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Model Overview

GPUNet is a new family of Convolutional Neural Networks crafted by NVIDIA AI.

Model Architecture

\n \n

The above table describes the general structure of GPUNet, which consists of 8 stages, and we search for the configurations of each stage. The layers within a stage share the same configurations. The first two stages are to search for the head configurations using convolutions. Inspired by EfficientNet-V2, the 2 and 3 stages use Fused Inverted Residual Blocks(IRB); however, we observed the increasing latency after replacing the rest IRB with Fused-IRB. Therefore, from stages 4 to 7, we use IRB as the primary layer. The column #Layers shows the range of #Layers in the stage, for example, [3, 10] at stage 4 means that the stage can have three to 10 IRBs, and the column filters shows the range of filters for the layers in the stage. We also tuned the expansion ratio, activation types, kernel sizes, and the Squeeze Excitation(SE) layer inside the IRB/Fused-IRB. Finally, the dimensions of the input image increased from 224 to 512 at step 32.

GPUNet has provided seven specific model architectures at different latencies. You can easily query the architecture details from the JSON formatted model (for example, those in eval.py). The following figure describes GPUNet-0, GPUNet-1, and GPUNet-2 in the paper. Note that only the first IRB's stride is two and the stride of the rest IRBs is 1 in stages 2, 3, 4, and 6.

\n \n

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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\n \n

\n\nThe above table describes the general structure of GPUNet, which consists of 8 stages, and we search for the configurations of each stage. The layers within a stage share the same configurations. The first two stages are to search for the head configurations using convolutions. Inspired by [EfficientNet-V2](https://arxiv.org/abs/2104.00298), the 2 and 3 stages use Fused Inverted Residual Blocks(IRB); however, we observed the increasing latency after replacing the rest IRB with Fused-IRB. Therefore, from stages 4 to 7, we use IRB as the primary layer. The column \\#Layers shows the range of \\#Layers in the stage, for example, [3, 10] at stage 4 means that the stage can have three to 10 IRBs, and the column filters shows the range of filters for the layers in the stage. We also tuned the expansion ratio, activation types, kernel sizes, and the Squeeze Excitation(SE) layer inside the IRB/Fused-IRB. Finally, the dimensions of the input image increased from 224 to 512 at step 32.\n\nGPUNet has provided seven specific model architectures at different latencies. You can easily query the architecture details from the JSON formatted model (for example, those in eval.py). The following figure describes GPUNet-0, GPUNet-1, and GPUNet-2 in the paper. Note that only the first IRB's stride is two and the stride of the rest IRBs is 1 in stages 2, 3, 4, and 6.\n

\n \n

\n\n\n \n## Training\n\nThis model was trained using script available in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/GPUNet).\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/PyTorch/Classification/GPUNet#performance).\n\n## References\n- [Original paper](https://arxiv.org/pdf/2205.00841.pdf)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/GPUNet)\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/GPUNet) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","GPUNet-P1 pretrained weights (PyTorch, AMP, ImageNet)",["D",1680638555791],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/gpunet_p1_pyt_ckpt","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Deep-Learning-Examples.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":240,"_241":242,"_243":244,"_245":246},"Deep Learning Examples","value","pltfm_deep_learning_examples","filter",{"_247":248,"_241":242},"category","label","field","platform",{"_107":1424,"_109":1425},"ngcApps",[1377],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1358":1359,"_1360":1361,"_1362":1363,"_1364":1365,"_1366":260,"_138":284,"_1367":1368,"_1369":55,"_1370":86,"_1371":86,"_1372":1373,"_1374":86,"_1375":1376},"amplitude",{"_1355":1356,"_956":-7,"_1357":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1349":1350,"_1351":1352,"_1353":1354},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1231":168,"_1232":1233,"_1234":1235,"_1236":1237},"announcements",[],"consentState",{"_322":1221,"_1222":86,"_1223":30,"_1224":30,"_1225":1226},"consentConfig",{"_304":305,"_306":307,"_308":309,"_310":311,"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323},"showContactAdmin",1784010466579,[],[],[298,299,300,301,302,303],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_50":-5,"_52":-5,"_54":55},"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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