{
  "id": 5392,
  "title": "code submission as a zip file",
  "url": "/competitions/multi-modal-gesture-recognition/discussion/5392",
  "author_name": "",
  "post_date": "2013-08-14T04:52:05.517Z",
  "votes": null,
  "comment_count": 1,
  "views": 673,
  "content": "<p>Hi Xavier,</p>\n<p><span style=\"line-height: 1.4\">Do we submit the code via the same submission webpage where we also submit our prediction CSV file and the model ?</span></p>\n<p>Our code comprises of separate modules written in Linux binary, matlab/octave code, and shell scripts. We will provide a README to describe each module and how to run each in succession to generate the final prediction CSV file. In the README, we will also specify all dependency 3rd party code+libraries that we use, e.g OpenCV, ffmpeg, SVM-light, Matlab, etc.</p>\n<p>I am assuming we can organize all these code into directories that are meaningfully named, and then zip everything up into a single zip file.</p>\n<p>telepoints</p>\n<p>&nbsp;</p>",
  "messages": [
    {
      "id": "28664",
      "postDate": "08/14/2013 04:52:05",
      "content": "<p>Hi Xavier,</p>\n<p><span style=\"line-height: 1.4\">Do we submit the code via the same submission webpage where we also submit our prediction CSV file and the model ?</span></p>\n<p>Our code comprises of separate modules written in Linux binary, matlab/octave code, and shell scripts. We will provide a README to describe each module and how to run each in succession to generate the final prediction CSV file. In the README, we will also specify all dependency 3rd party code+libraries that we use, e.g OpenCV, ffmpeg, SVM-light, Matlab, etc.</p>\n<p>I am assuming we can organize all these code into directories that are meaningfully named, and then zip everything up into a single zip file.</p>\n<p>telepoints</p>\n<p>&nbsp;</p>",
      "rawMarkdown": "",
      "votes": null
    },
    {
      "id": "28684",
      "postDate": "08/14/2013 12:58:17",
      "content": "<p>Dear Kongwah,</p>\n<p>yes, you should upload the code on the Kaggle platform, but not as a prediction. You can find an option to upload the model (code+any extra file).</p>\n<p>Thanks.</p>\n<p>Xavier</p>",
      "rawMarkdown": "",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 28684,
      "author_name": "xbaro100272",
      "author_url": "",
      "post_date": "08/14/2013 12:58:17",
      "content": "<p>Dear Kongwah,</p>\n<p>yes, you should upload the code on the Kaggle platform, but not as a prediction. You can find an option to upload the model (code+any extra file).</p>\n<p>Thanks.</p>\n<p>Xavier</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "28664": "",
    "28684": ""
  },
  "source": "meta"
}