Model Overview

Mask R-CNN is a convolution based network for object instance segmentation. This implementation provides 1.3x faster training while maintaining target accuracy.

Model Architecture

Mask R-CNN builds on top of Faster R-CNN adding a mask head for the task of image segmentation.

The architecture consists of the following:

ResNet-50 backbone with Feature Pyramid Network (FPN)
Region proposal network (RPN) head
RoI Align
Bounding and classification box head
Mask head

$\"Architecture\"$

Figure 1. Diagram of Mask R-CNN framework from original paper

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

COCO 2017 - Dataset for large-scale object detection, segmentation and captioning.

Performance

Performance numbers for this model are available in NGC.

References

License

This model was trained using open-source software available in Deep Learning Examples repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.

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This implementation provides 1.3x faster training while maintaining target accuracy.\n\n## Model Architecture\n\n\nMask R-CNN builds on top of Faster R-CNN adding a mask head for the task of image segmentation.\n\nThe architecture consists of the following:\n\n- ResNet-50 backbone with Feature Pyramid Network (FPN)\n- Region proposal network (RPN) head\n- RoI Align\n- Bounding and classification box head\n- Mask head\n\n![Architecture](https://github.com/NVIDIA/DeepLearningExamples/raw/master/TensorFlow2/Segmentation/MaskRCNN/documentation/architecture.png)\n\n\nFigure 1. Diagram of Mask R-CNN framework from [original paper](https://arxiv.org/abs/1703.06870)\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/maskrcnn_tf2) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [COCO 2017](https://cocodataset.org/#download) - Dataset for large-scale object detection, segmentation and captioning.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/maskrcnn_tf2/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1703.06870)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/maskrcnn_tf2)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","MaskRCNN TF2 checkpoint (AMP)",["D",1663865757509],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/models/maskrcnn__tf2_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_157":1251,"_159":1252},"ngcApps",[1204],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1185":1186,"_1187":1188,"_1189":1190,"_1191":1192,"_1193":287,"_188":313,"_1194":1195,"_1196":59,"_1197":136,"_1198":136,"_1199":1200,"_1201":136,"_1202":1203},"amplitude",{"_1182":1183,"_824":-7,"_1184":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1176":1177,"_1178":1179,"_1180":1181},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787612068460,"1.12.0","defaultSearchSuggestions",{"_1064":219,"_1065":1066,"_1067":1068,"_1069":94},"announcements",[],"consentState",{"_350":1054,"_1055":34,"_1056":34,"_1057":136,"_1058":1059},"consentConfig",{"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351},"showContactAdmin",1787612068461,[],[],[327,328,329,330,331],{"_48":63,"_50":74,"_52":152,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":152,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":152,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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Key	Value
AP BBox	0.37793
AP Segm	0.34151

Key	Value
batch_size	2
training_precision	AMP
dataset	COCO2017
epochs	12