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": 600718, "postDate": "2019-08-16T13:46:56.573Z", "content": "If we are using an external pretrained model (like the ones xhlulu posted), and not the external data itself, do we have to post it here?
", "rawMarkdown": "If we are using an external pretrained model (like the ones xhlulu posted), and not the external data itself, do we have to post it here?", "votes": 6 }, { "id": 633408, "postDate": "2019-09-24T21:39:04.317Z", "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/\n", "votes": 3 }, { "id": 632153, "postDate": "2019-09-23T09:36:28.497Z", "content": "We use some of the following models or datasets:\n1. PaddlePaddle image classification model zoo:\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\n2. PaddleDetection model zoo:\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\n3. Coco dataset:\nhttp://cocodataset.org/#home\n4. Objects365 dataset:\nhttps://www.objects365.org/download.html
", "rawMarkdown": "We use some of the following models or datasets:\n1. PaddlePaddle image classification model zoo:\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\n2. PaddleDetection model zoo:\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\n3. Coco dataset:\nhttp://cocodataset.org/#home\n4. Objects365 dataset:\nhttps://www.objects365.org/download.html", "votes": 1 }, { "id": 631696, "postDate": "2019-09-22T14:00:58.430Z", "content": "http://shuoyang1213.me/WIDERFACE/
", "rawMarkdown": "http://shuoyang1213.me/WIDERFACE/", "votes": 1 }, { "id": 542388, "postDate": "2019-06-03T20:11:25.780Z", "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-object-detection/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": 1 }, { "id": 633053, "postDate": "2019-09-24T11:39:43.530Z", "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", "replies": [ { "id": 637986, "postDate": "2019-10-01T12:30:39.143Z", "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); which was on September 24th.
\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-object-detection/discussion/109879#latest-632117)); which was on September 24th.\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." } ] }, { "id": 635367, "postDate": "2019-09-27T12:40:34.823Z", "content": "https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018
", "rawMarkdown": "https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018" }, { "id": 633426, "postDate": "2019-09-24T22:51:14.437Z", "content": "https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nhttps://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models\nhttps://github.com/google-research/bert
", "rawMarkdown": "https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nhttps://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models\nhttps://github.com/google-research/bert" }, { "id": 633114, "postDate": "2019-09-24T12:56:55.610Z", "content": "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
", "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" }, { "id": 632495, "postDate": "2019-09-23T17:10:51.070Z", "content": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models
", "rawMarkdown": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models" }, { "id": 632179, "postDate": "2019-09-23T10:26:58.473Z", "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" }, { "id": 632149, "postDate": "2019-09-23T09:32:24.023Z", "content": "Open Images V5\nhttps://storage.googleapis.com/openimages/web/download.html
\n\nmmdet pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "rawMarkdown": "Open Images V5\nhttps://storage.googleapis.com/openimages/web/download.html\n\nmmdet pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md" }, { "id": 632131, "postDate": "2019-09-23T09:01:37.220Z", "content": "faster rcnn inception resnet v2 atrous oidv4
", "rawMarkdown": "[faster rcnn inception resnet v2 atrous oidv4](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_v4_2018_12_12.tar.gz)" }, { "id": 631024, "postDate": "2019-09-21T07:16:14.173Z", "content": "http://cocodataset.org/#home\nhttps://www.objects365.org/download.html
", "rawMarkdown": "http://cocodataset.org/#home\nhttps://www.objects365.org/download.html" }, { "id": 630938, "postDate": "2019-09-21T03:27:04.163Z", "content": "Pretrained models from https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018
", "rawMarkdown": "Pretrained models from https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018" }, { "id": 630048, "postDate": "2019-09-19T16:26:22.897Z", "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" }, { "id": 628350, "postDate": "2019-09-17T07:39:05.610Z", "content": "We use the image-level label, segmentation label of the whole OpenImageV5:\nhttps://storage.googleapis.com/openimages/web/download.html
", "rawMarkdown": "We use the image-level label, segmentation label of the whole OpenImageV5:\nhttps://storage.googleapis.com/openimages/web/download.html\n" }, { "id": 628097, "postDate": "2019-09-16T19:37:49.533Z", "content": "", "rawMarkdown": "http://cocodataset.org/#home" }, { "id": 621759, "postDate": "2019-09-08T22:48:11.450Z", "content": "https://pytorch.org/docs/stable/torchvision/models.html
", "rawMarkdown": "https://pytorch.org/docs/stable/torchvision/models.html" }, { "id": 589154, "postDate": "2019-07-31T14:35:45.727Z", "content": "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1
", "rawMarkdown": "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1" }, { "id": 552881, "postDate": "2019-06-14T18:19:32.190Z", "rawMarkdown": "" }, { "id": 632556, "postDate": "2019-09-23T18:35:55.670Z", "rawMarkdown": "", "isDeleted": true }, { "id": 630808, "postDate": "2019-09-20T18:47:26.060Z", "rawMarkdown": "", "isDeleted": true }, { "id": 543408, "postDate": "2019-06-04T14:14:50.867Z", "rawMarkdown": "", "isDeleted": true } ], "comments": [ { "id": 600718, "author_name": "impulsecorp", "author_url": "", "post_date": "2019-08-16T13:46:56.573000", "content": "If we are using an external pretrained model (like the ones xhlulu posted), and not the external data itself, do we have to post it here?
", "votes": 6, "replies": [] }, { "id": 633408, "author_name": "aagapi", "author_url": "", "post_date": "2019-09-24T21:39:04.317000", "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": 3, "replies": [] }, { "id": 632153, "author_name": "littletomatodonkey ", "author_url": "", "post_date": "2019-09-23T09:36:28.497000", "content": "We use some of the following models or datasets:\n1. PaddlePaddle image classification model zoo:\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\n2. PaddleDetection model zoo:\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\n3. Coco dataset:\nhttp://cocodataset.org/#home\n4. Objects365 dataset:\nhttps://www.objects365.org/download.html
", "votes": 1, "replies": [] }, { "id": 631696, "author_name": "Oljike", "author_url": "", "post_date": "2019-09-22T14:00:58.430000", "content": "http://shuoyang1213.me/WIDERFACE/
", "votes": 1, "replies": [] }, { "id": 633053, "author_name": "Zehao Zhao", "author_url": "", "post_date": "2019-09-24T11:39:43.530000", "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": 0, "replies": [ { "id": 637986, "author_name": "Rodrigo Benenson", "author_url": "", "post_date": "2019-10-01T12:30:39.143000", "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); which was on September 24th.
\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": 0, "replies": [] } ] }, { "id": 635367, "author_name": "Zack", "author_url": "", "post_date": "2019-09-27T12:40:34.823000", "content": "https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018
", "votes": 0, "replies": [] }, { "id": 633426, "author_name": "Mykyta", "author_url": "", "post_date": "2019-09-24T22:51:14.437000", "content": "https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nhttps://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models\nhttps://github.com/google-research/bert
", "votes": 0, "replies": [] }, { "id": 633114, "author_name": "yuyu2172", "author_url": "", "post_date": "2019-09-24T12:56:55.610000", "content": "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
", "votes": 0, "replies": [] }, { "id": 632495, "author_name": "Schwert", "author_url": "", "post_date": "2019-09-23T17:10:51.070000", "content": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models
", "votes": 0, "replies": [] }, { "id": 632179, "author_name": "JiaqiFan", "author_url": "", "post_date": "2019-09-23T10:26:58.473000", "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": 0, "replies": [] }, { "id": 632149, "author_name": "Appian", "author_url": "", "post_date": "2019-09-23T09:32:24.023000", "content": "Open Images V5\nhttps://storage.googleapis.com/openimages/web/download.html
\n\nmmdet pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "votes": 0, "replies": [] }, { "id": 632131, "author_name": "ncv", "author_url": "", "post_date": "2019-09-23T09:01:37.220000", "content": "faster rcnn inception resnet v2 atrous oidv4
", "votes": 0, "replies": [] }, { "id": 631024, "author_name": "Prisms", "author_url": "", "post_date": "2019-09-21T07:16:14.173000", "content": "http://cocodataset.org/#home\nhttps://www.objects365.org/download.html
", "votes": 0, "replies": [] }, { "id": 630938, "author_name": "Mohammad Azam Khan", "author_url": "", "post_date": "2019-09-21T03:27:04.163000", "content": "Pretrained models from https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018
", "votes": 0, "replies": [] }, { "id": 630048, "author_name": "chicm", "author_url": "", "post_date": "2019-09-19T16:26:22.897000", "content": "mmdetection pretrained models:\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md
", "votes": 0, "replies": [] }, { "id": 628350, "author_name": "louis", "author_url": "", "post_date": "2019-09-17T07:39:05.610000", "content": "We use the image-level label, segmentation label of the whole OpenImageV5:\nhttps://storage.googleapis.com/openimages/web/download.html
", "votes": 0, "replies": [] }, { "id": 628097, "author_name": "Vadik", "author_url": "", "post_date": "2019-09-16T19:37:49.533000", "content": "", "votes": 0, "replies": [] }, { "id": 621759, "author_name": "Ryan Wong", "author_url": "", "post_date": "2019-09-08T22:48:11.450000", "content": "https://pytorch.org/docs/stable/torchvision/models.html
", "votes": 0, "replies": [] }, { "id": 589154, "author_name": "xhlulu", "author_url": "", "post_date": "2019-07-31T14:35:45.727000", "content": "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1
", "votes": 0, "replies": [] }, { "id": 552881, "author_name": "Arun Kapoor", "author_url": "", "post_date": "2019-06-14T18:19:32.190000", "content": "", "votes": 0, "replies": [] }, { "id": 632556, "author_name": "", "author_url": "", "post_date": "2019-09-23T18:35:55.670000", "content": "", "votes": 0, "replies": [] }, { "id": 630808, "author_name": "", "author_url": "", "post_date": "2019-09-20T18:47:26.060000", "content": "", "votes": 0, "replies": [] }, { "id": 543408, "author_name": "", "author_url": "", "post_date": "2019-06-04T14:14:50.867000", "content": "", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "600718": "If we are using an external pretrained model (like the ones xhlulu posted), and not the external data itself, do we have to post it here?", "633408": "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/\n", "632153": "We use some of the following models or datasets:\n1. PaddlePaddle image classification model zoo:\nhttps://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification\n2. PaddleDetection model zoo:\nhttps://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/MODEL_ZOO.md\n3. Coco dataset:\nhttp://cocodataset.org/#home\n4. Objects365 dataset:\nhttps://www.objects365.org/download.html", "631696": "http://shuoyang1213.me/WIDERFACE/", "542388": "Per the [Competition Rules](https://www.kaggle.com/c/open-images-2019-object-detection/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.", "633053": "1. Tensorflow Hub Pretrained Models( Faster-CNN with inception_resnet as backbone):\nhttps://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\n2. Tensorflow Hub Pretrained Models(SSD with mobilenet as backbone):\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1\n3. Keras RetinaNet Pretrained Models:\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.1/retinanet_resnet50_level_1_converted.h5\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.2/retinanet_resnet101_level_1_v1.2_converted.h5\nhttps://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018/releases/download/v1.2/retinanet_resnet152_level_1_v1.2_converted.h5", "637322": "https://github.com/facebookresearch/WSL-Images\nhttps://github.com/hujie-frank/SENet\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "635367": "https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018", "633426": "https://storage.googleapis.com/openimages/web/challenge2019_downloads.html\nhttps://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models\nhttps://github.com/google-research/bert", "633114": "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", "632495": "FAIR's Imagenet pretrained models:\nhttps://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models", "632179": "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", "632149": "Open Images V5\nhttps://storage.googleapis.com/openimages/web/download.html\n\nmmdet pretrained models\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "632131": "[faster rcnn inception resnet v2 atrous oidv4](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_oid_v4_2018_12_12.tar.gz)", "631024": "http://cocodataset.org/#home\nhttps://www.objects365.org/download.html", "630938": "Pretrained models from https://github.com/ZFTurbo/Keras-RetinaNet-for-Open-Images-Challenge-2018", "630048": "mmdetection pretrained models:\nhttps://github.com/open-mmlab/mmdetection/blob/master/docs/MODEL_ZOO.md", "628350": "We use the image-level label, segmentation label of the whole OpenImageV5:\nhttps://storage.googleapis.com/openimages/web/download.html\n", "628097": "http://cocodataset.org/#home", "621759": "https://pytorch.org/docs/stable/torchvision/models.html", "589154": "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1\nhttps://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1", "552881": "", "632556": "", "630808": "", "543408": "" } }