{
  "fetched_at": "2026-10-19T08:18:26Z",
  "source_url": "https://www.kaggle.com/competitions/applications-of-deep-learning-wustl-fall-2023/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This dataset contains ~30K images of people both with and without glasses.\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `id` - A unique id, for identification.\n*   `file` - The filename, for example, 1.jpg.\n*   `glasses` - The label, 1=glasses, 0=no glasses.",
      "isPublished": false,
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      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "You will be evaluated with logloss.\n\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the glasses variable. The file should contain a header and have the following format:\n\n    id,glasses\n    2,0\n    5,0\n    6,0\n    etc.",
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    },
    {
      "name": "rules",
      "content": "- Teams must be 5 people or less.\n- It is okay to compete by yourself.",
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      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "abstract",
      "content": "You must use AI to determine if pictures of people have glasses on or not. You can see examples of these images here.\n\n![Glasses or No Glasses](https://data.heatonresearch.com/images/wustl/kaggle/2023-fall/row.jpg)\n\nSome of the images will be at different angles, zooms, and lighting. The images are generated to represent a diverse group of people.\n\n![Angles](https://data.heatonresearch.com/images/wustl/kaggle/2023-fall/row-2.jpg)\n\nThese images are 512x512 and were generated with Stable Diffusion, they are not real people.\n",
      "isPublished": false,
      "hasIsPublished": false,
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      "hasPostTitle": false,
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    }
  ]
}