{
  "fetched_at": "2026-10-24T18:14:17Z",
  "source_url": "https://www.kaggle.com/competitions/dnn2021ssl/data",
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    {
      "name": "Description",
      "content": "#Introduction to Machine Learning (Spring 2021)\n##Final Project (Image classification with Semi Supervised Learning)\n\n![description_1](https://i.imgur.com/fl86beG.png)\n- Build a machine learning model for the image classification with few labeled data where most of the data are unlabeled. Leverage ‘large unlabeled data’ to improve the model performance which originally has ‘small sized labeled data’ only.\n\n\nReference :\nhttps://paperswithcode.com/task/semi-supervised-image-classification\nhttps://amitness.com/2020/07/semi-supervised-learning/\nhttps://blog.est.ai/2020/11/ssl/\n\n\n",
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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\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "Leaderboard",
      "content": "### There are two baselines at **Leaderboard** tab. \n\n![baselines](https://i.imgur.com/75ZSsrC.png)\n\n### 1. **Baseline(100%)**\nThe score results from trained model with 40,551 labeled dataset. (whole train dataset)\nModel : CNN in model.py file\n\n### 2. **Baseline(10%)**\nThe score results from trained model with 10% labeled dataset over whole train dataset. \nModel : CNN in model.py file\n\nThose two baselines use hyper-parameters as below.\n![params](https://i.imgur.com/gewatbq.png)",
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