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 DetectNet_v2 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 DetectNet_v2 networks.
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
Note: When using the ResNet34 model, please set the
all_projections field in the
False. 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 DetectNet_v2 object detection networks and shouldn't be used for YOLOV3, RetinaNet, FasterRCNN, SSD and DSSD based object detection models. For pre-trained weights for those models, 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.
Install the NGC CLI from ngc.nvidia.com
Configure the NGC CLI using the following command
ngc config set
ngc registry model list nvidia/tao_pretrained_detectnet_v2:*
ngc registry model download-version nvidia/tao_pretrained_detectnet_v2:<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 Object Detection pre-trained models for YOLOV4, YOLOV3, FasterRCNN, SSD, DSSD, and RetinaNet architectures 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.