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Pre-trained EfficientDet Model trained on COCO

Logo for Pre-trained EfficientDet Model trained on COCO
Pre-trained EfficientDet models trained on COCO to facilitate transfer learning using TAO Toolkit.
Latest Version
October 16, 2023
41.73 MB

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.



The model is trained on ImageNet1K and COCO dataset.


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.


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.


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

  • efficientdet-d0


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.

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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.