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  "fetched_at": "2026-10-24T10:32:38Z",
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      "content": "## **Problem 3: CURE-OR Classification Competition on Kaggle (35pts)**\nAfter filling in the table in problem 2, you have had some basic understandings and observations how these networks perform in `CURE-OR` dataset. In this problem, you need to choose and design your own training strategy to achieve better top-1 accuracy in a `Kaggle` competition.\n\nLink: https://www.kaggle.com/competitions/funml-sp22-final-exam-problem-3/\n\n- You should start with one of pretrained `alexnet` or `vgg16` or `resnet18`. <br>\n\n- You should only use the train data for training your network. You can use any loss function, optimizer, regularization, data augumentation strategy or modify model structure to help youself get better test results. <br>\n\n- You should explain your strategy and desgin in detail.\n\n- Use the cell provided below to submit test result as a `.csv` file in `Kaggle`.\n\n- In `Kaggle`, 50% of your result will be used to calculate the top-1 accuracy for the score on the leaderboard. The rest of 50% will be used to calculate your actual score after the deadline. This means the score on the leaderboard only gives you a sense of how you perform, and it will not be your final score. <br>\n\n- You have 5 times to submit result in `Kaggle` every day. Please use it wisely.\n\n- 10 pts will be according to your strategy design and the rest of 25 pts depends on your ranking. $$\\text{score}=(50-\\text{your ranking} +1)\\times0.5$$ <br>",
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      "name": "rules",
      "content": "\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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