Model Overview

ResNet with bottleneck 3x3 Convolutions substituted by 3x3 Grouped Convolutions.

Model Architecture

$\"ResNextArch\"$

Image source: Aggregated Residual Transformations for Deep Neural Networks

Image shows difference between ResNet bottleneck block and ResNeXt bottleneck block.

ResNeXt101-32x4d model's cardinality equals to 32 and bottleneck width equals to 4.

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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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/resnext_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/pdf/1611.05431.pdf)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnext_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnext101-32x4d)\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/Classification/ConvNets/resnext101-32x4d) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","ResNeXt101-32x4d pretrained weights (PyTorch, AMP, ImageNet)",["D",1663867551153],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/resnext_101-32x4d_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",1784264843353,[],[],[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":50,"_51":52,"_53":59,"_55":56},{"_45":46,"_47":48,"_49":102,"_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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