Model Overview

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

Model Architecture

Despite the changes described in the previous section,\nthe overall architecture, as described in the following diagram, has not changed.

\n \n
\nFigure 1. The architecture of a Single Shot MultiBox Detector model. Image has been taken from the Single Shot MultiBox Detector paper.\n

The backbone is followed by 5 additional convolutional layers.\nIn addition to the convolutional layers, we attached 6 detection heads:

The first detection head is attached to the last conv4_x layer.
The other five detection heads are attached to the corresponding 5 additional layers.

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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\n \n
\nFigure 1. The architecture of a Single Shot MultiBox Detector model. Image has been taken from the Single Shot MultiBox Detector paper.\n

\n\nThe backbone is followed by 5 additional convolutional layers.\nIn addition to the convolutional layers, we attached 6 detection heads:\n* The first detection head is attached to the last conv4_x layer.\n* The other five detection heads are attached to the corresponding 5 additional layers.\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_pyt) and in [GitHub repo](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/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_pyt/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_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/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/PyTorch/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 PyTorch checkpoint (AMP)",["D",1663867421629],"publisher","NVIDIA Deep Learning Examples","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/dle/models/ssd__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",{"_152":1444,"_154":1445},"ngcApps",[1397],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1378":1379,"_1380":1381,"_1382":1383,"_1384":1385,"_1386":282,"_183":306,"_1387":1388,"_1389":59,"_1390":131,"_1391":131,"_1392":1393,"_1394":131,"_1395":1396},"amplitude",{"_1375":1376,"_977":-7,"_1377":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1369":1370,"_1371":1372,"_1373":1374},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1252":214,"_1253":1254,"_1255":1256,"_1257":1258},"announcements",[],"consentState",{"_344":1242,"_1243":131,"_1244":34,"_1245":34,"_1246":1247},"consentConfig",{"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345},"showContactAdmin",1784006883465,[],[],[320,321,322,323,324],{"_48":63,"_50":73,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":147,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":325,"_58":59},{"_48":49,"_50":51,"_52":147,"_54":-5,"_56":-5,"_58":59},5,"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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Key	Value
batch_size	512
training_precision	AMP
dataset	COCO2017
iterations	15000