{
  "id": 273817,
  "title": "Final Day Instructions ",
  "url": "/competitions/computer-vision-training-fcis-21/discussion/273817",
  "author_name": "Mohammad Essam",
  "post_date": "2021-09-22T17:39:14.385000",
  "votes": 9,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hello Everyone ! <br>\nyou had a great performance so far , according to the current leaderboard we have 26 teams that got more than 0.5 IOU . I hope more teams could join them to guarantee their certificates . <br>\nas we are 1 day away from the competition closing , here is the instructions of final day : <br>\n1 - Competition will be closed tomorrow <strong>12 am</strong> (at the midnight) . </p>\n<p>2-  you should select 2 submissions form your submissions to be judged by the hidden test data ( which we will calculate your final score with) , if you didn't choose submissions kaggle will choose automatically the 2 submissions with the highest IOU on Public leaderboard . </p>\n<p>3 - for those that can't implement Unet you can find the model <a href=\"https://github.com/zhixuhao/unet/blob/master/model.py\" target=\"_blank\">here</a> or use segmentation_models package to get Unet in one line or any other segmentation model  ( but make sure that weights = None) .</p>",
  "messages": [
    {
      "id": 1520913,
      "postDate": "2021-09-22T17:39:14.387Z",
      "content": "<p>Hello Everyone ! <br>\nyou had a great performance so far , according to the current leaderboard we have 26 teams that got more than 0.5 IOU . I hope more teams could join them to guarantee their certificates . <br>\nas we are 1 day away from the competition closing , here is the instructions of final day : <br>\n1 - Competition will be closed tomorrow <strong>12 am</strong> (at the midnight) . </p>\n<p>2-  you should select 2 submissions form your submissions to be judged by the hidden test data ( which we will calculate your final score with) , if you didn't choose submissions kaggle will choose automatically the 2 submissions with the highest IOU on Public leaderboard . </p>\n<p>3 - for those that can't implement Unet you can find the model <a href=\"https://github.com/zhixuhao/unet/blob/master/model.py\" target=\"_blank\">here</a> or use segmentation_models package to get Unet in one line or any other segmentation model  ( but make sure that weights = None) .</p>",
      "rawMarkdown": "Hello Everyone ! \nyou had a great performance so far , according to the current leaderboard we have 26 teams that got more than 0.5 IOU . I hope more teams could join them to guarantee their certificates . \nas we are 1 day away from the competition closing , here is the instructions of final day : \n1 - Competition will be closed tomorrow **12 am** (at the midnight) . \n\n\n2-  you should select 2 submissions form your submissions to be judged by the hidden test data ( which we will calculate your final score with) , if you didn't choose submissions kaggle will choose automatically the 2 submissions with the highest IOU on Public leaderboard . \n\n\n3 - for those that can't implement Unet you can find the model [here](https://github.com/zhixuhao/unet/blob/master/model.py) or use segmentation_models package to get Unet in one line or any other segmentation model  ( but make sure that weights = None) .\n",
      "votes": 9
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1520913": "Hello Everyone ! \nyou had a great performance so far , according to the current leaderboard we have 26 teams that got more than 0.5 IOU . I hope more teams could join them to guarantee their certificates . \nas we are 1 day away from the competition closing , here is the instructions of final day : \n1 - Competition will be closed tomorrow **12 am** (at the midnight) . \n\n\n2-  you should select 2 submissions form your submissions to be judged by the hidden test data ( which we will calculate your final score with) , if you didn't choose submissions kaggle will choose automatically the 2 submissions with the highest IOU on Public leaderboard . \n\n\n3 - for those that can't implement Unet you can find the model [here](https://github.com/zhixuhao/unet/blob/master/model.py) or use segmentation_models package to get Unet in one line or any other segmentation model  ( but make sure that weights = None) .\n"
  }
}