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 FasterRCNN adding an additional mask head for the task of image segmentation.

The architecture consists of following:

R-50 backbone with FPN
RPN head
RoI ALign
Bounding and classification box head
Mask head

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 2014 - 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 FasterRCNN adding an additional mask head for the task of image segmentation.\n \nThe architecture consists of following:\n- R-50 backbone with FPN\n- RPN head\n- RoI ALign\n- Bounding and classification box head\n- Mask head\n \n\n \n## Training\n\nThis model was trained using script available on [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:mask_r_cnn_for_pytorch) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/MaskRCNN)\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [COCO 2014](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://ngc.nvidia.com/catalog/resources/nvidia:mask_r_cnn_for_pytorch/performance)\n\n## References\n- [original paper](https://arxiv.org/abs/1703.06870)\n- [NVIDIA model implementation in NGC](https://ngc.nvidia.com/catalog/resources/nvidia:mask_r_cnn_for_pytorch)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/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/PyTorch/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 PyTorch checkpoint (FP32)",["D",1680635704302],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/maskrcnn_pyt_ckpt_fp32","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[279,280],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_247":289,"_112":290,"_283":291,"_285":286},{"_247":281,"_112":282,"_283":284,"_285":286},"Deep Learning Examples","pltfm_deep_learning_examples","filter",{"_287":288,"_112":282},"category","label","field","platform","Computer Vision","soln_computer_vision",{"_287":292,"_112":290},"technology",{"_156":1426,"_158":1427},"ngcApps",[1379],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1360":1361,"_1362":1363,"_1364":1365,"_1366":1367,"_1368":304,"_187":330,"_1369":1370,"_1371":58,"_1372":135,"_1373":135,"_1374":1375,"_1376":135,"_1377":1378},"amplitude",{"_1357":1358,"_964":-7,"_1359":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1351":1352,"_1353":1354,"_1355":1356},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793589486777,"1.29.0","defaultSearchSuggestions",{"_1234":220,"_1235":1236,"_1237":1238,"_1239":1240},"announcements",[],"consentState",{"_366":1224,"_1225":34,"_1226":34,"_1227":135,"_1228":1229},"consentConfig",{"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367},"showContactAdmin",[],[],[343,344,345,346,347],{"_48":62,"_50":73,"_52":151,"_54":-5,"_56":-5,"_57":58},{"_48":62,"_50":69,"_52":151,"_54":-5,"_56":-5,"_57":58},{"_48":62,"_50":63,"_52":151,"_54":-5,"_56":-5,"_57":58},{"_48":59,"_50":60,"_52":53,"_54":55,"_56":61,"_57":58},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":19,"_57":58},"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
backbone	RN50
dataset	COCO2014

Key	Value
BBOX_MAP	0.380
MASK_MAP	0.344

Key	Value
bs	32
dataset	COCO2014
iterations	60000
training_precision	FP32