{
  "fetched_at": "2026-10-19T12:28:14Z",
  "source_url": "https://www.kaggle.com/competitions/agtech-ai-hackathon-online-2022-demo/data",
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    {
      "name": "data-description",
      "content": "<p>This is a datasets of cat and dog with labeled. This Data contains around 2.5K images </p>\n\n<h2>File descriptions</h2>\n<ul>\n<li><b> Cat and Dog/train</b> - the train set image contains around 1440 images</li>\n<li><b> Cat and Dog/val</b> - the test set image contains around 480 images</li></li>\n<li><b>Cat and Dog/test</b> - the test set image contains around 480 images</li>\n<li><b>sample_submission.csv</b> - the test submission CSV file</li>\n</ul>\n",
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    {
      "name": "Description",
      "content": "<p>Dataset for cat and dog. 1,200 cat and 1,200 dog.\n\n<h3>Submission File</h3>\nWrite a program in python to create a prediction model for classifying cat and dog by using deep learning algorithms.\n\n&gt;The output format see in \"Evaluation Section\"",
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      "name": "Evaluation",
      "content": "<p>Submissions are evaluated using categorization accuracy. Each image has been labeled with one true class. For each image, you must submit a set of the predicted labels.</p>\n\n<p>Before we delve into the details of each of the accuracy score, it is important to cover the four building blocks used to define the evaluation metrics:</p>\n\n- True Positive (TP) – Actual label is positive and prediction is also positive\n- True Negative (TN) – Actual label is negative and prediction is also negative\n- False Positive (FP) – Actual label is negative but prediction is positive\n- False Negative (FN) – Actual label is positive but prediction is negative\n\n![test](https://i.ibb.co/HnvkpfQ/Classification-Image.png)\n\n<p>Accuracy is the most basic version of evaluation metrics. It is calculated as the ratio of correct predictions (TP + TN) over all the predictions made (TP + TN + FP + FN).</p>\n\n![test](https://miro.medium.com/max/1210/1*N4Lo9Miw397g3XpX7o0CDw.png)\n\n<h3>Submission File</h3>\nYou must submit a csv file with the image file name, and a probability for each class.\n\n<p>The order of the rows does not matter. The file must have a header and should look like the following:</p>\n\n<pre>file name, class\n3, cat\n12, dog\n21, dog\n...\n</pre> e. Please use markdown.",
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    {
      "name": "rules",
      "content": "## **One account per participant**\n<p>You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple account</p>\n\n## **Submission Limits**\n<p>You may submit a maximum of 20 entries per day.</p>\n\n## **Competition Timeline**\n<p>Start Date: May 14, 2022 1:00 PM +7 UTC</p>\n<p>End Date (Final Submission Deadline): May 14, 2022 11:59 PM +7 UTC</p>\n\n",
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