Model Overview

A convolution-based neural network for the task of object detection.

Model Architecture

EfficientDet is a one-stage detector with the following architecture components:

ImageNet-pretrained EfficientNet backbone
Weighted bi-directional feature pyramid network (BiFPN)
Bounding and classification box head
A compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time

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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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/efficientdet_pyt/performance).\n\n## References\n- [Original paper](https://arxiv.org/abs/1911.09070)\n- [NVIDIA model implementation in NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/dle/resources/efficientdet_pyt)\n- [NVIDIA model implementation on GitHub](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Detection/Efficientdet)\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/Efficientdet) repository.\nFor terms of use, please refer to the license of the script and the datasets the model was derived from.","displayName","EfficientDet-D0 backbone checkpoint (Pytorch, AMP)",["D",1663870671732],"publisher","NVIDIA Deep Learning Examples","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/dle/models/efficientdet_b0-nohead_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",{"_106":1413,"_108":1414},"ngcApps",[1366],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1347":1348,"_1349":1350,"_1351":1352,"_1353":1354,"_1355":249,"_137":273,"_1356":1357,"_1358":56,"_1359":86,"_1360":86,"_1361":1362,"_1363":86,"_1364":1365},"amplitude",{"_1344":1345,"_945":-7,"_1346":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1338":1339,"_1340":1341,"_1342":1343},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1220":168,"_1221":1222,"_1223":1224,"_1225":1226},"announcements",[],"consentState",{"_310":1210,"_1211":86,"_1212":31,"_1213":31,"_1214":1215},"consentConfig",{"_292":293,"_294":295,"_296":297,"_298":299,"_300":301,"_302":303,"_304":305,"_306":307,"_308":309,"_310":311},"showContactAdmin",1783930110923,[],[],[287,288,289,290,291],{"_45":59,"_47":70,"_49":67,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":67,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":67,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":67,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":67,"_51":-5,"_53":-5,"_55":56},"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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