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 and classification.
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 pretrained weights that may be used as a starting point with the following object detection networks in Train Adapt Optimize (TAO) Toolkit to facilitate transfer learning.
It is trained on a subset of the Google OpenImages dataset. Following backbones are supported with these detection networks.
Some combinations might not be supported. See the matrix below for all supported combinations.
To see the full list of all the backbones, scroll over to the version history tab.
Note: These are unpruned models with just the feature extractor weights, and may not be used without re-training in an object detection application
ResNet101 model is currently only supported for FasterRCNN currently. Please make sure to turn set the
all_projections field to
False in the spec file when training a
ResNet101 model. For more information about this parameter please refer to the TAO Getting Started Guide.
Note: The pre-trained weights in this model are only for the detection networks above and shouldn't be used for DetectNet_v2 based object detection models. For pre-trained weights with DetectNet_v2, click here
The object detection apps in TAO expect data in KITTI file 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.
Before running the container, use docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.
Install the NGC CLI from ngc.nvidia.com
To view all the backbones that are supported by object detection architecture in TAO:
ngc registry model list nvidia/tao_pretrained_object_detection:*
ngc registry model download-version nvidia/tao_pretrained_object_detection:<template> --dest <path>
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
Configure the NGC command line interface using the command mentioned below and follow the prompts.
ngc config set
ngc registry resource download-version "nvidia/tao_cv_samples:v1.0.2"
jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
If you wish to run view the notebook from a remote client, please modify the URL as follows:
a.b.c.d is the IP address of the machine running the container.
Get TAO DetectNet_v2 Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
Get TAO EfficientDet Object Detection pre-trained models for DetectNet_v2 architecture from NGC model registry
Get TAO classification pre-trained models from NGC model registry
Get TAO Instance segmentation pre-trained models for MaskRCNN architecture from NGC
Get TAO Semantic segmentation pre-trained models for UNet architecture from NGC
Get Purpose-built models from NGC model registry:
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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.