language like C, Java, Javascript, Python.</p>\n<p>Please register a Kaggle account with your student ID.</p><p>\n</p><h2>Data Description</h2>\n<p>The data description is illustrated below figure</p>\n<img src=\"https://scontent.ftpe8-4.fna.fbcdn.net/v/t1.6435-9/192637730_1594365827426277_1594772284325430964_n.jpg?_nc_cat=110&ccb=1-3&_nc_sid=730e14&_nc_ohc=rkUImxr8eacAX_CW9KY&_nc_ht=scontent.ftpe8-4.fna&oh=546f57b4f7d8bf6b9d19effbeda41e74&oe=60DA2FA2\">\n<h2>Output format</h2>\n<p>A submission file of prediction results should be a CSV file containing two columns: Id and Label, separated by ','.\n</p><p>The file should contain a header and have the following format:</p><p>\n</p><pre>ID,Label\ntest_00039.jpg,0\ntest_00049.jpg,0\ntest_00056.jpg,0\ntest_00076.jpg,0\ntest_00078.jpg,0\ntest_00102.jpg,0</pre>",
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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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    },
    {
      "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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