ConvNets PyTorch codebase

PyTorch codebase for training and using ConvNet models. Refer to the following guides for the use-cases of this codebase, setup instructions and performance numbers:

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Refer to the following guides for the use-cases of this codebase, setup instructions and performance numbers:\n\n- [EfficientNet for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/efficientnet_pyt)\n- [ResNet50 v1.5 for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnet_pyt)\n- [ResNet50 v1.5 Triton deployment for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnet_pyt_triton)\n- [ResNeXt101-32x4d for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnext_pyt)\n- [ResNeXt101-32x4d Triton deployment for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/resnext_pyt_triton)\n- [SE-ResNeXt101-32x4d for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/seresnext_pyt)\n- [SE-ResNeXt101-32x4d Triton deployment for PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/seresnext_pyt_triton)","displayName","ConvNets PyTorch codebase",["D",1680810368351],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/resources/convnets-pyt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[300],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_269":301,"_302":303,"_304":305,"_306":89},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_307":308,"_302":303},"category","field","platform",{"_174":1482,"_176":1483},"ngcApps",[1435],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1416":1417,"_1418":1419,"_1420":1421,"_1422":1423,"_1424":320,"_111":344,"_1425":1426,"_1427":60,"_1428":92,"_1429":92,"_1430":1431,"_1432":92,"_1433":1434},"amplitude",{"_1413":1414,"_1015":-7,"_1415":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1407":1408,"_1409":1410,"_1411":1412},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1290":423,"_1291":1292,"_1293":1294,"_1295":1296},"announcements",[],"consentState",{"_381":1280,"_1281":92,"_1282":35,"_1283":35,"_1284":1285},"consentConfig",{"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382},"showContactAdmin",1783956225132,[],[],[358,359,360,361,362],{"_49":64,"_51":75,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":169,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":66,"_55":67,"_57":68,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":63,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"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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	Files	Compressed Size
21.03.4Selected	171	1.97 MB	April 6, 2023 UTC
21.03.3	171	1.97 MB	February 7, 2023 UTC
21.03.2	171	1.97 MB	January 10, 2023 UTC
21.03.1	171	1.97 MB	November 4, 2022 UTC
21.03.0	171	1.97 MB	September 22, 2022 UTC