{
  "fetched_at": "2026-10-19T06:39:19Z",
  "source_url": "https://www.kaggle.com/competitions/MLDS-2021-22/data",
  "pages": [
    {
      "name": "data-description",
      "content": "data should contain images, overview. ",
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    {
      "name": "Description",
      "content": "This is the official Kaggle competition for the 2021-22 MLDS Long Competition at UT Austin. You can find all necessary information in the \"Data\" tab above. This competition is comprised of 20,000 different synthetic images, with 10,000 as a training and testing set and the other 10,000 as the final validation and scoring set. You will be graded on how well you can identify 38 unique objects in each scene by giving a confidence that it exists for each image. The final deadline for this project is currently set to midnight on April 17th, 2022. A PowerPoint with further explanation can be found in the \"Data\" tab under `Custom Image Object Detection.pptx`\n\n[Join our Discord](https://discord.gg/zQUrvdKJbb) to ask any questions or join the club!",
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    {
      "name": "Evaluation",
      "content": "The scoring portion of the contest will be over the second half of the data provided, namely those with an ID between 10,001 and 20,000. A submission should be a `.csv` containing two columns: `Id` and `Predicted`. \n\n- `Id` is a concatenation of the the scene number, between 10,001 and 20,000 with the class number 1-43. This should be formatted as {scene_id}_{class_id}, ex. \"`00001_38`\"\n- `Predicted` should be your probabilistic belief that the class_id specified is in the scene_id specified (from the previous example, how likely it is that you think class 38 is in scene 00001). Your input for this confidence must be a valid probability, given as a float from 0-1. \n\nAn example file is shown in the `sample_submission.csv` file provided.",
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    },
    {
      "name": "rules",
      "content": "Teams of up to 3\nFeel free to use any methods to preform object detection including: using pre trained models, using external data\nHave fun!",
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