Model Overview

EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.

Model Architecture

EfficientNets are developed based on AutoML and Compound Scaling. In particular,\na mobile-size baseline network called EfficientNet-B0 is developed from AutoML MNAS Mobile\nframework, the building block is mobile inverted bottleneck MBConv with squeeze-and-excitation optimization.\nThen, through a compound scaling method, this baseline is scaled up to obtain EfficientNet-B1\nto B7.

$\"Efficientnet_structure\"$

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.

","resourceVersions",[218,219],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_191":22,"_192":193,"_49":194,"_195":-5,"_196":197,"_198":199,"_200":201,"_202":16,"_203":31,"_204":-5,"_205":31,"_206":-5,"_207":31,"_208":-5,"_209":31,"_210":-5,"_211":31,"_212":-5,"_213":31,"_214":-5,"_215":31,"_216":217},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/dle/models/efficientnet_tf2_savedmodel_v1_b0_amp/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/dle/efficientnet_tf2_savedmodel_v1_b0_amp:21.09.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/dle/models/efficientnet_tf2_savedmodel_v1_b0_amp/21.09.0/version-history","accessCheckResult",{"_190":111},"hasRelatedCollections","context",{"_186":187,"_188":189},1787615065488,[],[],[179,180,181,182,183,184,185],{"_45":60,"_47":71,"_49":62,"_51":63,"_53":68,"_55":56},{"_45":60,"_47":69,"_49":62,"_51":63,"_53":70,"_55":56},{"_45":60,"_47":67,"_49":62,"_51":63,"_53":68,"_55":56},{"_45":60,"_47":65,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":62,"_51":63,"_53":64,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":59,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56},"orgName","nvidia","teamName","dle","allowed","versionId","description","","UPLOAD_COMPLETE","releaseType","createdDate",["D",1646698650191],"updatedDate",["D",1740776611678],"totalSizeInBytes",97822976,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_78":221,"_80":222,"_195":-5,"_203":31,"_205":31,"_216":220,"_207":31},{"_78":22,"_80":22,"_195":-5,"_203":31,"_205":31,"_216":220,"_207":31},[],"-","Latest (21.09.0)","nvidia/dle/efficientnet_tf2_savedmodel_v1_b0_amp","key","nvidia/dle/models/efficientnet_tf2_savedmodel_v1_b0_amp","resourceType","models","resourceId","efficientnet_tf2_savedmodel_v1_b0_amp","shortDescription","EfficientNet v1 B0 trained on DGX2-V100 with batch_size=4096","## Model Overview\n\nEfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.\n\n## Model Architecture\n\n\nEfficientNets are developed based on AutoML and Compound Scaling. In particular, \na mobile-size baseline network called EfficientNet-B0 is developed from AutoML MNAS Mobile\nframework, the building block is mobile inverted bottleneck MBConv with squeeze-and-excitation optimization. \nThen, through a compound scaling method, this baseline is scaled up to obtain EfficientNet-B1\nto B7.\n\n![Efficientnet_structure](https://1.bp.blogspot.com/-Cdtb97FtgdA/XO3BHsB7oEI/AAAAAAAAEKE/bmtkonwgs8cmWyI5esVo8wJPnhPLQ5bGQCLcBGAs/s1600/image4.png)\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:efficientnet_for_tensorflow2) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet)\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://ngc.nvidia.com/catalog/resources/nvidia:efficientnet_for_tensorflow2/performance)\n\n## References\n- [original paper](https://arxiv.org/abs/1905.11946)\n- [NVIDIA model implementation in NGC](https://ngc.nvidia.com/catalog/resources/nvidia:efficientnet_for_tensorflow2)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Classification/ConvNets/efficientnet)\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/Classification/ConvNets/efficientnet) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","EfficientNet-V1-B0 savedmodel (TensorFlow2, AMP, Imagenet)",["D",1680635126151],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/efficientnet_tf2_savedmodel_v1_b0_amp","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes",97747352,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[266],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_233":267,"_268":269,"_270":271,"_272":273},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_274":275,"_268":269},"category","label","field","platform",{"_132":1254,"_134":1255},"ngcApps",[1207],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1188":1189,"_1190":1191,"_1192":1193,"_1194":1195,"_1196":287,"_163":313,"_1197":1198,"_1199":56,"_1200":111,"_1201":111,"_1202":1203,"_1204":111,"_1205":1206},"amplitude",{"_1185":1186,"_825":-7,"_1187":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1179":1180,"_1181":1182,"_1183":1184},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787615065426,"1.12.0","defaultSearchSuggestions",{"_1065":193,"_1066":1067,"_1068":1069,"_1070":1071},"announcements",[],"consentState",{"_351":1055,"_1056":111,"_1057":31,"_1058":31,"_1059":1060},"consentConfig",{"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352},"showContactAdmin",1787615065428,[],[],[327,328,329,330,331,332],{"_45":60,"_47":71,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":127,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":16,"_55":56},{"_45":46,"_47":48,"_49":127,"_51":-5,"_53":-5,"_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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Name	Size	Updated	Actions

keras_metadata.pb	874.14 KB	February 28, 2025 UTC	curl wget Download
saved_model.pb	11.09 MB	February 28, 2025 UTC	curl wget Download