{
  "fetched_at": "2026-10-19T13:34:43Z",
  "source_url": "https://www.kaggle.com/competitions/2024-upscale/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Files\n\n*   **downscaled.zip** - the low-resolution images (PNGs)\n*   **upscaled_nearest.csv** - the low-resolution images up-scaled with nearest neighbor algorithm, as a CSV\n*   **upscaled_bicubic.csv** - the low-resolution images up-scaled with bi-cubic algorithm, as a CSV\n\nOther relevant data / notebooks can be found here: https://drive.google.com/drive/u/4/folders/1Uicvd8pQg6zcWZN5qJd7zht6AXqKzlAR\n\n## Columns\n\n*   `id` - id of the pixel\n*   `b` - blue component\n*   `g` - green component\n*   `r` - red component\n\n## Marks\n- better than nearest: 50%\n- better than bicubic: 60%\n- Top 10: 80%\n- Top 5: 100%",
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    {
      "name": "Description",
      "content": "In the ZIP file, you will find the 30 low-resolution images (each one is 64x64px).\nYou are asked to create a 256x256px high-resolution version of each image.\n\nYou will need to create your own training dataset. We provide you a code that turns a 256x256px image to its low resolution (64x64px) version (it is the code that was used to create the testing set).",
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    {
      "name": "Marking scheme:",
      "content": "Better than bicubic -> 100%",
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    },
    {
      "name": "rules",
      "content": "Your anonymized information may be utilized for conducting basic statistical analysis and for the purpose of training an ensemble learning during a TD. It is also possible that the information may be included in a scholarly publication.",
      "isPublished": false,
      "hasIsPublished": false,
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    {
      "name": "abstract",
      "content": "Deadline: 19th of March 2024.\nBe careful, uploading a solution CSV may take time (~5min) because of the amount of data.",
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}