{
  "fetched_at": "2026-10-19T07:38:33Z",
  "source_url": "https://www.kaggle.com/competitions/caltech256images/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This page appears alongside the data files. It describes what files have been provided and the format of each. There is no single format for this page that is appropriate for all competitions, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.\n\nParticipants should be able to answer these types of questions after reading the data description:\n\nWhat files do I need?  \nWhat should I expect the data format to be?  \nWhat am I predicting?  \nWhat acronyms will I encounter?\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\n## Columns\n\n*   `example_id` - definition of example_id\n*   `feature_1` - definition of feature_1\n*   etc.",
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      "postTitle": "",
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    {
      "name": "Description",
      "content": "**Objective**: To classify images in the Caltech-256 dataset, which is an improvement over Caltech-101 dataset using a Convolutional Neural Network.\n\n\n**Description**: Caltech-256 is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images. The dataset is a superset of the Caltech-101 dataset.\n\n\n**Acknowledgements**\nOriginal data source and banner image: http://www.vision.caltech.edu/Image_Datasets/Caltech256/\n\nHere is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).\n\n\n\n",
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      "postTitle": "",
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    },
    {
      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. \n\nThe evaluation metric for this competition is Categorization Accuracy.\n\nThe file should contain a header and have the following format:\n```\nimg_path, label\ntest/1.jpg, Faces-Easy-101\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\nNo private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\nTeam Mergers\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\nTeam Limits\nThe maximum team size is 4.\n\nSubmission Limits\nYou may submit a maximum of 10 entries per day.\n\nCompetition Timeline\nCompetition Timeline dates (including Entry Deadline, Final Submission Deadline, Start Date, and Team Merger Deadline, as applicable) are reflected on the competition’s Overview > Timeline page.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}