{
  "id": 164717,
  "title": "SIIM code requirements",
  "url": "/competitions/siim-isic-melanoma-classification/discussion/164717",
  "author_name": "",
  "post_date": "2020-07-07T09:22:04.887062600Z",
  "votes": 2,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Hello to the organizers ( <a href=\"/juliaelliott\">@juliaelliott</a> <a href=\"/jwebermsk\">@jwebermsk</a>) and Kagglers, \nI developed several SIIM models outside Kaggle Notebooks (my own machines and colab). I didn't use any external data except those mentioned in the forum. Can I, and if so how, use these results/submissions to be prize eligible?</p>",
  "messages": [
    {
      "id": "918484",
      "postDate": "07/07/2020 09:22:04",
      "content": "<p>Hello to the organizers ( <a href=\"/juliaelliott\">@juliaelliott</a> <a href=\"/jwebermsk\">@jwebermsk</a>) and Kagglers, \nI developed several SIIM models outside Kaggle Notebooks (my own machines and colab). I didn't use any external data except those mentioned in the forum. Can I, and if so how, use these results/submissions to be prize eligible?</p>",
      "rawMarkdown": "Hello to the organizers ( @juliaelliott @jwebermsk) and Kagglers, \nI developed several SIIM models outside Kaggle Notebooks (my own machines and colab). I didn't use any external data except those mentioned in the forum. Can I, and if so how, use these results/submissions to be prize eligible?",
      "votes": null
    },
    {
      "id": "918523",
      "postDate": "07/07/2020 09:44:47",
      "content": "<p>Your approach is prize eligible. Actually many of the top participants follow the same method (train outside Kaggle).</p>\n\n<ol>\n<li>You can directly upload submission.csv</li>\n<li>You can also upload the trained model(s), use a Kaggle notebook for inference, and click submit button there</li>\n</ol>\n\n<p>You only need to submit your training code (and writeup) if you win a prize.</p>",
      "rawMarkdown": "Your approach is prize eligible. Actually many of the top participants follow the same method (train outside Kaggle).\n\n1. You can directly upload submission.csv\n2. You can also upload the trained model(s), use a Kaggle notebook for inference, and click submit button there\n\nYou only need to submit your training code (and writeup) if you win a prize.",
      "votes": null
    },
    {
      "id": "918883",
      "postDate": "07/07/2020 15:04:38",
      "content": "<p>Following up on Sirish's comments, if you have remaining questions after reading sections A.4. and B.11. from the Rules tab, let us know.</p>",
      "rawMarkdown": "Following up on Sirish's comments, if you have remaining questions after reading sections A.4. and B.11. from the Rules tab, let us know.",
      "votes": null
    },
    {
      "id": "919317",
      "postDate": "07/07/2020 19:36:00",
      "content": "<p>Thank you <a href=\"/sirishks\">@sirishks</a> for your answer and <a href=\"/jwebermsk\">@jwebermsk</a> for confirming and clarification!</p>",
      "rawMarkdown": "Thank you @sirishks for your answer and @jwebermsk for confirming and clarification!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 918523,
      "author_name": "sirishks",
      "author_url": "",
      "post_date": "07/07/2020 09:44:47",
      "content": "<p>Your approach is prize eligible. Actually many of the top participants follow the same method (train outside Kaggle).</p>\n\n<ol>\n<li>You can directly upload submission.csv</li>\n<li>You can also upload the trained model(s), use a Kaggle notebook for inference, and click submit button there</li>\n</ol>\n\n<p>You only need to submit your training code (and writeup) if you win a prize.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 918883,
      "author_name": "jwebermsk",
      "author_url": "",
      "post_date": "07/07/2020 15:04:38",
      "content": "<p>Following up on Sirish's comments, if you have remaining questions after reading sections A.4. and B.11. from the Rules tab, let us know.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 919317,
      "author_name": "wrrosa",
      "author_url": "",
      "post_date": "07/07/2020 19:36:00",
      "content": "<p>Thank you <a href=\"/sirishks\">@sirishks</a> for your answer and <a href=\"/jwebermsk\">@jwebermsk</a> for confirming and clarification!</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "918484": "Hello to the organizers ( @juliaelliott @jwebermsk) and Kagglers, \nI developed several SIIM models outside Kaggle Notebooks (my own machines and colab). I didn't use any external data except those mentioned in the forum. Can I, and if so how, use these results/submissions to be prize eligible?",
    "918523": "Your approach is prize eligible. Actually many of the top participants follow the same method (train outside Kaggle).\n\n1. You can directly upload submission.csv\n2. You can also upload the trained model(s), use a Kaggle notebook for inference, and click submit button there\n\nYou only need to submit your training code (and writeup) if you win a prize.",
    "918883": "Following up on Sirish's comments, if you have remaining questions after reading sections A.4. and B.11. from the Rules tab, let us know.",
    "919317": "Thank you @sirishks for your answer and @jwebermsk for confirming and clarification!"
  },
  "source": "meta"
}