{
  "fetched_at": "2026-10-24T17:00:00Z",
  "source_url": "https://www.kaggle.com/competitions/ccu-python-dl-miniproject/data",
  "pages": [
    {
      "name": "data-description",
      "content": "# Train file : Data that have label ,for you to train and split the validation and your own test dataset.\n# Train_Label.csv : Including 2 columns, Img_name is the name of image, Label is its category.\n# Test file : Data that do not have label ,for you to test and predict, you need to use the Image in this folder to predict and record the Image's names and its labels, then turn it to a .csv file ,format has to be like Train_Label.csv.",
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    {
      "name": "Description",
      "content": "Mini project 2 (Deadline: 16/05/2023)\n* Please use CNN to train and test on one of the following provided datasets.\n- Similar to the 1st mini project, please submit the report, code, and other materials as a compressed file named f{group_ID}.rar’, e.g., f001.rar.\n- The project grade will be given based on the analysis in the report (50%) and the total obtained accuracy (50%), e.g., total_accuracy = 90% => 90% x 50% = 45% is given on the performance score.",
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    {
      "name": "Evaluation",
      "content": "- Multi-class accuracy is a performance metric used in machine learning to measure the accuracy of a multi-class classification model. It is calculated by dividing the number of correctly classified instances by the total number of instances in the dataset.\n\n>The formula for multi-class accuracy is : accuracy = (number of correctly classified instances) / (total number of instances)\n\n- Multi-class accuracy is an important metric because it provides a measure of how well a model is performing overall in correctly classifying instances into multiple classes.",
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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\nDon't cheat!\nApply yourself!\nHave fun!",
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