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  "fetched_at": "2026-10-19T07:48:59Z",
  "source_url": "https://www.kaggle.com/competitions/fcis-sc-deeplearning-competition/data",
  "pages": [
    {
      "name": "rules",
      "content": "    \n\t\n    <h3>One account per team 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    </p>\n    <h3>Tensorflow is only the allowed API </h3>\n    <h3>Submission Limits</h3>\n    <p>You may submit a maximum of 3 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>17/4/2019</strong></p>\n    <p>End Date: <strong>12/5/2019 </strong></p>\n\t",
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    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.rar</strong> - the training set</li>\n<li><strong>test.rar</strong> - the test set</li>\n<li><strong>Submit.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n<h2>Data fields</h2>\n<ul>\n<li><strong>ID</strong> - the name of the photo</li>\n<li><strong>Label</strong> - the predicted class for that ID</li>\n</ul>\n<h2>Labels</h2>\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2256844%2F61cf0030c7934df02e8c15b0e8e8b9a8%2FCapture.PNG?generation=1574748985762547&alt=media)\n",
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    {
      "name": "Description",
      "content": "<h2>Introduction</h2>\n<p>\nWelcome to the competition. This competition is for SC Students in Neural Networks course at FCIS. The goal of the competition is to build deep learning models that can distinguish between indoor scenes. This dataset contains great training and testing sets for forming models that can distinguish the scenes.\n</p><p>\n</p><h2>Content</h2>\n<p>\nThis dataset contains 3,910 images of 10 classes of Indoor scenes. The data is split into 3,138 training images and 772 testing images, where each class has been split roughly in a 80-20 split. \n</p><p></p>\n",
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    {
      "name": "Evaluation",
      "content": "<p>You have to submit a file in CSV format named as 'submit.csv'.</p>\n<hr>\n<p>The evaluation metric for this competition is the overall accuracy </p><p> \n\n</p><h2>Submission Format</h2>\n<p><strong> The file contains 2 column, the first column is ID, which is the name of the test image. The second column is label, which is your predicted label for the image </strong> </p>\n<p>The file should contain a header and have the following format:</p>\n<pre>id,label<br>00070.jpg,1<br></pre>",
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