{
  "fetched_at": "2026-10-19T16:15:16Z",
  "source_url": "https://www.kaggle.com/competitions/2024-zjutest-5/data",
  "pages": [
    {
      "name": "data-description",
      "content": "train.csv - the training set\ntest.csv - the test set\nsample_submission.csv - a sample submission file in the correct format\n\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "数据集中有一个emergency_train.csv，用来存放训练样本的标签。",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "\nID,TARGET\n    2,0\n    5,0\n    6,0\n    etc.",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "Don't cheat!\nApply yourself!\nHave fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "abstract",
      "content": "迁移学习指的是保存已有问题的解决模型，并将其利用在其他不同但相关问题上。 比如说，训练用来辨识汽车的模型也可以被用来提升识别卡车的能力。很多情况下迁移学习能够简化或降低模型构建的难度，甚至还能取得不错的准确度。\n本实验将针对一个小的图片数据集，使用PyTorch进行迁移学习演示，包括如何使用预训练模型，并将结果自己搭建的卷积神经网络模型进行性能比较。\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}