{
  "id": 104397,
  "title": "External Data Disclosure Thread",
  "url": "/competitions/understanding_cloud_organization/discussion/104397",
  "author_name": "",
  "post_date": "2019-08-16T15:09:22.137758700Z",
  "votes": 5,
  "comment_count": 21,
  "views": 0,
  "content": "<p>Please use this thread to list the external data you are planning to use. Once a data source has been posted the first time, it does not need to be posted again by other competitors planning to use the data. You must post here one week prior to competition close.</p>",
  "messages": [
    {
      "id": "600798",
      "postDate": "08/16/2019 15:09:22",
      "content": "<p>Please use this thread to list the external data you are planning to use. Once a data source has been posted the first time, it does not need to be posted again by other competitors planning to use the data. You must post here one week prior to competition close.</p>",
      "rawMarkdown": "Please use this thread to list the external data you are planning to use. Once a data source has been posted the first time, it does not need to be posted again by other competitors planning to use the data. You must post here one week prior to competition close.",
      "votes": null
    },
    {
      "id": "601049",
      "postDate": "08/17/2019 00:19:59",
      "content": "<p><a href=\"https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset\">https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset</a></p>\n\n<p><a href=\"https://github.com/osmr/imgclsmob\">https://github.com/osmr/imgclsmob</a></p>",
      "rawMarkdown": "https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset\n\nhttps://github.com/osmr/imgclsmob",
      "votes": null
    },
    {
      "id": "605093",
      "postDate": "08/22/2019 03:58:16",
      "content": "<p>I will use cloudy data from <a href=\"https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data\">https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data</a></p>",
      "rawMarkdown": "I will use cloudy data from https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data",
      "votes": null
    },
    {
      "id": "619920",
      "postDate": "09/06/2019 18:54:25",
      "content": "<p>I am using <a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "rawMarkdown": "I am using https://github.com/qubvel/segmentation_models.pytorch",
      "votes": null
    },
    {
      "id": "632298",
      "postDate": "09/23/2019 12:49:20",
      "content": "<p><a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "rawMarkdown": "https://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
      "votes": null
    },
    {
      "id": "633415",
      "postDate": "09/24/2019 21:51:04",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models\">https://github.com/qubvel/segmentation_models</a>\n<a href=\"https://github.com/karolzak/keras-unet\">https://github.com/karolzak/keras-unet</a>\n<a href=\"https://github.com/zhixuhao/unet\">https://github.com/zhixuhao/unet</a>\n<a href=\"https://github.com/qubvel/tta_wrapper\">https://github.com/qubvel/tta_wrapper</a>\n<a href=\"https://github.com/qubvel/efficientnet\">https://github.com/qubvel/efficientnet</a>\n<a href=\"https://github.com/CyberZHG/keras-gradient-accumulation\">https://github.com/CyberZHG/keras-gradient-accumulation</a></p>",
      "rawMarkdown": "https://github.com/qubvel/segmentation_models\nhttps://github.com/karolzak/keras-unet\nhttps://github.com/zhixuhao/unet\nhttps://github.com/qubvel/tta_wrapper\nhttps://github.com/qubvel/efficientnet\nhttps://github.com/CyberZHG/keras-gradient-accumulation",
      "votes": null
    },
    {
      "id": "649415",
      "postDate": "10/15/2019 10:09:58",
      "content": "<p><a href=\"https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\">https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5</a></p>",
      "rawMarkdown": "https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5",
      "votes": null
    },
    {
      "id": "649626",
      "postDate": "10/15/2019 15:31:18",
      "content": "<p>This is quite useful. Thanks</p>",
      "rawMarkdown": "This is quite useful. Thanks",
      "votes": null
    },
    {
      "id": "657686",
      "postDate": "10/25/2019 11:12:44",
      "content": "<p>I may use data from <a href=\"https://worldview.earthdata.nasa.gov/\">https://worldview.earthdata.nasa.gov/</a></p>",
      "rawMarkdown": "I may use data from https://worldview.earthdata.nasa.gov/",
      "votes": null
    },
    {
      "id": "658852",
      "postDate": "10/26/2019 16:25:08",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "rawMarkdown": "https://github.com/qubvel/segmentation_models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
      "votes": null
    },
    {
      "id": "664382",
      "postDate": "11/03/2019 15:49:58",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "rawMarkdown": "https://github.com/qubvel/segmentation_models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
      "votes": null
    },
    {
      "id": "668047",
      "postDate": "11/07/2019 23:00:59",
      "content": "<p>Pretrained Model:\n<a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a>\n<a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "rawMarkdown": "Pretrained Model:\nhttps://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch\nhttps://github.com/qubvel/segmentation_models.pytorch",
      "votes": null
    },
    {
      "id": "668252",
      "postDate": "11/08/2019 07:03:53",
      "content": "<p><a href=\"https://github.com/qubvel/classification_models\">https://github.com/qubvel/classification_models</a></p>",
      "rawMarkdown": "https://github.com/qubvel/classification_models",
      "votes": null
    },
    {
      "id": "669124",
      "postDate": "11/09/2019 14:23:05",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "rawMarkdown": "https://github.com/qubvel/segmentation_models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
      "votes": null
    },
    {
      "id": "669306",
      "postDate": "11/09/2019 21:05:53",
      "content": "<p>ImageNet data via:\n<a href=\"https://github.com/qubvel/segmentation_models\">https://github.com/qubvel/segmentation_models</a>\n<a href=\"https://keras.io/applications/\">https://keras.io/applications/</a>\n<a href=\"https://pypi.org/project/keras-efficientnets/\">https://pypi.org/project/keras-efficientnets/</a></p>",
      "rawMarkdown": "ImageNet data via:\nhttps://github.com/qubvel/segmentation_models\nhttps://keras.io/applications/\nhttps://pypi.org/project/keras-efficientnets/",
      "votes": null
    },
    {
      "id": "670260",
      "postDate": "11/11/2019 08:26:58",
      "content": "<p><a href=\"https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py\">https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py</a> \n<a href=\"https://www.zooniverse.org/projects/raspstephan/sugar-flower-fish-or-gravel/classify\">https://www.zooniverse.org/projects/raspstephan/sugar-flower-fish-or-gravel/classify</a> \n<a href=\"https://github.com/facebookresearch/WSL-Images/blob/master/hubconf.py\">https://github.com/facebookresearch/WSL-Images/blob/master/hubconf.py</a></p>",
      "rawMarkdown": "https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py \nhttps://www.zooniverse.org/projects/raspstephan/sugar-flower-fish-or-gravel/classify \nhttps://github.com/facebookresearch/WSL-Images/blob/master/hubconf.py",
      "votes": null
    },
    {
      "id": "670402",
      "postDate": "11/11/2019 12:29:28",
      "content": "<p><a href=\"https://github.com/adobe/antialiased-cnns\">https://github.com/adobe/antialiased-cnns</a></p>",
      "rawMarkdown": "https://github.com/adobe/antialiased-cnns",
      "votes": null
    },
    {
      "id": "670722",
      "postDate": "11/11/2019 19:27:04",
      "content": "<p>I may use data from:\nLandsat (<a href=\"https://landsat.gsfc.nasa.gov/data/where-to-get-data/\">https://landsat.gsfc.nasa.gov/data/where-to-get-data/</a>)\nSentinel (<a href=\"https://sentinel.esa.int/web/sentinel/sentinel-data-access\">https://sentinel.esa.int/web/sentinel/sentinel-data-access</a>)\nCOCO (<a href=\"http://cocodataset.org/\">http://cocodataset.org/</a>)\nPascal VOC (<a href=\"http://host.robots.ox.ac.uk/pascal/VOC/\">http://host.robots.ox.ac.uk/pascal/VOC/</a>)</p>\n\n<p>I may use code from:\n<a href=\"https://github.com/lalonderodney/SegCaps\">https://github.com/lalonderodney/SegCaps</a>\n<a href=\"https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer\">https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer</a></p>",
      "rawMarkdown": "I may use data from:\nLandsat (https://landsat.gsfc.nasa.gov/data/where-to-get-data/)\nSentinel (https://sentinel.esa.int/web/sentinel/sentinel-data-access)\nCOCO (http://cocodataset.org/)\nPascal VOC (http://host.robots.ox.ac.uk/pascal/VOC/)\n\n\nI may use code from:\nhttps://github.com/lalonderodney/SegCaps\nhttps://github.com/lessw2020/Ranger-Deep-Learning-Optimizer",
      "votes": null
    },
    {
      "id": "673466",
      "postDate": "11/15/2019 02:23:53",
      "content": "<p>keras segmentation models: <a href=\"https://github.com/qubvel/segmentation_models\">https://github.com/qubvel/segmentation_models</a></p>",
      "rawMarkdown": "keras segmentation models: https://github.com/qubvel/segmentation_models",
      "votes": null
    },
    {
      "id": "673818",
      "postDate": "11/15/2019 14:22:24",
      "content": "<p><a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a>\n<a href=\"https://pytorch.org/docs/stable/torchvision/index.html\">https://pytorch.org/docs/stable/torchvision/index.html</a>\n<a href=\"https://github.com/catalyst-team/catalyst\">https://github.com/catalyst-team/catalyst</a>\n<a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "rawMarkdown": "https://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch\nhttps://pytorch.org/docs/stable/torchvision/index.html\nhttps://github.com/catalyst-team/catalyst\nhttps://github.com/qubvel/segmentation_models.pytorch",
      "votes": null
    },
    {
      "id": "675744",
      "postDate": "11/18/2019 13:46:29",
      "content": "<p><a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\nmodels trained from imagenet</p>",
      "rawMarkdown": "https://github.com/Cadene/pretrained-models.pytorch\nmodels trained from imagenet",
      "votes": null
    },
    {
      "id": "676042",
      "postDate": "11/18/2019 23:30:16",
      "content": "<p><a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a>\n<a href=\"https://github.com/catalyst-team/catalyst\">https://github.com/catalyst-team/catalyst</a>\n<a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "rawMarkdown": "https://github.com/lukemelas/EfficientNet-PyTorch\nhttps://github.com/catalyst-team/catalyst\nhttps://github.com/qubvel/segmentation_models.pytorch",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 601049,
      "author_name": "thoffma",
      "author_url": "",
      "post_date": "08/17/2019 00:19:59",
      "content": "<p><a href=\"https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset\">https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset</a></p>\n\n<p><a href=\"https://github.com/osmr/imgclsmob\">https://github.com/osmr/imgclsmob</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 605093,
      "author_name": "mukul1904",
      "author_url": "",
      "post_date": "08/22/2019 03:58:16",
      "content": "<p>I will use cloudy data from <a href=\"https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data\">https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 619920,
      "author_name": "joonl04",
      "author_url": "",
      "post_date": "09/06/2019 18:54:25",
      "content": "<p>I am using <a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 632298,
      "author_name": "tikutiku",
      "author_url": "",
      "post_date": "09/23/2019 12:49:20",
      "content": "<p><a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 633415,
      "author_name": "dimitreoliveira",
      "author_url": "",
      "post_date": "09/24/2019 21:51:04",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models\">https://github.com/qubvel/segmentation_models</a>\n<a href=\"https://github.com/karolzak/keras-unet\">https://github.com/karolzak/keras-unet</a>\n<a href=\"https://github.com/zhixuhao/unet\">https://github.com/zhixuhao/unet</a>\n<a href=\"https://github.com/qubvel/tta_wrapper\">https://github.com/qubvel/tta_wrapper</a>\n<a href=\"https://github.com/qubvel/efficientnet\">https://github.com/qubvel/efficientnet</a>\n<a href=\"https://github.com/CyberZHG/keras-gradient-accumulation\">https://github.com/CyberZHG/keras-gradient-accumulation</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 649415,
      "author_name": "dslate",
      "author_url": "",
      "post_date": "10/15/2019 10:09:58",
      "content": "<p><a href=\"https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\">https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 649626,
      "author_name": "yixinchen1",
      "author_url": "",
      "post_date": "10/15/2019 15:31:18",
      "content": "<p>This is quite useful. Thanks</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 657686,
      "author_name": "mnpinto",
      "author_url": "",
      "post_date": "10/25/2019 11:12:44",
      "content": "<p>I may use data from <a href=\"https://worldview.earthdata.nasa.gov/\">https://worldview.earthdata.nasa.gov/</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 658852,
      "author_name": "idv2005",
      "author_url": "",
      "post_date": "10/26/2019 16:25:08",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 664382,
      "author_name": "vanche",
      "author_url": "",
      "post_date": "11/03/2019 15:49:58",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 668047,
      "author_name": "quanqiu",
      "author_url": "",
      "post_date": "11/07/2019 23:00:59",
      "content": "<p>Pretrained Model:\n<a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a>\n<a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 668252,
      "author_name": "harangdev",
      "author_url": "",
      "post_date": "11/08/2019 07:03:53",
      "content": "<p><a href=\"https://github.com/qubvel/classification_models\">https://github.com/qubvel/classification_models</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 669124,
      "author_name": "idv2005",
      "author_url": "",
      "post_date": "11/09/2019 14:23:05",
      "content": "<p><a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 669306,
      "author_name": "cdeotte",
      "author_url": "",
      "post_date": "11/09/2019 21:05:53",
      "content": "<p>ImageNet data via:\n<a href=\"https://github.com/qubvel/segmentation_models\">https://github.com/qubvel/segmentation_models</a>\n<a href=\"https://keras.io/applications/\">https://keras.io/applications/</a>\n<a href=\"https://pypi.org/project/keras-efficientnets/\">https://pypi.org/project/keras-efficientnets/</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 670260,
      "author_name": "bestfitting",
      "author_url": "",
      "post_date": "11/11/2019 08:26:58",
      "content": "<p><a href=\"https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py\">https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py</a> \n<a href=\"https://www.zooniverse.org/projects/raspstephan/sugar-flower-fish-or-gravel/classify\">https://www.zooniverse.org/projects/raspstephan/sugar-flower-fish-or-gravel/classify</a> \n<a href=\"https://github.com/facebookresearch/WSL-Images/blob/master/hubconf.py\">https://github.com/facebookresearch/WSL-Images/blob/master/hubconf.py</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 670402,
      "author_name": "momi64",
      "author_url": "",
      "post_date": "11/11/2019 12:29:28",
      "content": "<p><a href=\"https://github.com/adobe/antialiased-cnns\">https://github.com/adobe/antialiased-cnns</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 670722,
      "author_name": "simonwenkel",
      "author_url": "",
      "post_date": "11/11/2019 19:27:04",
      "content": "<p>I may use data from:\nLandsat (<a href=\"https://landsat.gsfc.nasa.gov/data/where-to-get-data/\">https://landsat.gsfc.nasa.gov/data/where-to-get-data/</a>)\nSentinel (<a href=\"https://sentinel.esa.int/web/sentinel/sentinel-data-access\">https://sentinel.esa.int/web/sentinel/sentinel-data-access</a>)\nCOCO (<a href=\"http://cocodataset.org/\">http://cocodataset.org/</a>)\nPascal VOC (<a href=\"http://host.robots.ox.ac.uk/pascal/VOC/\">http://host.robots.ox.ac.uk/pascal/VOC/</a>)</p>\n\n<p>I may use code from:\n<a href=\"https://github.com/lalonderodney/SegCaps\">https://github.com/lalonderodney/SegCaps</a>\n<a href=\"https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer\">https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 673466,
      "author_name": "micheomaano",
      "author_url": "",
      "post_date": "11/15/2019 02:23:53",
      "content": "<p>keras segmentation models: <a href=\"https://github.com/qubvel/segmentation_models\">https://github.com/qubvel/segmentation_models</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 673818,
      "author_name": "dathudeptrai",
      "author_url": "",
      "post_date": "11/15/2019 14:22:24",
      "content": "<p><a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\n<a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a>\n<a href=\"https://pytorch.org/docs/stable/torchvision/index.html\">https://pytorch.org/docs/stable/torchvision/index.html</a>\n<a href=\"https://github.com/catalyst-team/catalyst\">https://github.com/catalyst-team/catalyst</a>\n<a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 675744,
      "author_name": "hengck23",
      "author_url": "",
      "post_date": "11/18/2019 13:46:29",
      "content": "<p><a href=\"https://github.com/Cadene/pretrained-models.pytorch\">https://github.com/Cadene/pretrained-models.pytorch</a>\nmodels trained from imagenet</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 676042,
      "author_name": "spsancti",
      "author_url": "",
      "post_date": "11/18/2019 23:30:16",
      "content": "<p><a href=\"https://github.com/lukemelas/EfficientNet-PyTorch\">https://github.com/lukemelas/EfficientNet-PyTorch</a>\n<a href=\"https://github.com/catalyst-team/catalyst\">https://github.com/catalyst-team/catalyst</a>\n<a href=\"https://github.com/qubvel/segmentation_models.pytorch\">https://github.com/qubvel/segmentation_models.pytorch</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "600798": "Please use this thread to list the external data you are planning to use. Once a data source has been posted the first time, it does not need to be posted again by other competitors planning to use the data. You must post here one week prior to competition close.",
    "601049": "https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset\n\nhttps://github.com/osmr/imgclsmob",
    "605093": "I will use cloudy data from https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data",
    "619920": "I am using https://github.com/qubvel/segmentation_models.pytorch",
    "632298": "https://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
    "633415": "https://github.com/qubvel/segmentation_models\nhttps://github.com/karolzak/keras-unet\nhttps://github.com/zhixuhao/unet\nhttps://github.com/qubvel/tta_wrapper\nhttps://github.com/qubvel/efficientnet\nhttps://github.com/CyberZHG/keras-gradient-accumulation",
    "649415": "https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5",
    "649626": "This is quite useful. Thanks",
    "657686": "I may use data from https://worldview.earthdata.nasa.gov/",
    "658852": "https://github.com/qubvel/segmentation_models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
    "664382": "https://github.com/qubvel/segmentation_models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
    "668047": "Pretrained Model:\nhttps://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch\nhttps://github.com/qubvel/segmentation_models.pytorch",
    "668252": "https://github.com/qubvel/classification_models",
    "669124": "https://github.com/qubvel/segmentation_models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch",
    "669306": "ImageNet data via:\nhttps://github.com/qubvel/segmentation_models\nhttps://keras.io/applications/\nhttps://pypi.org/project/keras-efficientnets/",
    "670260": "https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py \nhttps://www.zooniverse.org/projects/raspstephan/sugar-flower-fish-or-gravel/classify \nhttps://github.com/facebookresearch/WSL-Images/blob/master/hubconf.py",
    "670402": "https://github.com/adobe/antialiased-cnns",
    "670722": "I may use data from:\nLandsat (https://landsat.gsfc.nasa.gov/data/where-to-get-data/)\nSentinel (https://sentinel.esa.int/web/sentinel/sentinel-data-access)\nCOCO (http://cocodataset.org/)\nPascal VOC (http://host.robots.ox.ac.uk/pascal/VOC/)\n\n\nI may use code from:\nhttps://github.com/lalonderodney/SegCaps\nhttps://github.com/lessw2020/Ranger-Deep-Learning-Optimizer",
    "673466": "keras segmentation models: https://github.com/qubvel/segmentation_models",
    "673818": "https://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/lukemelas/EfficientNet-PyTorch\nhttps://pytorch.org/docs/stable/torchvision/index.html\nhttps://github.com/catalyst-team/catalyst\nhttps://github.com/qubvel/segmentation_models.pytorch",
    "675744": "https://github.com/Cadene/pretrained-models.pytorch\nmodels trained from imagenet",
    "676042": "https://github.com/lukemelas/EfficientNet-PyTorch\nhttps://github.com/catalyst-team/catalyst\nhttps://github.com/qubvel/segmentation_models.pytorch"
  },
  "source": "meta"
}