Per the Competition Rules, External Data is permitted in this competition, but must be posted to this forum thread no later than the Entry Deadline (one week before competition close). You are not required to re-post use of an external dataset if it's already been shared in this thread.
", "messages": [ { "id": "573199", "postDate": "07/11/2019 23:57:28", "content": "Per the Competition Rules, External Data is permitted in this competition, but must be posted to this forum thread no later than the Entry Deadline (one week before competition close). You are not required to re-post use of an external dataset if it's already been shared in this thread.
", "rawMarkdown": "Per the [Competition Rules](https://www.kaggle.com/c/open-images-2019-instance-segmentation/rules), External Data is permitted in this competition, but must be posted to this forum thread no later than the Entry Deadline (one week before competition close). You are not required to re-post use of an external dataset if it's already been shared in this thread.", "votes": null }, { "id": "630940", "postDate": "09/21/2019 03:29:27", "content": "Pretrained models from \n- https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018\n- https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1
", "rawMarkdown": "Pretrained models from \n- https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018\n- https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1", "votes": null }, { "id": "631264", "postDate": "09/21/2019 18:04:15", "content": "mmdetection pretrained models:\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "rawMarkdown": "mmdetection pretrained models:\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "votes": null }, { "id": "631529", "postDate": "09/22/2019 07:23:56", "content": "Pretrained models:\nhttps://github.com/matterport/Mask_RCNN
", "rawMarkdown": "Pretrained models:\nhttps://github.com/matterport/Mask_RCNN", "votes": null }, { "id": "631948", "postDate": "09/23/2019 02:13:30", "content": "Pretrained models:\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/
", "rawMarkdown": "Pretrained models:\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/", "votes": null }, { "id": "632178", "postDate": "09/23/2019 10:25:48", "content": "Open Images Dataset V5\nhttps://storage.googleapis.com/openimages/web/download.html
\n\nmmdetection pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "rawMarkdown": "Open Images Dataset V5\nhttps://storage.googleapis.com/openimages/web/download.html\n\nmmdetection pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "votes": null }, { "id": "632196", "postDate": "09/23/2019 10:54:06", "content": "pretrained model:\nhttps://github.com/open-mmlab/mmdetection/tree/master/configs/htc
", "rawMarkdown": "pretrained model:\nhttps://github.com/open-mmlab/mmdetection/tree/master/configs/htc", "votes": null }, { "id": "632302", "postDate": "09/23/2019 12:55:48", "content": "http://cocodataset.org/#home\nhttp://shuoyang1213.me/WIDERFACE/
", "rawMarkdown": "http://cocodataset.org/#home\nhttp://shuoyang1213.me/WIDERFACE/", "votes": null }, { "id": "632498", "postDate": "09/23/2019 17:13:17", "content": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models\nAnnotations of the other tracks:\nhttps://storage.googleapis.com/openimages/web/challenge2019_downloads.html
", "rawMarkdown": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models\nAnnotations of the other tracks:\nhttps://storage.googleapis.com/openimages/web/challenge2019_downloads.html", "votes": null }, { "id": "632591", "postDate": "09/23/2019 19:16:48", "content": "May or may not use some of the following:
\n\nhttps://storage.cloud.google.com/cloud-tpu-artifacts/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603.data-00000-of-00001
\nhttps://storage.cloud.google.com/cloud-tpu-artifacts/resnet/resnet-nhwc-2018-10-14/model.ckpt-112602.data-00000-of-00001
\nhttps://storage.googleapis.com/openimages/2017_07/oidv2-resnet_v1_101.ckpt.tar.gz
\nhttp://download.tensorflow.org/models/resnet_v1_101_2018_05_04.tar.gz
\nhttp://download.tensorflow.org/models/official/20181001_resnet/checkpoints/resnet_imagenet_v1_fp32_20181001.tar.gz
\nhttp://download.tensorflow.org/models/official/resnet_v2_imagenet_checkpoint.tar.gz
\nhttp://download.tensorflow.org/models/official/resnet_v1_imagenet_checkpoint.tar.gz
\nhttps://github.com/tensorflow/models/tree/master/research/slim
We may use the following external data:
\n\nPretrained models:\n ChainerCV pretrained models https://github.com/chainer/chainercv (links to weight files are available in code, e.g., https://github.com/chainer/chainercv/blob/d8f903acbc6e3369a9871aa175893fa5c0f3946c/chainercv/links/model/resnet/resnet.py#L115-L116)\n deep-residual-networks https://github.com/KaimingHe/deep-residual-networks\n pretrained-models.pytorch https://github.com/Cadene/pretrained-models.pytorch\n SENet https://github.com/hujie-frank/SENet\n TensorFlow-Slim image classification model library https://github.com/tensorflow/models/blob/master/research/slim/README.md
\n\nData (images and annotations):\n ImageNet http://image-net.org/download-images\n LVIS https://www.lvisdataset.org/dataset\n Objects365 https://www.objects365.org/overview.html
", "rawMarkdown": "We may use the following external data:\n\nPretrained models:\n ChainerCV pretrained models https://github.com/chainer/chainercv (links to weight files are available in code, e.g., https://github.com/chainer/chainercv/blob/d8f903acbc6e3369a9871aa175893fa5c0f3946c/chainercv/links/model/resnet/resnet.py#L115-L116)\n deep-residual-networks https://github.com/KaimingHe/deep-residual-networks\n pretrained-models.pytorch https://github.com/Cadene/pretrained-models.pytorch\n SENet https://github.com/hujie-frank/SENet\n TensorFlow-Slim image classification model library https://github.com/tensorflow/models/blob/master/research/slim/README.md\n\nData (images and annotations):\n ImageNet http://image-net.org/download-images\n LVIS https://www.lvisdataset.org/dataset\n Objects365 https://www.objects365.org/overview.html", "votes": null }, { "id": "633409", "postDate": "09/24/2019 21:39:35", "content": "I may or may use some of the following external datasets and/or pretrained models:
\n\nExternal datasets:
\n\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nPascal VOC: https://pjreddie.com/projects/pascal-voc-dataset-mirror/\nImageNet: http://image-net.org/index\nObjects365: https://www.objects365.org\nCOCO: http://cocodataset.org\nLVIS: https://www.lvisdataset.org/dataset\nVisualQA: https://visualqa.org/\nCIFAR-10: http://www.cs.toronto.edu/~kriz/cifar.html\nWiderface: http://shuoyang1213.me/WIDERFACE/index.html
\n\nPretrained weights for classification backbones and object detection models:
\n\nhttps://pytorch.org/docs/stable/torchvision/models.html\nhttps://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-artifacts\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-checkpoints\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/\nhttps://github.com/tensorflow/models\nhttps://github.com/tensorflow/tpu/tree/master/models\nhttps://github.com/chainer/chainercv\nhttps://github.com/fchollet/deep-learning-models/releases/\nhttps://github.com/facebookresearch/maskrcnn-benchmark/blob/master/MODEL_ZOO.md\nhttps://github.com/KaimingHe/deep-residual-networks\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/NVIDIA/ContrastiveLosses4VRD\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/
", "rawMarkdown": "I may or may use some of the following external datasets and/or pretrained models:\n\nExternal datasets:\n\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nPascal VOC: https://pjreddie.com/projects/pascal-voc-dataset-mirror/\nImageNet: http://image-net.org/index\nObjects365: https://www.objects365.org\nCOCO: http://cocodataset.org\nLVIS: https://www.lvisdataset.org/dataset\nVisualQA: https://visualqa.org/\nCIFAR-10: http://www.cs.toronto.edu/~kriz/cifar.html\nWiderface: http://shuoyang1213.me/WIDERFACE/index.html\n\nPretrained weights for classification backbones and object detection models:\n\nhttps://pytorch.org/docs/stable/torchvision/models.html\nhttps://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-artifacts\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-checkpoints\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/\nhttps://github.com/tensorflow/models\nhttps://github.com/tensorflow/tpu/tree/master/models\nhttps://github.com/chainer/chainercv\nhttps://github.com/fchollet/deep-learning-models/releases/\nhttps://github.com/facebookresearch/maskrcnn-benchmark/blob/master/MODEL_ZOO.md\nhttps://github.com/KaimingHe/deep-residual-networks\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/NVIDIA/ContrastiveLosses4VRD\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/", "votes": null }, { "id": "635420", "postDate": "09/27/2019 14:04:19", "content": "I (or members of my team) may or may not use the following external datasets and/or pretrained models:
\n\nDatasets:\nImageNet: http://image-net.org/index\nCOCO: http://cocodataset.org\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html
\n\nPretrained models:\nFast RCNN: https://github.com/rbgirshick/fast-rcnn\nMask RCNN: https://github.com/matterport/Mask_RCNN
", "rawMarkdown": "I (or members of my team) may or may not use the following external datasets and/or pretrained models:\n\nDatasets:\nImageNet: http://image-net.org/index\nCOCO: http://cocodataset.org\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\n\nPretrained models:\nFast RCNN: https://github.com/rbgirshick/fast-rcnn\nMask RCNN: https://github.com/matterport/Mask_RCNN", "votes": null }, { "id": "637321", "postDate": "09/30/2019 23:19:18", "content": "https://github.com/facebookresearch/WSL-Images\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "rawMarkdown": "https://github.com/facebookresearch/WSL-Images\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "votes": null }, { "id": "637982", "postDate": "10/01/2019 12:23:30", "content": "Please @drn01z3 take note that per the competition rules section 7.C \"External Data\"; the used external data must be declared prior to the Entry Deadline (as was reminded in this thread). This deadline was September 24th, 2019.
\n\nPlease make sure to upload results that do not use the WSL-Images dataset (any of the other datasets below in this thread is fine). Failing to follow the competition rules will lead to your entry being disqualified.
", "rawMarkdown": "Please @drn01z3 take note that per the [competition rules](https://www.kaggle.com/c/open-images-2019-instance-segmentation/rules) section 7.C \"External Data\"; the used external data must be declared prior to the Entry Deadline (as was reminded in [this thread](https://www.kaggle.com/c/open-images-2019-instance-segmentation/discussion/109878)). This deadline was September 24th, 2019.\n\nPlease make sure to upload results that do _not_ use the WSL-Images dataset (any of the other datasets below in this thread is fine). Failing to follow the competition rules will lead to your entry being disqualified.", "votes": null }, { "id": "638003", "postDate": "10/01/2019 12:48:05", "content": "For information; there was a discussion in the Object Detection track regarding Objects365 dataset. The topic has be resolved and Objects365 will be allowed.
", "rawMarkdown": "For information; there [was a discussion in the Object Detection track regarding Objects365 dataset](https://www.kaggle.com/c/open-images-2019-object-detection/discussion/110045#637869). The topic has be resolved and Objects365 will be allowed.", "votes": null } ], "comments": [ { "id": 630940, "author_name": "muhammedazamkhan", "author_url": "", "post_date": "09/21/2019 03:29:27", "content": "Pretrained models from \n- https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018\n- https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1
", "votes": null, "replies": [] }, { "id": 631264, "author_name": "idv2005", "author_url": "", "post_date": "09/21/2019 18:04:15", "content": "mmdetection pretrained models:\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "votes": null, "replies": [] }, { "id": 631529, "author_name": "taggatle", "author_url": "", "post_date": "09/22/2019 07:23:56", "content": "Pretrained models:\nhttps://github.com/matterport/Mask_RCNN
", "votes": null, "replies": [] }, { "id": 631948, "author_name": "its7171", "author_url": "", "post_date": "09/23/2019 02:13:30", "content": "Pretrained models:\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/
", "votes": null, "replies": [] }, { "id": 632178, "author_name": "garyanderson", "author_url": "", "post_date": "09/23/2019 10:25:48", "content": "Open Images Dataset V5\nhttps://storage.googleapis.com/openimages/web/download.html
\n\nmmdetection pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "votes": null, "replies": [] }, { "id": 632196, "author_name": "iiyamaiiyama", "author_url": "", "post_date": "09/23/2019 10:54:06", "content": "pretrained model:\nhttps://github.com/open-mmlab/mmdetection/tree/master/configs/htc
", "votes": null, "replies": [] }, { "id": 632302, "author_name": "vadiksadik", "author_url": "", "post_date": "09/23/2019 12:55:48", "content": "http://cocodataset.org/#home\nhttp://shuoyang1213.me/WIDERFACE/
", "votes": null, "replies": [] }, { "id": 632498, "author_name": "hirotoschwert", "author_url": "", "post_date": "09/23/2019 17:13:17", "content": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models\nAnnotations of the other tracks:\nhttps://storage.googleapis.com/openimages/web/challenge2019_downloads.html
", "votes": null, "replies": [] }, { "id": 632591, "author_name": "vecxoz", "author_url": "", "post_date": "09/23/2019 19:16:48", "content": "May or may not use some of the following:
\n\nhttps://storage.cloud.google.com/cloud-tpu-artifacts/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603.data-00000-of-00001
\nhttps://storage.cloud.google.com/cloud-tpu-artifacts/resnet/resnet-nhwc-2018-10-14/model.ckpt-112602.data-00000-of-00001
\nhttps://storage.googleapis.com/openimages/2017_07/oidv2-resnet_v1_101.ckpt.tar.gz
\nhttp://download.tensorflow.org/models/resnet_v1_101_2018_05_04.tar.gz
\nhttp://download.tensorflow.org/models/official/20181001_resnet/checkpoints/resnet_imagenet_v1_fp32_20181001.tar.gz
\nhttp://download.tensorflow.org/models/official/resnet_v2_imagenet_checkpoint.tar.gz
\nhttp://download.tensorflow.org/models/official/resnet_v1_imagenet_checkpoint.tar.gz
\nhttps://github.com/tensorflow/models/tree/master/research/slim
We may use the following external data:
\n\nPretrained models:\n ChainerCV pretrained models https://github.com/chainer/chainercv (links to weight files are available in code, e.g., https://github.com/chainer/chainercv/blob/d8f903acbc6e3369a9871aa175893fa5c0f3946c/chainercv/links/model/resnet/resnet.py#L115-L116)\n deep-residual-networks https://github.com/KaimingHe/deep-residual-networks\n pretrained-models.pytorch https://github.com/Cadene/pretrained-models.pytorch\n SENet https://github.com/hujie-frank/SENet\n TensorFlow-Slim image classification model library https://github.com/tensorflow/models/blob/master/research/slim/README.md
\n\nData (images and annotations):\n ImageNet http://image-net.org/download-images\n LVIS https://www.lvisdataset.org/dataset\n Objects365 https://www.objects365.org/overview.html
", "votes": null, "replies": [ { "id": 638003, "author_name": "benenson", "author_url": "", "post_date": "10/01/2019 12:48:05", "content": "For information; there was a discussion in the Object Detection track regarding Objects365 dataset. The topic has be resolved and Objects365 will be allowed.
", "votes": null, "replies": [] } ] }, { "id": 633409, "author_name": "aagapi", "author_url": "", "post_date": "09/24/2019 21:39:35", "content": "I may or may use some of the following external datasets and/or pretrained models:
\n\nExternal datasets:
\n\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nPascal VOC: https://pjreddie.com/projects/pascal-voc-dataset-mirror/\nImageNet: http://image-net.org/index\nObjects365: https://www.objects365.org\nCOCO: http://cocodataset.org\nLVIS: https://www.lvisdataset.org/dataset\nVisualQA: https://visualqa.org/\nCIFAR-10: http://www.cs.toronto.edu/~kriz/cifar.html\nWiderface: http://shuoyang1213.me/WIDERFACE/index.html
\n\nPretrained weights for classification backbones and object detection models:
\n\nhttps://pytorch.org/docs/stable/torchvision/models.html\nhttps://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-artifacts\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-checkpoints\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/\nhttps://github.com/tensorflow/models\nhttps://github.com/tensorflow/tpu/tree/master/models\nhttps://github.com/chainer/chainercv\nhttps://github.com/fchollet/deep-learning-models/releases/\nhttps://github.com/facebookresearch/maskrcnn-benchmark/blob/master/MODEL_ZOO.md\nhttps://github.com/KaimingHe/deep-residual-networks\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/NVIDIA/ContrastiveLosses4VRD\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/
", "votes": null, "replies": [] }, { "id": 635420, "author_name": "jmdavison", "author_url": "", "post_date": "09/27/2019 14:04:19", "content": "I (or members of my team) may or may not use the following external datasets and/or pretrained models:
\n\nDatasets:\nImageNet: http://image-net.org/index\nCOCO: http://cocodataset.org\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html
\n\nPretrained models:\nFast RCNN: https://github.com/rbgirshick/fast-rcnn\nMask RCNN: https://github.com/matterport/Mask_RCNN
", "votes": null, "replies": [] }, { "id": 637321, "author_name": "drn01z3", "author_url": "", "post_date": "09/30/2019 23:19:18", "content": "https://github.com/facebookresearch/WSL-Images\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "votes": null, "replies": [ { "id": 637982, "author_name": "benenson", "author_url": "", "post_date": "10/01/2019 12:23:30", "content": "Please @drn01z3 take note that per the competition rules section 7.C \"External Data\"; the used external data must be declared prior to the Entry Deadline (as was reminded in this thread). This deadline was September 24th, 2019.
\n\nPlease make sure to upload results that do not use the WSL-Images dataset (any of the other datasets below in this thread is fine). Failing to follow the competition rules will lead to your entry being disqualified.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "573199": "Per the [Competition Rules](https://www.kaggle.com/c/open-images-2019-instance-segmentation/rules), External Data is permitted in this competition, but must be posted to this forum thread no later than the Entry Deadline (one week before competition close). You are not required to re-post use of an external dataset if it's already been shared in this thread.", "630940": "Pretrained models from \n- https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018\n- https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1", "631264": "mmdetection pretrained models:\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "631529": "Pretrained models:\nhttps://github.com/matterport/Mask_RCNN", "631948": "Pretrained models:\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/", "632178": "Open Images Dataset V5\nhttps://storage.googleapis.com/openimages/web/download.html\n\nmmdetection pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "632196": "pretrained model:\nhttps://github.com/open-mmlab/mmdetection/tree/master/configs/htc", "632302": "http://cocodataset.org/#home\nhttp://shuoyang1213.me/WIDERFACE/", "632498": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models\nAnnotations of the other tracks:\nhttps://storage.googleapis.com/openimages/web/challenge2019_downloads.html", "632591": "May or may not use some of the following: \n\nhttps://storage.cloud.google.com/cloud-tpu-artifacts/resnet/resnet-nhwc-2018-02-07/model.ckpt-112603.data-00000-of-00001 \nhttps://storage.cloud.google.com/cloud-tpu-artifacts/resnet/resnet-nhwc-2018-10-14/model.ckpt-112602.data-00000-of-00001 \nhttps://storage.googleapis.com/openimages/2017_07/oidv2-resnet_v1_101.ckpt.tar.gz \nhttp://download.tensorflow.org/models/resnet_v1_101_2018_05_04.tar.gz \nhttp://download.tensorflow.org/models/official/20181001_resnet/checkpoints/resnet_imagenet_v1_fp32_20181001.tar.gz \nhttp://download.tensorflow.org/models/official/resnet_v2_imagenet_checkpoint.tar.gz \nhttp://download.tensorflow.org/models/official/resnet_v1_imagenet_checkpoint.tar.gz \nhttps://github.com/tensorflow/models/tree/master/research/slim", "633077": "We may use the following external data:\n\nPretrained models:\n ChainerCV pretrained models https://github.com/chainer/chainercv (links to weight files are available in code, e.g., https://github.com/chainer/chainercv/blob/d8f903acbc6e3369a9871aa175893fa5c0f3946c/chainercv/links/model/resnet/resnet.py#L115-L116)\n deep-residual-networks https://github.com/KaimingHe/deep-residual-networks\n pretrained-models.pytorch https://github.com/Cadene/pretrained-models.pytorch\n SENet https://github.com/hujie-frank/SENet\n TensorFlow-Slim image classification model library https://github.com/tensorflow/models/blob/master/research/slim/README.md\n\nData (images and annotations):\n ImageNet http://image-net.org/download-images\n LVIS https://www.lvisdataset.org/dataset\n Objects365 https://www.objects365.org/overview.html", "633409": "I may or may use some of the following external datasets and/or pretrained models:\n\nExternal datasets:\n\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nPascal VOC: https://pjreddie.com/projects/pascal-voc-dataset-mirror/\nImageNet: http://image-net.org/index\nObjects365: https://www.objects365.org\nCOCO: http://cocodataset.org\nLVIS: https://www.lvisdataset.org/dataset\nVisualQA: https://visualqa.org/\nCIFAR-10: http://www.cs.toronto.edu/~kriz/cifar.html\nWiderface: http://shuoyang1213.me/WIDERFACE/index.html\n\nPretrained weights for classification backbones and object detection models:\n\nhttps://pytorch.org/docs/stable/torchvision/models.html\nhttps://github.com/Cadene/pretrained-models.pytorch\nhttps://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md\nhttps://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-artifacts\nhttps://console.cloud.google.com/storage/browser/cloud-tpu-checkpoints\nhttps://pjreddie.com/darknet/yolo/\nhttps://keras.io/applications/\nhttps://github.com/tensorflow/models\nhttps://github.com/tensorflow/tpu/tree/master/models\nhttps://github.com/chainer/chainercv\nhttps://github.com/fchollet/deep-learning-models/releases/\nhttps://github.com/facebookresearch/maskrcnn-benchmark/blob/master/MODEL_ZOO.md\nhttps://github.com/KaimingHe/deep-residual-networks\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/NVIDIA/ContrastiveLosses4VRD\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/", "635420": "I (or members of my team) may or may not use the following external datasets and/or pretrained models:\n\nDatasets:\nImageNet: http://image-net.org/index\nCOCO: http://cocodataset.org\nOpenimages v5: https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\n\nPretrained models:\nFast RCNN: https://github.com/rbgirshick/fast-rcnn\nMask RCNN: https://github.com/matterport/Mask_RCNN", "637321": "https://github.com/facebookresearch/WSL-Images\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "637982": "Please @drn01z3 take note that per the [competition rules](https://www.kaggle.com/c/open-images-2019-instance-segmentation/rules) section 7.C \"External Data\"; the used external data must be declared prior to the Entry Deadline (as was reminded in [this thread](https://www.kaggle.com/c/open-images-2019-instance-segmentation/discussion/109878)). This deadline was September 24th, 2019.\n\nPlease make sure to upload results that do _not_ use the WSL-Images dataset (any of the other datasets below in this thread is fine). Failing to follow the competition rules will lead to your entry being disqualified.", "638003": "For information; there [was a discussion in the Object Detection track regarding Objects365 dataset](https://www.kaggle.com/c/open-images-2019-object-detection/discussion/110045#637869). The topic has be resolved and Objects365 will be allowed." }, "source": "meta" }