TAO Pretrained EfficientDet-TF2

What is Train Adapt Optimize (TAO) Toolkit?

Train Adapt Optimize (TAO) Toolkit is a python based AI toolkit for taking purpose-built pre-trained AI models and customizing them with your own data. TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune and export highly optimized and accurate AI models for edge deployment.

The pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.

Build end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO, DeepStream SDK and TensorRT. The models are suitable for object detection, classification and segmentation.

Model Overview

Object detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and places bounding boxes around the object. This model card contains pre-trained weights for the backbones that may be used as a starting point with the EfficientDet object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.

Model Architecture

The model in this instance is an EfficientDet-D0 architecture.

Training

Dataset

The model is trained on ImageNet1K and COCO dataset.

Performance

No performance data is available with this model.

How to Use this Model

Running EfficientDet Models Using TAO

The EfficientDet app in TAO expect data in COCO format. TAO provides a simple command line interface to train a deep learning model for object detection.

The models in this model area are only compatible with TAO Toolkit. For more information about the TAO container, please visit the TAO container page.

Limitations

The EfficientDet model was trained on the COCO dataset with 80 object categories. Hence, the model may not perform well on different data distributions. We recommend further fine tuning on the target domain to get higher mAP.

Versions

The following efficientdet-x backbone versions are supported in TAO Toolkit:

efficientdet-d0

License

This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this link, or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Technical blogs

Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0
Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO
Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
Customize Action Recognition with TAO and deploy with DeepStream
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO

Ethical Considerations

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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TAO adapts popular network architectures and backbones to your data, allowing you to train, fine tune, prune and export highly optimized and accurate AI models for edge deployment.\n\nThe pre-trained models accelerate the AI training process and reduce costs associated with large scale data collection, labeling, and training models from scratch. Transfer learning with pre-trained models can be used for AI applications in smart cities, retail, healthcare, industrial inspection and more.\n\nBuild end-to-end services and solutions for transforming pixels and sensor data to actionable insights using TAO, [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) and [TensorRT](https://developer.nvidia.com/tensorrt). The models are suitable for object detection, classification and segmentation.\n\n## Model Overview \n\nObject detection is a popular computer vision technique that can detect one or multiple objects in a frame. Object detection will recognize the individual objects in an image and places bounding boxes around the object. This model card contains pre-trained weights for the backbones that may be used as a starting point with the [EfficientDet](https://arxiv.org/pdf/1911.09070.pdf) object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.\n\n## Model Architecture \n\nThe model in this instance is an EfficientDet-D0 architecture.\n\n## Training\n\n### Dataset\n\nThe model is trained on ImageNet1K and COCO dataset.\n\n## Performance\n\nNo performance data is available with this model.\n\n## How to Use this Model\n\n### Running EfficientDet Models Using TAO \n\nThe EfficientDet app in TAO expect data in COCO format. TAO provides a simple command line interface to train a deep learning model for object detection.\n\nThe models in this model area are only compatible with TAO Toolkit. For more information about the TAO container, please visit the TAO container page.\n\n## Limitations\n\nThe EfficientDet model was trained on the COCO dataset with 80 object categories. Hence, the model may not perform well on different data distributions. We recommend further fine tuning on the target domain to get higher mAP.\n\n## Versions\n\nThe following efficientdet-x backbone versions are supported in TAO Toolkit:\n\n- efficientdet-d0\n\n## License \n\nThis work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License (CC-BY-NC-SA-4.0). To view a copy of this license, please visit this [link](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode), or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\n## Technical blogs \n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n\n## Suggested reading \n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/index.html)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_quick_start_guide.html) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html).\n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your models for video analytics application using DeepStream. Learn more about [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk)\n- Deploy your models in [Riva](https://developer.nvidia.com/riva) for ConvAI use case.\n\n## Ethical Considerations \n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","Pre-trained EfficientDet Model trained on COCO",["D",1713901407239],"publisher","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/pretrained_efficientdet_tf2_coco","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[242,243,244,245,246,247,248,249,250,251,252,253,254],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_210":290,"_256":291,"_257":292,"_259":260},{"_210":286,"_256":287,"_257":288,"_259":260},{"_210":284,"_256":284,"_257":285,"_259":260},{"_210":282,"_256":282,"_257":283,"_259":260},{"_210":279,"_256":280,"_257":281,"_259":260},{"_210":275,"_256":276,"_257":277,"_259":260},{"_210":273,"_256":273,"_257":274,"_259":260},{"_210":271,"_256":271,"_257":272,"_259":260},{"_210":269,"_256":269,"_257":270,"_259":260},{"_210":266,"_256":266,"_257":267,"_259":260},{"_210":264,"_256":264,"_257":265,"_259":260},{"_210":262,"_256":262,"_257":263,"_259":260},{"_210":255,"_256":255,"_257":258,"_259":260},"Transfer Learning Toolkit","value","filter",{"_261":260,"_256":255},"category","label","field","Transfer Learning",{"_261":260,"_256":262},"TLT",{"_261":260,"_256":264},"TAO Toolkit",{"_261":268,"_256":266},"framework","TAO",{"_261":260,"_256":269},"Smart Infrastructure",{"_261":260,"_256":271},"Smart City",{"_261":260,"_256":273},"Metropolis","pltfm_metropolis",{"_261":278,"_256":276},"platform","DeepStream","pltfm_deepstream",{"_261":278,"_256":280},"Deep Learning",{"_261":260,"_256":282},"CV",{"_261":260,"_256":284},"Computer Vision","soln_computer_vision",{"_261":289,"_256":287},"technology","AI","soln_ai",{"_261":289,"_256":291},{"_107":1426,"_109":1427},"ngcApps",[1379],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1360":1361,"_1362":1363,"_1364":1365,"_1366":1367,"_1368":304,"_138":330,"_1369":1370,"_1371":56,"_1372":86,"_1373":86,"_1374":1375,"_1376":86,"_1377":1378},"amplitude",{"_1357":1358,"_964":-7,"_1359":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1351":1352,"_1353":1354,"_1355":1356},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1794166027826,"1.30.0","defaultSearchSuggestions",{"_1234":171,"_1235":1236,"_1237":1238,"_1239":1240},"announcements",[],"consentState",{"_366":1224,"_1225":31,"_1226":31,"_1227":86,"_1228":1229},"consentConfig",{"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367},"showContactAdmin",[],[],[343,344,345,346,347],{"_45":59,"_47":70,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":68,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":59,"_47":60,"_49":102,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":50,"_51":52,"_53":54,"_55":56},{"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_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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