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  "source_url": "https://www.kaggle.com/competitions/2021bmi707-assignment-2-q5/data",
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      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.tar</strong> - the training set (Dropbox link: <a href=\"https://www.dropbox.com/s/feaakvusdvkep3n/train.tar\">https://www.dropbox.com/s/feaakvusdvkep3n/train.tar)</a></li>\n<li><strong>train.csv</strong> - the labels of the training set (Dropbox link: <a href=\"https://www.dropbox.com/s/9kebfecemhfkj7k/train.csv\">https://www.dropbox.com/s/9kebfecemhfkj7k/train.csv)</a></li>\n<li><strong>testPublic.tar</strong> - the public test set (the labels are not given). This dataset is used to generate the evaluation results for the leaderboard. (Dropbox link: <a href=\"https://www.dropbox.com/s/20d8b9z8znc2lmp/testPublic.tar\">https://www.dropbox.com/s/20d8b9z8znc2lmp/testPublic.tar</a>) </li>\n<li><strong>testPrivate.tar</strong> - the private test set (the labels are not given). This dataset is used to generate the final evaluation. (Dropbox link: <a href=\"https://www.dropbox.com/s/lun96wxiq84z7eg/testPrivate.tar\">https://www.dropbox.com/s/lun96wxiq84z7eg/testPrivate.tar</a>) </li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format. (Dropbox link: <a href=\"https://www.dropbox.com/s/a9o0lkszeusdmr9/sampleSubmission.csv\">https://www.dropbox.com/s/a9o0lkszeusdmr9/sampleSubmission.csv</a>)</li>\n</ul>\n<h2>Data fields in the submission</h2>\n<ul>\n<li><strong>Id</strong> - the file name of the image</li>\n<li><strong>Category</strong> - the diagnostic category of the image</li>\n</ul>",
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      "name": "Description",
      "content": "<h2>Question 5: Multiclass classification</h2>\n<p>In this question, we will build multiclass classifiers to distinguish different types of lung diseases using the NIHCXR8 data.</p>\n<p>Please download the training set and the validation set from the Data tab (https://www.kaggle.com/c/2021bmi707-assignment-2-q5/data). Please note that this dataset is different from the one we used in Questions 3 and 4. Please <b>DO NOT</b> use any additional dataset to train or augment your models.</p> \n<p>Feel free to use any (ImageNet or any custom) architecture to classify all available classes.</p>\n<p>In addition to participating in this BMI707 internal Kaggle contest, you would need to provide your answers to the questions and codes in the assignment writeup.</p>\n",
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    {
      "name": "Evaluation",
      "content": "<p>The evaluation metric for this competition is <a href=\"https://en.wikipedia.org/wiki/Sensitivity_and_specificity#Confusion_matrix\">accuracy</a>.</p>\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: Id and Category.</p>\n<p>The file should contain a header and have the following format:</p>\n<pre>Id,Category<br>00000041_004.png,0<br></pre>",
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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</h3>\n    <p>\n        Privately sharing code or data is not permitted.\n        It's okay to share code if made available to all participants on the forums.\n    </p>\n    <h3>Team Limits</h3>\n    <p>Each student is required to submit .</p>\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 2 final submissions for judging.</p>\n    <h3>Competition Timeline</h3>\n    <p>Start Date: <strong>April 14, 2021</strong></p>\n    <p>Entry Deadline: <strong>April 28, 2021 11:59 PM EDT</strong></p>\n    <p>End Date: <strong>April 28, 2021 11:59 PM EDT</strong></p>\n\t\n",
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