{
  "fetched_at": "2026-10-19T07:23:02Z",
  "source_url": "https://www.kaggle.com/competitions/2024-datalab-cup2/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## File descriptions\n- `pascal_voc_training_data.txt` - the training records\n- `pascal_voc_testing_data.txt` - the testing image records\n- `VOCdevkit_train.zip` - including all training images and annotations\n- `VOCdevkit_test.zip` - including all testing images\n- `evaluate.zip` - including evaluate.py, calculating mAP and output .csv file for uploading\n- `test_predion_example.txt` - example file of an input file for evaluate.py\n- `sample_output_file.csv` - example CSV file(output by `evaluate.py`) for you to submit on this competition",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is comparing your mean Average Precision on the different packs of testing data with the ground truth results(mean squared error).\n\nThe ranking on the leaderboard is 50% of the testing packs. Your final score will be decided by the rest of 50% after the end of the competition.\n\nThere are two baseline results, Benchmark-60 and Benchmark-80. You have to outperform Benchmark-60 to get 60 points, and Benchmark-80 to get 80. Meanwhile, the lower point(error) you achieve, the higher the final score you will get.",
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    {
      "name": "rules",
      "content": "## One Team per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n## No 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\n## Submission Limits\nYou are allowed to submit a maximum of 5 entries per day.\n\nYou may select up to 3 final submissions for judging.\n\n## Competition Timeline\nStart Date: 2024/10/24 15:30 PM GMT+8\n\nEnd Date: 2024/11/20 00:00 AM GMT+8",
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    {
      "name": "abstract",
      "content": "## Object Detection\n\nIn this competition, you have to train a model that recognizes objects in an image. Your goal is to output bounding boxes for objects.\n\nGiven an image(shape = [undefined, undefined, 3]), you need to output a list of bounding boxes (xmin, ymin, xmax, ymax, classlabel, confidencescore) for objects showed in image and its class.",
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