Model Overview

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

Model Architecture

The ResNet50 v1.5 model is a modified version of the original ResNet50 v1 model.

The difference between v1 and v1.5 is that in the bottleneck blocks which requires\ndownsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution.

This difference makes ResNet50 v1.5 slightly more accurate (~0.5% top1) than v1 but comes with a small performance drawback (~5% imgs/sec).

The model is initialized as described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification

This model is trained with mixed precision using Tensor Cores on the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.

Training

This model was trained using script available 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 GitHub readme performance section.

References

Original paper
NVIDIA model implementation on GitHub

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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Therefore, researchers can get results over 2x faster than training without Tensor Cores while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.\n\n\n \n## Training\n\nThis model was trained using script available in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PaddlePaddle/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 [GitHub readme performance section](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PaddlePaddle/Classification/RN50v1.5#performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1512.03385)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PaddlePaddle/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/PaddlePaddle/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 checkpoints (PaddlePaddle, AMP, ImageNet)",["D",1680635632721],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/resnet_50_paddle_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[250],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_218":251,"_252":253,"_254":255,"_256":257},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_258":259,"_252":253},"category","label","field","platform",{"_117":1243,"_119":1244},"ngcApps",[1196],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1177":1178,"_1179":1180,"_1181":1182,"_1183":1184,"_1185":271,"_148":297,"_1186":1187,"_1188":55,"_1189":96,"_1190":96,"_1191":1192,"_1193":96,"_1194":1195},"amplitude",{"_1174":1175,"_810":-7,"_1176":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1168":1169,"_1170":1171,"_1172":1173},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789449789178,"1.15.0","defaultSearchSuggestions",{"_1050":178,"_1051":1052,"_1053":1054,"_1055":1056},"announcements",[],"consentState",{"_336":1040,"_1041":96,"_1042":31,"_1043":31,"_1044":1045},"consentConfig",{"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337},"showContactAdmin",1789449789180,[],[],[311,312,313,314,315,316],{"_45":58,"_47":68,"_49":112,"_51":-5,"_53":-5,"_54":55},{"_45":58,"_47":66,"_49":112,"_51":-5,"_53":-5,"_54":55},{"_45":58,"_47":64,"_49":112,"_51":-5,"_53":-5,"_54":55},{"_45":58,"_47":59,"_49":112,"_51":-5,"_53":-5,"_54":55},{"_45":56,"_47":57,"_49":50,"_51":52,"_53":317,"_54":55},{"_45":46,"_47":48,"_49":112,"_51":-5,"_53":-5,"_54":55},4,"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
resnet_50_paddle.pdmodel	2.24 MB	February 28, 2025 UTC	curl wget Download
resnet_50_paddle.pdopt	97.52 MB	February 28, 2025 UTC	curl wget Download
resnet_50_paddle.pdparams	97.72 MB	February 28, 2025 UTC	curl wget Download