{
  "fetched_at": "2026-10-19T09:39:42Z",
  "source_url": "https://www.kaggle.com/competitions/ai-team1/data",
  "pages": [
    {
      "name": "data-description",
      "content": "this dataset contains natural images of size 150*150 for 6 categories. the training set contains 6 folders around 2000 for each category, the testing set contains 3000 images. the goal is to build a model that can recognize the category of each image in the testing set\n\n## Files\n\n*   **seg_train** -training folder\n*   **seg_test** - testing folder",
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    {
      "name": "Description",
      "content": "this dataset contains natural images of size 150*150 for 6 categories. the training set contains 6 folders around 2000 for each category, the testing set contains 3000 images. the goal is to build a model that can recognize the category of each image in the testing set",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
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    {
      "name": "Evaluation",
      "content": "your evaluation based on your work ,your models performance\n\n---\n\nSubmissions are evaluated on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.\n\n\n## Submission File\nFor each ID in the test set, you must predict a probability for the TARGET variable. The file should contain a header and have the following format:\n\n    ID,Prediction\n    2.jpg,cat\n   ",
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      "hasIsPublished": false,
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    },
    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!!\n - Apply yourself!\n - Have fun!",
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      "hasIsPublished": false,
      "postTitle": "",
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    },
    {
      "name": "abstract",
      "content": "\nthe goal of the competition is to analyze the data, use data augmentation if needed, and build suitable deep learning models that can recognize 6 categories of natural images (buildings, forest, glacier, mountain, sea and street)",
      "isPublished": false,
      "hasIsPublished": false,
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}