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Model Overview

With modified architecture and initialization this ResNet50 version gives ~0.5% better accuracy than original.

Model Architecture

The model architecture was present in Deep Residual Learning for Image Recognition paper. The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.

$\"ResidualLayer\"$

Image source: Deep Residual Learning for Image Recognition

Training

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

Dataset

The following datasets were used to train this model:

ImageNet - Image database organized according to the WordNet hierarchy, in which each noun is depicted by hundreds and thousands of images.

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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The main advantage of the model is the usage of residual layers as a building block that helps with gradient propagation during training.\n\n![ResidualLayer](https://github.com/NVIDIA/DeepLearningExamples/raw/master/MxNet/Classification/RN50v1.5/img/residual_diagram.png)\n\n\n_Image source: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)_\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/resnet_mxnet) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/MxNet/Classification/RN50v1.5).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [ImageNet](https://image-net.org/) - Image database organized according to the WordNet hierarchy, in which each noun is depicted by hundreds and thousands of images.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnet_mxnet/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1512.03385)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnet_mxnet)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/MxNet/Classification/RN50v1.5)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/MxNet/Classification/RN50v1.5) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","ResNet50 v1.5 MXNet checkpoint (AMP)",["D",1663869835202],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/resnet_50_mx_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":1216,"_109":1217},"ngcApps",[1169],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1150":1151,"_1152":1153,"_1154":1155,"_1156":1157,"_1158":249,"_138":275,"_1159":1160,"_1161":56,"_1162":86,"_1163":86,"_1164":1165,"_1166":86,"_1167":1168},"amplitude",{"_1147":1148,"_787":-7,"_1149":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1141":1142,"_1143":1144,"_1145":1146},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787876229528,"1.12.0","defaultSearchSuggestions",{"_1027":168,"_1028":1029,"_1030":1031,"_1032":1033},"announcements",[],"consentState",{"_313":1017,"_1018":86,"_1019":31,"_1020":31,"_1021":1022},"consentConfig",{"_295":296,"_297":298,"_299":300,"_301":302,"_303":304,"_305":306,"_307":308,"_309":310,"_311":312,"_313":314},"showContactAdmin",1787876229529,[],[],[289,290,291,292,293],{"_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":294,"_55":56},5,"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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