{
  "fetched_at": "2026-10-19T08:04:32Z",
  "source_url": "https://www.kaggle.com/competitions/cv-lecture-22-final-project/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## File descriptions\n\n(Labeled) Training set: `train/`, where images in the same sub-folder are in the same category.\n(Unlabeled) Training set: 'unsup/'\nTest set: `test/`\n\nFile that you need to upload: sample_submission.csv\nNote: There is no .txt file, you need to write your own data_loader to process the data.",
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    {
      "name": "Description",
      "content": "## Semi-Supervised Image Classification Competition\n\nImage classification is a classic task in the Computer Vision domain. In this classification competition, you need to train your model based on 1) a total of 20 classes of labeled images; 2) auxiliary unlabeled images, and predict the type of images in the test set to get as much accuracy as possible.\n\nNOTE: You are NOT allowed to use ImageNet pretrained weights.\n\n\n",
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    {
      "name": "Evaluation",
      "content": "We adopt the Categorization Accuracy metric for evaluation.",
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