{
  "id": 313946,
  "title": "All masks have Retangular/Square shape?  Kaggle Segmentation Masks competitions.",
  "url": "/competitions/hotel-id-to-combat-human-trafficking-2022-fgvc9/discussion/313946",
  "author_name": "Marília Prata",
  "post_date": "2022-03-19T22:24:26.289000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>First of all we' re not talking about face (pandemics masks).</p>\n<h1>Generating Image Segmentation Masks — The Easy Way By Abhiroop Talasila</h1>\n<p>In image segmentation, we have Semantic Segmentation and Instance Segmentation, and different Segmentation models like U-Net, Mask R-CNN. </p>\n<p>\"Most Image Segmentation tutorials use pre-processed and labeled datasets with both ground truth images and masks generated. This is hardly ever the case in real projects when you want to work on a similar task.\"</p>\n<p>\"VGG Image Annotator (VIA):  VIA is an extremely light annotator with support for both images and videos. You can go through the project’s home page to know more. While using VIA, you have two options: either V2 or V3.\"</p>\n<p><a href=\"https://www.kaggle.com/code/paultimothymooney/identification-and-segmentation-of-nuclei-in-cells/notebook\" target=\"_blank\">https://www.kaggle.com/code/paultimothymooney/identification-and-segmentation-of-nuclei-in-cells/notebook</a>  <br>\nIt's always Paul :)</p>\n<p>\"If you’ve done everything right, your end root folder tree should look something like this. The number of files in each mask folder corresponds to the number of objects you’ve annotated in the ground truth image.\"</p>\n<p><a href=\"https://towardsdatascience.com/generating-image-segmentation-masks-the-easy-way-dd4d3656dbd1\" target=\"_blank\">https://towardsdatascience.com/generating-image-segmentation-masks-the-easy-way-dd4d3656dbd1</a></p>\n<h1>Kaggle Segmentation Competitons</h1>\n<p><a href=\"https://www.kaggle.com/c/open-images-2019-instance-segmentation\" target=\"_blank\">https://www.kaggle.com/c/open-images-2019-instance-segmentation</a></p>\n<p><a href=\"https://www.kaggle.com/c/open-images-instance-segmentation-rvc-2020\" target=\"_blank\">https://www.kaggle.com/c/open-images-instance-segmentation-rvc-2020</a></p>",
  "messages": [
    {
      "id": 1729286,
      "postDate": "2022-03-19T22:24:26.290Z",
      "content": "<p>First of all we' re not talking about face (pandemics masks).</p>\n<h1>Generating Image Segmentation Masks — The Easy Way By Abhiroop Talasila</h1>\n<p>In image segmentation, we have Semantic Segmentation and Instance Segmentation, and different Segmentation models like U-Net, Mask R-CNN. </p>\n<p>\"Most Image Segmentation tutorials use pre-processed and labeled datasets with both ground truth images and masks generated. This is hardly ever the case in real projects when you want to work on a similar task.\"</p>\n<p>\"VGG Image Annotator (VIA):  VIA is an extremely light annotator with support for both images and videos. You can go through the project’s home page to know more. While using VIA, you have two options: either V2 or V3.\"</p>\n<p><a href=\"https://www.kaggle.com/code/paultimothymooney/identification-and-segmentation-of-nuclei-in-cells/notebook\" target=\"_blank\">https://www.kaggle.com/code/paultimothymooney/identification-and-segmentation-of-nuclei-in-cells/notebook</a>  <br>\nIt's always Paul :)</p>\n<p>\"If you’ve done everything right, your end root folder tree should look something like this. The number of files in each mask folder corresponds to the number of objects you’ve annotated in the ground truth image.\"</p>\n<p><a href=\"https://towardsdatascience.com/generating-image-segmentation-masks-the-easy-way-dd4d3656dbd1\" target=\"_blank\">https://towardsdatascience.com/generating-image-segmentation-masks-the-easy-way-dd4d3656dbd1</a></p>\n<h1>Kaggle Segmentation Competitons</h1>\n<p><a href=\"https://www.kaggle.com/c/open-images-2019-instance-segmentation\" target=\"_blank\">https://www.kaggle.com/c/open-images-2019-instance-segmentation</a></p>\n<p><a href=\"https://www.kaggle.com/c/open-images-instance-segmentation-rvc-2020\" target=\"_blank\">https://www.kaggle.com/c/open-images-instance-segmentation-rvc-2020</a></p>",
      "rawMarkdown": "First of all we' re not talking about face (pandemics masks).\n\n#Generating Image Segmentation Masks — The Easy Way By Abhiroop Talasila\n\nIn image segmentation, we have Semantic Segmentation and Instance Segmentation, and different Segmentation models like U-Net, Mask R-CNN. \n\n\"Most Image Segmentation tutorials use pre-processed and labeled datasets with both ground truth images and masks generated. This is hardly ever the case in real projects when you want to work on a similar task.\"\n\n\"VGG Image Annotator (VIA):  VIA is an extremely light annotator with support for both images and videos. You can go through the project’s home page to know more. While using VIA, you have two options: either V2 or V3.\"\n\nhttps://www.kaggle.com/code/paultimothymooney/identification-and-segmentation-of-nuclei-in-cells/notebook  \nIt's always Paul :)\n\n\"If you’ve done everything right, your end root folder tree should look something like this. The number of files in each mask folder corresponds to the number of objects you’ve annotated in the ground truth image.\"\n\nhttps://towardsdatascience.com/generating-image-segmentation-masks-the-easy-way-dd4d3656dbd1\n\n\n#Kaggle Segmentation Competitons\n\nhttps://www.kaggle.com/c/open-images-2019-instance-segmentation\n\nhttps://www.kaggle.com/c/open-images-instance-segmentation-rvc-2020",
      "votes": 3
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1729286": "First of all we' re not talking about face (pandemics masks).\n\n#Generating Image Segmentation Masks — The Easy Way By Abhiroop Talasila\n\nIn image segmentation, we have Semantic Segmentation and Instance Segmentation, and different Segmentation models like U-Net, Mask R-CNN. \n\n\"Most Image Segmentation tutorials use pre-processed and labeled datasets with both ground truth images and masks generated. This is hardly ever the case in real projects when you want to work on a similar task.\"\n\n\"VGG Image Annotator (VIA):  VIA is an extremely light annotator with support for both images and videos. You can go through the project’s home page to know more. While using VIA, you have two options: either V2 or V3.\"\n\nhttps://www.kaggle.com/code/paultimothymooney/identification-and-segmentation-of-nuclei-in-cells/notebook  \nIt's always Paul :)\n\n\"If you’ve done everything right, your end root folder tree should look something like this. The number of files in each mask folder corresponds to the number of objects you’ve annotated in the ground truth image.\"\n\nhttps://towardsdatascience.com/generating-image-segmentation-masks-the-easy-way-dd4d3656dbd1\n\n\n#Kaggle Segmentation Competitons\n\nhttps://www.kaggle.com/c/open-images-2019-instance-segmentation\n\nhttps://www.kaggle.com/c/open-images-instance-segmentation-rvc-2020"
  }
}