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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 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 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://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/maskrcnn_pyt) 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 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_pyt/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_pyt)\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 (AMP)",["D",1663865787341],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/maskrcnn__pyt_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",{"_107":1412,"_109":1413},"ngcApps",[1365],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1346":1347,"_1348":1349,"_1350":1351,"_1352":1353,"_1354":249,"_138":273,"_1355":1356,"_1357":56,"_1358":86,"_1359":86,"_1360":1361,"_1362":86,"_1363":1364},"amplitude",{"_1343":1344,"_944":-7,"_1345":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1337":1338,"_1339":1340,"_1341":1342},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1219":168,"_1220":1221,"_1222":1223,"_1224":1225},"announcements",[],"consentState",{"_310":1209,"_1210":86,"_1211":31,"_1212":31,"_1213":1214},"consentConfig",{"_292":293,"_294":295,"_296":297,"_298":299,"_300":301,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311},"showContactAdmin",1783947190753,[],[],[287,288,289,290,291],{"_45":60,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":59,"_55":56},"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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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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