TAO CV Sample Workflows

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. TAO can train models for common vision AI tasks such as object detection, classification, instance segmentation as well as other complex tasks such as facial landmark, gaze estimation, heart rate estimation and others.

This resource lists out several sample notebooks to walk you through full training workflow using TAO 3.0.

Getting Started

To get started, first choose the model architecture that you want to build, then select the appropriate model card on NGC and then choose one of the supported backbones.

$\"LOGO\"$

Running TAO Toolkit

\n
Setup your python environment using python virtualenv and virtualenvwrapper.
\n
\n
In TAO Toolkit, we have created an abstraction above the container, you will launch all your training jobs from the launcher. No need to manually pull the appropriate container, tao-launcher will handle that. You may install the launcher using pip with the following commands.
\n

pip3 install nvidia-tao\n

Download the sample jupyter notebooks using the command mentioned in the CLI tab and kick start a jupyter server using the following command

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

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Purpose-built Model	Jupyter notebook
PeopleNet	detectnet_v2/detectnet_v2.ipynb
TrafficCamNet	detectnet_v2/detectnet_v2.ipynb
DashCamNet	detectnet_v2/detectnet_v2.ipynb
FaceDetectIR	detectnet_v2/detectnet_v2.ipynb
VehicleMakeNet	classification/classification.ipynb
VehicleTypeNet	classification/classification.ipynb
PeopleSegNet	mask_rcnn/mask_rcnn.ipynb
PeopleSemSegNet	unet/unet_isbi.ipynb
Bodypose Estimation	bpnet/bpnet.ipynb
License Plate Detection	detectnet_v2/detectnet_v2.ipynb
License Plate Recognition	lprnet/lprnet.ipynb
Gaze Estimation	gazenet/gazenet.ipynb
Facial Landmark	fpenet/fpenet.ipynb
Heart Rate Estimation	heartratenet/heartratenet.ipynb
Gesture Recognition	gesturenet/gesturenet.ipynb
Emotion Recognition	emotionnet/emotionnet.ipynb
FaceDetect	facenet/facenet.ipynb
ActionRecognitionNet	action_recognition_net/actionrecognitionnet.ipynb
PoseClassificationNet	pose_classification_net/pose_classificationnet.ipynb
Pointpillars	pointpillars/pointpillars.ipynb

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Open model architecture	Jupyter notebook
DetectNet_v2	detectnet_v2/detectnet_v2.ipynb
FasterRCNN	faster_rcnn/faster_rcnn.ipynb
YOLOV3	yolo_v3/yolo_v3.ipynb
YOLOV4	yolo_v4/yolo_v4.ipynb
YOLOv4-Tiny	yolo_v4_tiny/yolo_v4_tiny.ipynb
SSD	ssd/ssd.ipynb
DSSD	dssd/dssd.ipynb
RetinaNet	retinanet/retinanet.ipynb
MaskRCNN	mask_rcnn/mask_rcnn.ipynb
UNET	unet/unet_isbi.ipynb
Image Classification	classification/classification.ipynb
EfficientDet	efficientdet/efficientdet.ipynb

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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). TAO can train models for common vision AI tasks such as object detection, classification, instance segmentation as well as other complex tasks such as facial landmark, gaze estimation, heart rate estimation and others.\n\nThis resource lists out several sample notebooks to walk you through full training workflow using TAO 3.0.\n\n## Getting Started \n\nTo get started, first choose the model architecture that you want to build, then select the appropriate model card on NGC and then choose one of the supported backbones.\n\n![LOGO](https://github.com/vpraveen-nv/model_card_images/raw/main/api/openimage_table.jpg)\n\n## Running TAO Toolkit \n\n1. Setup your python environment using python `virtualenv` and `virtualenvwrapper`.\n\n2. In TAO Toolkit, we have created an abstraction above the container, you will launch all your training jobs from the launcher. No need to manually pull the appropriate container, tao-launcher will handle that. You may install the launcher using pip with the following commands.\n\n```sh\npip3 install nvidia-tao\n```\n\n2. Download the sample jupyter notebooks using the command mentioned in the CLI tab and kick start a jupyter server using the following command\n\n```sh\njupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n```\n\n|**Purpose-built Model** | **Jupyter notebook** |\n|--|--|\n| PeopleNet | detectnet_v2/detectnet_v2.ipynb |\n| TrafficCamNet | detectnet_v2/detectnet_v2.ipynb |\n| DashCamNet | detectnet_v2/detectnet_v2.ipynb |\n| FaceDetectIR| detectnet_v2/detectnet_v2.ipynb |\n| VehicleMakeNet | classification/classification.ipynb |\n| VehicleTypeNet | classification/classification.ipynb |\n| PeopleSegNet | mask_rcnn/mask_rcnn.ipynb |\n| PeopleSemSegNet | unet/unet_isbi.ipynb |\n| Bodypose Estimation | bpnet/bpnet.ipynb |\n| License Plate Detection | detectnet_v2/detectnet_v2.ipynb |\n| License Plate Recognition | lprnet/lprnet.ipynb |\n| Gaze Estimation | gazenet/gazenet.ipynb |\n| Facial Landmark | fpenet/fpenet.ipynb |\n| Heart Rate Estimation | heartratenet/heartratenet.ipynb |\n| 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	Files	Compressed Size
v1.4.1	205	5.14 MB	June 15, 2022 UTC
v1.4.0	205	5.13 MB	June 3, 2022 UTC
v1.3.0Selected	170	2.96 MB	November 23, 2021 UTC
v1.2.0	144	1.12 MB	August 24, 2021 UTC