{
  "fetched_at": "2026-10-20T10:18:38Z",
  "source_url": "https://www.kaggle.com/competitions/bs-6204-classroom-hackathon-on-deep-learning/data",
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    {
      "name": "rules",
      "content": "Try to apply what you have learned so far. Have fun!",
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      "content": "<p> Training, validation and testing images are listed in the 3 folders train/ val/ test/ </p>\n<p>The data files train.csv and test.csv contain image file name and the corresponding labels (0 or 1).</p>\n<p>Each image is 512 pixels in height and 512 pixels in width. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.</p>\n",
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    {
      "name": "Description",
      "content": "\n<p>Welcome! This notebook contains everything you need for the hackathon.\n\nYou’ll start with a simple CNN baseline trained on medical images. Then, you can modify the code to improve performance.\n\nYour instructor provided a data_public-3.zip file containing train, val, and test folders. Upload it before running Step 1.\n\n",
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    {
      "name": "Evaluation",
      "content": "<h3>Goal</h3>\nThe goal in this competition is to take an image of a fundus photo, and determine the category of this image.<br>For every image in the test set, you should predict the correct label. \n\n<h3>Metric</h3>\n<p>This competition is evaluated on the AUC of your model prediction.</p>\n\n<h3>Submission File Format</h3>\n<p>Just submit the submission.csv generated by your code</p>\n",
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