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":1448,"_159":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":287,"_188":311,"_1391":1392,"_1393":59,"_1394":136,"_1395":136,"_1396":1397,"_1398":136,"_1399":1400},"amplitude",{"_1379":1380,"_982":-7,"_1381":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1257":219,"_1258":1259,"_1260":1261,"_1262":94},"announcements",[],"consentState",{"_348":1247,"_1248":136,"_1249":34,"_1250":34,"_1251":1252},"consentConfig",{"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349},"showContactAdmin",1783956574097,[],[],[325,326,327,328,329],{"_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":152,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":152,"_54":-5,"_56":-5,"_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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Key	Value
AP BBox	0.37793
AP Segm	0.34151

Key	Value
batch_size	2
training_precision	AMP
dataset	COCO2017
epochs	12