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 models in this instance are feature extractors based on the EfficientNet architecture.

Training

Dataset

These models are trained on a subset of the Google OpenImages 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.

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Install the NGC CLI from ngc.nvidia.com
\n
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Configure the NGC CLI using the following command
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ngc config set\n

To view all the backbones that are supported by object detection architecture in TAO:

ngc registry model list nvidia/tao/pretrained_efficientdet_tf2:*\n

To download the model:

--dest \n\">

ngc registry model download-version nvidia/tao/pretrained_efficientde_tf2t:<template> --dest <path>\n

Instructions to run the sample notebook

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Get the NGC API key from the SETUP tab on the left. Please store this key for future use. Detailed instructions can be found here
\n
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Configure the NGC command line interface using the command mentioned below and follow the prompts.
\n

ngc config set\n

Download the sample notebooks from NGC using the command below

ngc registry resource download-version \"nvidia/tao/getting_started:4.0.0\"\n

Invoke the jupyter notebook using the following command

jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n

Open an internet browser and type in the following URL to start running the notebooks when running on a local machine.

http://0.0.0.0:8888\n

If you wish to run view the notebook from a remote client, please modify the URL as follows:

http://a.b.c.d:8888\n

Where, the a.b.c.d is the IP address of the machine running the container.

Limitations

These are unpruned models with just the feature extractor weights and, may not be used without re-training in an EfficientDet object detection application with TAO Toolkit.

Versions

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

efficientnet-b0

License

This work is licensed under the Creative Commons Attribution 4.0 International License. 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.

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