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  "fetched_at": "2026-10-19T20:37:58Z",
  "source_url": "https://www.kaggle.com/competitions/egci-486-final-exam-2025-1/data",
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      "name": "data-description",
      "content": "![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2561061%2Fb1a80731d2ff42814844ff5f4dbfb3da%2Fexample_image.png?generation=1764677402343477&alt=media)\n<p>The dataset consists of ten color classes, each representing a single color: Black, Blue, Brown, Green, Grey, Orange, Red, Violet, White, and Yellow. Each class contains exactly 25 images, resulting in a perfectly balanced dataset. The main purpose of this dataset is to support the training and evaluation of image classification models, enabling them to correctly predict and classify images by color. This dataset is also suitable for developing and testing machine learning techniques in a balanced classification scenario. Students can refer to the original dataset on Kaggle for more details.</p>\n\n<h2>File descriptions</h2>\n<ul>\n<li><b>sample-submission.csv</b> - a sample submission file in the correct format</li>\n<li><b>starter_code.ipynb</b> - a python code to process a feature extraction, model and submission file</li>\n<li><b>train</b> - the training set contain 10 folders (Black, Blue, Brown, Green, Grey, Orange, Red, Violet, White, Yellow) contains 170 images</li>\n<li><b>test</b> - the test set image contains around 80 images</li>\n</ul>\n",
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      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n -  Don't cheat!\n - Apply yourself!\n - Have fun!",
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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 class.\n\n\nThe 10 classes to predict are:\n\n- Black\n- Blue\n- Brown\n- Green\n- Grey\n- Orange\n- Red\n- Violet\n- White\n- Yellow\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>id,classes\n1.jpg, Green\n2.jpg, Yellow\n3.jpg, Violet\n...\n</pre> \n",
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    {
      "name": "abstract",
      "content": "## Final Exam : Image Classification\n|**Day-Time**\t | **Item** |\n| --- | --- |\n| Monday 8 December 2025 at 13:30 PM| Open |  |\n| Monday 8 December 2025 at 15:00 PM| Close |  |\n\n\n## Member\n\n- This competition is an individual format. \n- All competitors must join the competition first.\n- After joining competition, all competitors must change their **Team Name** to **6XXXXXX-Name** in the Team tab.\n\n## Leaderboards\n\nEach group is allowed to send results 6 times per day. The score will be displayed into 2 parts:\n- 50 percents of dataset will be displayed in the Public Leaderboard where the contestants can check their scores at any time.\n- 50 percents of dataset will be stored and displayed in the Private Leaderboard which will be used to calculate the score to determine the result of the competition. The results will be displayed after the competition is over.\n\n\n## Evaluation Metric\nMetric: Categorization Accuracy\n\n## Submission\nTo submit the results, the test set should be predicted class result and collected into \".CSV\" file as shown in the example file \"sample_submission.csv\", otherwise Kaggle will not be able to calculate submission score.",
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