{
  "id": 126668,
  "title": "How to crop face when I submit using kaggle notebook?",
  "url": "/competitions/deepfake-detection-challenge/discussion/126668",
  "author_name": "",
  "post_date": "2020-01-19T08:44:00.781635900Z",
  "votes": 1,
  "comment_count": 2,
  "views": 0,
  "content": "<p>I preprocess all the data by facenet-pytorch, but I can’t use facenet-pytorch in kaggle notebook</p>",
  "messages": [
    {
      "id": "722881",
      "postDate": "01/19/2020 08:44:00",
      "content": "<p>I preprocess all the data by facenet-pytorch, but I can’t use facenet-pytorch in kaggle notebook</p>",
      "rawMarkdown": "I preprocess all the data by facenet-pytorch, but I can’t use facenet-pytorch in kaggle notebook",
      "votes": null
    },
    {
      "id": "723053",
      "postDate": "01/19/2020 12:59:21",
      "content": "<p>I believe there are already public datasets that contain the facenet-pytorch package. So after incorporating that, you could do something like this in your notebook to make it available to your code:</p>\n\n<p>!pip install /kaggle/input/facenet-pytorch-vggface2/facenet_pytorch-2.0.0-py3-none-any.whl</p>",
      "rawMarkdown": "I believe there are already public datasets that contain the facenet-pytorch package. So after incorporating that, you could do something like this in your notebook to make it available to your code:\n\n!pip install /kaggle/input/facenet-pytorch-vggface2/facenet_pytorch-2.0.0-py3-none-any.whl",
      "votes": null
    },
    {
      "id": "723093",
      "postDate": "01/19/2020 14:01:34",
      "content": "<p>Thank you very much！</p>",
      "rawMarkdown": "Thank you very much！",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 723053,
      "author_name": "peterdekkers101",
      "author_url": "",
      "post_date": "01/19/2020 12:59:21",
      "content": "<p>I believe there are already public datasets that contain the facenet-pytorch package. So after incorporating that, you could do something like this in your notebook to make it available to your code:</p>\n\n<p>!pip install /kaggle/input/facenet-pytorch-vggface2/facenet_pytorch-2.0.0-py3-none-any.whl</p>",
      "votes": null,
      "replies": [
        {
          "id": 723093,
          "author_name": "alazycoder",
          "author_url": "",
          "post_date": "01/19/2020 14:01:34",
          "content": "<p>Thank you very much！</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "722881": "I preprocess all the data by facenet-pytorch, but I can’t use facenet-pytorch in kaggle notebook",
    "723053": "I believe there are already public datasets that contain the facenet-pytorch package. So after incorporating that, you could do something like this in your notebook to make it available to your code:\n\n!pip install /kaggle/input/facenet-pytorch-vggface2/facenet_pytorch-2.0.0-py3-none-any.whl",
    "723093": "Thank you very much！"
  },
  "source": "meta"
}