Model Overview

With a ResNet-50 backbone and a number of architectural modifications, this version provides better accuracy and performance.

Model Architecture

Our implementation is based on the existing model from the TensorFlow models repository.\nThe network was altered in order to improve accuracy and increase throughput. Changes include:

Replacing the VGG backbone with the more popular ResNet50.
Adding multi-scale detection to the backbone using Feature Pyramid Networks.
Replacing the original hard negative mining loss function with Focal Loss.
Decreasing the input size to 320 x 320.

Training

This model was trained using script available on NGC and in GitHub repo.

Dataset

The following datasets were used to train this model:

COCO 2017 - Dataset for large-scale object detection, segmentation and captioning.

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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Changes include:\n- Replacing the VGG backbone with the more popular ResNet50.\n- Adding multi-scale detection to the backbone using [Feature Pyramid Networks](https://arxiv.org/pdf/1612.03144.pdf).\n- Replacing the original hard negative mining loss function with [Focal Loss](https://arxiv.org/pdf/1708.02002.pdf).\n- Decreasing the input size to 320 x 320.\n\n\n \n## Training\n\nThis model was trained using script available on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/ssd_tf1) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Detection/SSD).\n\n## Dataset\n\nThe following datasets were used to train this model:\n- [COCO 2017](https://cocodataset.org/#download) - Dataset for large-scale object detection, segmentation and captioning.\n\n\n## Performance\n\nPerformance numbers for this model are available in [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/ssd_tf1/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1512.02325)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/ssd_tf1)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Detection/SSD)\n\n## License\n\nThis model was trained using open-source software available in [Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Detection/SSD) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","SSD TensorFlow checkpoint (AMP)",["D",1663867397857],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/models/ssd__tf1_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",{"_153":1446,"_155":1447},"ngcApps",[1399],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1380":1381,"_1382":1383,"_1384":1385,"_1386":1387,"_1388":284,"_184":308,"_1389":1390,"_1391":59,"_1392":133,"_1393":133,"_1394":1395,"_1396":133,"_1397":1398},"amplitude",{"_1377":1378,"_980":-7,"_1379":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1371":1372,"_1373":1374,"_1375":1376},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1255":216,"_1256":1257,"_1258":1259,"_1260":1261},"announcements",[],"consentState",{"_346":1245,"_1246":133,"_1247":34,"_1248":34,"_1249":1250},"consentConfig",{"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347},"showContactAdmin",1784308391228,[],[],[322,323,324,325,326],{"_48":63,"_50":74,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":327,"_58":59},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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Key	Value
batch_size	256
training_precision	AMP
dataset	COCO2017
iterations	12500