TAO Toolkit Quick Start [Deprecated]

This resource has been deprecated. All new TAO tutorials can be found in our GitHub repo.

The NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, simplifies and accelerates the model training process by abstracting away the complexity of AI models and the deep learning framework. You can use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize the model for inference throughput — all without the need for AI expertise or large training datasets.

TAO quick start video.

TAO Toolkit 5.3

TAO Toolkit 5.3 version packages containers, models, Jupyter notebooks, start-up script and Helm chart for K8s deployment. Here are list of assets as part of TAO 5.3

Containers

The containers for the TAO Toolkit 5.3 are hosted on NGC under this instance.

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Container Name	Description	TAG	GitHub Repo
TAO TensorFlow 1	TensorFlow 1.15.x container for training DNNs	nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5	https://github.com/NVIDIA/tao_tensorflow1_backend
TAO TensorFlow 2	TensorFlow 2.11.x container for training DNNs	nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf2.11.0	https://github.com/NVIDIA/tao_tensorflow2_backend
TAO PyTorch	PyTorch container for training DNNs	nvcr.io/nvidia/tao/tao-toolkit:5.3.0-pyt	https://github.com/NVIDIA/tao_pytorch_backend
TAO Deploy	TensorRT container for optimization	nvcr.io/nvidia/tao/tao-toolkit:5.3.0-deploy	https://github.com/NVIDIA/tao_deploy
TAO Data Service	Container for AI-assisted annotation and few other data services	nvcr.io/nvidia/tao/tao-toolkit:5.3.0-data-services	https://github.com/NVIDIA/tao_dataset_suite
TAO Services	Container for TAO services	nvcr.io/nvidia/tao/tao-toolkit:5.3.0-api	https://github.com/NVIDIA/tao_front_end_services

Resources

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Container Name	Description	Location
TAO Toolkit Getting Started	Resource to help get started with TAO Toolkit.	nvidia/tao/tao-getting-started

Helm Chart

The TAO Toolkit Services Helm chart is hosted on NGC at nvidia/tao/tao-toolkit-api

Models

The following models are released as part of the TAO Toolkit.

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Model name	Description	**NGC Instance
EmotionNet	Network to classify emotions from face.	nvidia/tao/emotionnet
TrafficCamNet	4 class object detection network to detect cars in an image.	nvidia/tao/trafficcamnet
Optical Character Detection	Network to detect characters in an image.	nvidia/tao/ocdnet
CitySemSegformer	Model to segment objects in an image.	nvidia/tao/citysemsegformer
DashCamNet	4 class object detection network to detect cars in an image.	nvidia/tao/dashcamnet
PeopleSemSegnet	Semantic segmentation of persons in an image.	nvidia/tao/peoplesemsegnet
PeopleNet	3 class object detection network to detect people in an image.	nvidia/tao/peoplenet
CenterPose - Isaac ROS	Single-stage, keypoint-based method for category-level object pose estimation.	nvidia/tao/centerpose_ros
Re-Identification	Re-Identification network to generate embeddings for identifying persons in different scenes.	nvidia/tao/reidentificationnet--discoverable
Deformable DETR	Model trained on COCO2017 to detect and classify objects.	nvidia/tao/pretrained_deformable_detr_coco
VehicleTypeNet	Resnet18 model to classify a car crop into 1 out 6 car types.	nvidia/tao/vehicletypenet
Action Recognition Net	5 class action recognition network to recognize what people do in an image.	nvidia/tao/actionrecognitionnet
LPDNet	Object Detection network to detect license plates in an image of a car.	nvidia/tao/lpdnet
GestureNet	Classify gestures from hand crop images.	nvidia/tao/gesturenet
BodyPoseNet	Detect body pose from an image.	nvidia/tao/bodyposenet
License Plate Recognition	Model to recognize characters from the image crop of a License Plate.	nvidia/tao/lprnet
FaceDetectIR	1 class object detection network to detect faces in an image.	nvidia/tao/facedetectir
Re-Identification	Re-Identification network to generate embeddings for identifying persons in different scenes.	nvidia/tao/reidentificationnet
Visual ChangeNet Segmentation - (Research-only)	Visual ChangeNet-Segmentation (Research-only)	nvidia/tao/visual_changenet_segmentation_levircd
Visual ChangeNet Segmentation - (Commercial)	Visual ChangeNet-Segmentation (Commercial)	nvidia/tao/visual_changenet_segmentation_landsatscd
DINO	Model trained on COCO2017 to detect and classify objects.	nvidia/tao/pretrained_dino_coco
PointPillarNet	Model to detect one or more objects from a LIDAR point cloud file and return 3D bounding boxes.	nvidia/tao/pointpillarnet
PeopleSemSegformer	Model to segment persons in an image.	nvidia/tao/peoplesemsegformer
Optical Character Recognition	Model to recognise characters from a preceding OCDNet model.	nvidia/tao/ocrnet
HeartRateNet	Estimate heart-rate non-invasively from RGB facial videos.	nvidia/tao/heartratenet
Retail Object Detection	EfficientDet based object detection network to detect retail objects on a checkout counter.	nvidia/tao/retail_object_detection
FaceDetect	Detect faces from an image.	nvidia/tao/facenet
Optical Inspection	Model to detect defects in soldered components on a Printed Circuit Board.	nvidia/tao/optical_inspection
PeopleNet - AMR	3 class object detection network to detect people in an image.	nvidia/tao/peoplenet_amr
Mask Auto Label	Pretrained model to generate semantic segmentation labels.	nvidia/tao/mask_auto_label
Retail Object Recognition	Embedding generator model to recognize objects on a checkout counter.	nvidia/tao/retail_object_recognition
PeopleNet Transformer	3 class object detection network to detect people in an image.	nvidia/tao/peoplenet_transformer
Visual ChangeNet Classification	Visual ChangeNet - Classification Models	nvidia/tao/visual_changenet_classification
PeopleSegNet	1 class instance segmentation network to detect and segment instances of people in an image.	nvidia/tao/peoplesegnet
VehicleMakeNet	Resnet18 model to classify a car crop into 1 out 20 car brands.	nvidia/tao/vehiclemakenet
Facial Landmarks Estimation	Detect fiducial keypoints from an image of a face.	nvidia/tao/fpenet
Pose Classification	Pose classification network to classify poses of people from their skeletons.	nvidia/tao/poseclassificationnet
BodyPose3DNet	3D human pose estimation network to predict 34 keypoints in 3D of a person in an image.	nvidia/tao/bodypose3dnet
PeopleSemSegNet AMR	Semantic segmentation of persons in an image.	nvidia/tao/peoplesemsegnet_amr
PCB Defect Classification	Model to classify defects in soldered components on a Printed Circuit Board.	nvidia/tao/pcb_classification
Gaze Estimation	Detect a persons eye gaze point of regard and gaze vector.	nvidia/tao/gazenet

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Model name	Description	**NGC Instance
TAO Pretrained EfficientDet-TF2	Pretrained efficientnet backbones for TAO Toolkit's efficientdet-tf2	nvidia/tao/pretrained_efficientdet_tf2
Pre-trained Deformable DETR ImageNet weights	Pre-trained deformable_detr weights trained on ImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_deformable_detr_imagenet
Pre-trained SegFormer ImageNet weights	Pre-trained SegFormer weights trained on ImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_segformer_imagenet
Pre-trained SegFormer NvImageNet weights	Pre-trained SegFormer weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_segformer_nvimagenet
Pre-trained DINO ImageNet weights	Pre-trained DINO weights trained on ImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_dino_imagenet
Pre-trained FAN based NVImageNet Classification weights	Pre-trained FAN weights trained on NVImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_fan_classification_nvimagenet
Pre-trained ImageNet Back bones weights	Pre-trained weights trained on ImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_imagenet_backbones
Pre-trained DINO NvImageNet weights	Pre-trained DINO weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_dino_nvimagenet
TAO Pretrained Classification-TF2	Pretrained backbones for TAO Toolkit TF2 image classification	nvidia/tao/pretrained_classification_tf2
Pre-trained Segformer - CityScapes	Pre-trained segformer models trained on CityScapes.	nvidia/tao/pretrained_segformer_cityscapes
Pre-trained GCViT NVImageNet Classification weights	Pre-trained GcViT weights trained on NVImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_gcvit_classification_nvimagenet
TAO Pretrained EfficientDet	Pretrained weights to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_efficientdet
TAO Pretrained Semantic Segmentation	Pretrained weights to facilitate transfer learning using Transfer Learning Toolkit.	nvidia/tao/pretrained_semantic_segmentation
Pre-trained FAN based ImageNet Classification weights	Pre-trained FAN weights trained on ImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_fan_classification_imagenet
Pre-trained Deformable DETR NvImageNet weights	Pre-trained deformable_detr weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_deformable_detr_nvimagenet
Pre-trained GCViT ImageNet Classification weights	Pre-trained GCViT weights trained on ImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_gcvit_classification_imagenet
Pre-trained NVImageNet Back bones weights	Pre-trained weights trained on NVImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_nvimagenet_backbones
Pre-trained EfficientDet Model trained on COCO	Pre-trained EfficientDet models trained on COCO to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_efficientdet_tf2_coco
Pretrained Mask Auto Label	Pretrained model to facilitate transfer learning for MAL on TAO Toolkit	nvidia/tao/pretrained_mask_auto_label
TAO Pretrained Object Detection	Pretrained weights to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_object_detection
TAO Pretrained DetectNet V2	Pretrained weights to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_detectnet_v2
Pre-trained EfficientDet NvImageNet backbones	Pre-trained EfficientNet bacbone weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_efficientdet_tf2_nvimagenet
TAO Pretrained Instance Segmentation	Pretrained weights to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_instance_segmentation
TAO Pretrained Classification	Pretrained weights to facilitate transfer learning using TAO Toolkit.	nvidia/tao/pretrained_classification

Requirements

Recommended Hardware requirements

The following system configuration is recommended to achieve reasonable training performance with TAO Toolkit and supported models provided:

16 GB system RAM
16 GB of GPU RAM
8 core CPU
1 NVIDIA GPU
100 GB of SSD space

TAO Toolkit is supported on discrete GPUs, such as H100, A100, A40, A30, A2, A16, A100x, A30x, V100, T4, Titan-RTX and Quadro-RTX.

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Note: TAO Toolkit is not supported on GPU's before the Pascal generation
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Software requirements

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Software	Version	Comment
Ubuntu LTS	20.04
python	>=3.6.9<3.7	Not needed if you are using TAO API (See #3 below)
docker-ce	>19.03.5	Not needed if you are using TAO API (See #3 below)
docker-API	1.40	Not needed if you are using TAO API (See #3 below)
`nvidia-container-toolkit`	>1.3.0-1	Not needed if you are using TAO API (See #3 below)
nvidia-container-runtime	3.4.0-1	Not needed if you are using TAO API (See #3 below)
nvidia-docker2	2.5.0-1	Not needed if you are using TAO API (See #3 below)
nvidia-driver	>525.85	Not needed if you are using TAO API (See #3 below)
python-pip	>21.06	Not needed if you are using TAO API (See #3 below)

Package Content

Download the TAO package which contains startup scripts, Jupyter notebooks and config files.
\nTAO is supported on Google Colab; if you want to try on Colab, you can skip this step and directly scroll down to #4 in the How to run TAO section.

ngc registry resource download-version nvidia/tao/tao-getting-started:5.3.0 --dest ./\ncd ./getting_started_v5.3.0\n

File Hierarchy

setup\n    |--> quickstart_launcher.sh\n    |--> quickstart_api_bare_metal\n    |--> quickstart_api_aws_eks\n    |--> quickstart_api_azure_aks\n    |--> quickstart_api_gcp_gke\nnotebooks\n    |--> tao_api_starter_kit\n        |--> api\n            |--> automl\n            |--> end2end\n            |--> dataset_prepare\n        |--> client\n            |--> automl\n            |--> end2end\n            |--> dataset_prepare\n    |--> tao_launcher_starter_kit\n        |--> dino\n        |--> deformable_detr\n        |--> classification_pyt\n        |--> ocdnet\n        |-->  ...\n    |--> tao_data_services\n        |--> data\n        |-->  ...\n

How to run TAO?

TAO is available as a docker container or as a collection of Python wheels.

There are 4 ways to run TAO depending on user preference and their setup. See the full list below.

1. Launcher CLI

The TAO Launcher is a lightweight Python based CLI application to run TAO. The launcher basically acts as a front-end for the multiple TAO Toolkit containers built on both PyTorch and Tensorflow. The multiple containers essentially get launched automatically based on the type of model you plan to use for your computer vision or conversational AI use-cases.

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TAO Launcher
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To get started, use the setup/quickstart_launcher.sh to validate your setup and install TAO launcher. Jupyter notebooks to train using the Launcher is provided under notebooks/launcher_starter_kit.

Detail instructions on installing pre-requisite and setup is provided in TAO documentation - Launcher

2. Directly from Container

Users have option to also run TAO directly using the docker container. To use container directly, user needs to know which container to pull. There are multiple containers under TAO, and depending on the model that you want to train you will need to pull the appropriate container. This is not required when using the Launcher CLI.

export DOCKER_REGISTRY=\"nvcr.io\"\nexport DOCKER_NAME=\"nvidia/tao/tao-toolkit\"\nexport DOCKER_TAG=\"***\" ## for TensorFlow docker\nexport DOCKER_TAG=\"***\" ## for PyTorch docker\nexport DOCKER_CONTAINER=$DOCKER_REGISTRY/$DOCKER_NAME:$DOCKER_TAG\n\ndocker run -it --rm --gpus all -v /path/in/host:/path/in/docker $DOCKER_CONTAINER \\\ndetectnet_v2 train -e /path/to/experiment/spec.txt -r /path/to/results/dir -k $KEY --gpus 4\n

More information about running directly from docker is provided in TAO documentation - Container

3. TAO APIs

TAO Toolkit API is a Kubernetes service that enables building end-to-end AI models using REST APIs. The API service can be installed on a Kubernetes cluster (local / AWS EKS) using a Helm chart along with minimal dependencies. TAO toolkit jobs can be run using GPUs available on the cluster and can scale to a multi-node setting. Users can use a TAO client CLI to interact with TAO services remotely or can integrate it in their own apps and services directly using REST APIs.

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TAO API
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To get started, use the provided one-click deploy script to deploy either on bare-metal setup or on managed Kubernetes service like Amazon EKS. Jupyter notebooks to train using the APIs directly or using the client app is provided under notebooks/api_starter_kit
\nsetup/quickstart_api_bare_metal
\nsetup/quickstart_api_aws_eks

More information about setting up the API services and the API is provided in TAO documentation - API

4. Python Wheel

Users can also run TAO directly on bare-metal without docker or K8s. Users can deploy TAO notebooks directly on Google Colab without having to configure infrastructure. The full instructions are provided in the Colab notebook below.

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CV Task	Model Arch	One-click Deploy
Classification	ResNet18	Train on Colab
Multi-task Classification	ResNet18	Train on Colab
Object Detection	DSSD	Train on Colab
Object Detection	EfficientDet	Train on Colab
Object Detection	RetinaNet	Train on Colab
Object Detection	SSD	Train on Colab
Object Detection	YOLOv3	Train on Colab
Object Detection	YOLOv4	Train on Colab
Object Detection	YoloV4 Tiny	Train on Colab
Action Recognition	ActionRecognition	Train on Colab
OCR	LPRNet	Train on Colab
Pose Action Classification	PoseClassificationNet	Train on Colab
Emotion Recognition	EmotionNet	Train on Colab
Gesture Recognition	GestureNet	Train on Colab
Heart Rate Estimation	HeartRateNet	Train on Colab

After starting TAO service locally or remotely, start Jupyter notebook

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

Open an internet browser on localhost and navigate to the following URL: http://0.0.0.0:8888

Open the notebook that you are interested in training and start training.

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Note: All the instructions to train, prune, optimize and download pretrained models are provided in the notebook.
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Jupyter notebooks

All Notebooks and required spec files are provided in this package. The table below maps which notebook to use for fine-tuning either a purpose-build models like PeopleNet or an open model architecture like YOLO.

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Purpose-built Model	Launcher CLI notebook
PCB Classification	notebooks/tao_launcher_starter_kit/classification_pyt/classification.ipynb
Retail Object Recognition	notebooks/tao_launcher_starter_kit/metric_learning_recognition/metric_learning_recognition.ipynb
Optical Inspection	notebook/tao_launcher_starter_kit/optical_inspection/OpticalInspection.ipynb
Mask Auto Label	notebooks/tao_launcher_starter_kit/mal/mal.ipynb
OCRNet	notebooks/tao_launcher_starter_kit/ocrnet/ocrnet.ipynb
OCDNet	notebooks/tao_launcher_starter_kit/ocdnet/ocdnet.ipynb
PeopleSemSegFormer	notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb
PeopleNet	notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
TrafficCamNet	notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
DashCamNet	notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
FaceDetectIR	notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
VehicleMakeNet	notebooks/tao_launcher_starter_kit/classification/classification.ipynb
VehicleTypeNet	notebooks/tao_launcher_starter_kit/classification/classification.ipynb
PeopleSegNet	notebooks/tao_launcher_starter_kit/mask_rcnn/mask_rcnn.ipynb
PeopleSemSegNet	notebooks/tao_launcher_starter_kit/unet/unet_isbi.ipynb
Bodypose Estimation	notebooks/tao_launcher_starter_kit/bpnet/bpnet.ipynb
License Plate Detection	notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
License Plate Recognition	notebooks/tao_launcher_starter_kit/lprnet/lprnet.ipynb
Facial Landmark	notebooks/tao_launcher_starter_kit/fpenet/fpenet.ipynb
FaceDetect	notebooks/tao_launcher_starter_kit/facenet/facenet.ipynb
ActionRecognitionNet	notebooks/tao_launcher_starter_kit/action_recognition_net/actionrecognitionnet.ipynb
PoseClassificationNet	notebooks/tao_launcher_starter_kit/pose_classification_net/pose_classificationnet.ipynb
ReIdentificationNet	notebooks/tao_launcher_starter_kit/re_identification_net/reidentificationnet.ipynb
Visual ChangeNet Classification	notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_classification.ipynb
Visual ChangeNet Segmentation - Research Only (LevirCD)	notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation.ipynb
Visual ChangeNet Segmentation - Commercial (LandSATSCD)	notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation.ipynb

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Open model architecture	Jupyter notebook
Deformable DETR	notebooks/tao_launcher_starter_kit/deformable_detr/deformable_detr.ipynb
DINO	notebooks/tao_launcher_starter_kit/dino/dino.ipynb
Image Classification	notebooks/tao_launcher_starter_kit/classification_pyt/classification_pyt.ipynb
Image Classification	notebooks/tao_launcher_starter_kit/classification_tf2/classification.ipynb
Image Classification	notebooks/tao_launcher_starter_kit/classification_tf1/classification.ipynb
Optical Inspection	notebooks/tao_launcher_starter_kit/optical_inspection/optical_inspection.ipynb
Metric Learning Recognition	notebooks/tao_launcher_starter_kit/metric_learning_recognition/metric_learning_recognition.ipynb
Segformer	notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb
DetectNet_v2	notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
FasterRCNN	notebooks/tao_launcher_starter_kit/faster_rcnn/faster_rcnn.ipynb
YOLOV3	notebooks/tao_launcher_starter_kit/yolo_v3/yolo_v3.ipynb
YOLOV4	notebooks/tao_launcher_starter_kit/yolo_v4/yolo_v4.ipynb
YOLOv4-Tiny	notebooks/tao_launcher_starter_kit/yolo_v4_tiny/yolo_v4_tiny.ipynb
SSD	notebooks/tao_launcher_starter_kit/ssd/ssd.ipynb
DSSD	notebooks/tao_launcher_starter_kit/dssd/dssd.ipynb
RetinaNet	notebooks/tao_launcher_starter_kit/retinanet/retinanet.ipynb
MaskRCNN	notebooks/tao_launcher_starter_kit/mask_rcnn/mask_rcnn.ipynb
UNET	notebooks/tao_launcher_starter_kit/unet/unet_isbi.ipynb
EfficientDet	notebooks/tao_launcher_starter_kit/efficientdet/efficientdet.ipynb
Mask Auto Label	notebooks/tao_launcher_starter_kit/mal/mal.ipynb
Visual ChangeNet Classification	notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_classification.ipynb
Visual ChangeNet Segmentation	notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation.ipynb

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Important Links

TAO Toolkit Documentation

Blogs

Train like a 'pro' with AutoML in TAO
\nDeploy TAO on Azure ML
\nSynthetic Data and TAO
\nAction Recognition Blog
\nReal-time License Plate Detection
\n2 Pose Estimation: Part 1
\nPart 2
\nBuilding ConvAI with TAO Toolkit

License

TAO Toolkit getting Started\nLicense for TAO containers is included in the banner of the container. License for the pre-trained models are available with the model cards on NGC. By pulling and using the Train Adapt Optimize (TAO) Toolkit container to download models, you accept the terms and conditions of these licenses.

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All new TAO tutorials can be found in our [GitHub repo](https://github.com/NVIDIA/tao_tutorials).**\n\nThe NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, simplifies and accelerates the model training process by abstracting away the complexity of AI models and the deep learning framework. You can use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize the model for inference throughput — all without the need for AI expertise or large training datasets.\n\n[TAO quick start video](https://www.nvidia.com/en-us/on-demand/session/other2022-tao/).\n\n## TAO Toolkit 5.3\nTAO Toolkit 5.3 version packages containers, models, Jupyter notebooks, start-up script and Helm chart for K8s deployment. Here are list of assets as part of TAO 5.3\n\n### Containers\n\nThe containers for the TAO Toolkit 5.3 are hosted on NGC under this [instance](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/containers/tao-toolkit\n).\n\n| **Container Name** | **Description** | **TAG**| **GitHub Repo** |\n| :-- | :-- | :--| :--|\n| TAO TensorFlow 1 | TensorFlow 1.15.x container for training DNNs | nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf1.15.5 | https://github.com/NVIDIA/tao_tensorflow1_backend |\n| TAO TensorFlow 2 | TensorFlow 2.11.x container for training DNNs | nvcr.io/nvidia/tao/tao-toolkit:5.0.0-tf2.11.0 | https://github.com/NVIDIA/tao_tensorflow2_backend |\n| TAO PyTorch | PyTorch container for training DNNs | nvcr.io/nvidia/tao/tao-toolkit:5.3.0-pyt | https://github.com/NVIDIA/tao_pytorch_backend |\n| TAO Deploy | TensorRT container for optimization | nvcr.io/nvidia/tao/tao-toolkit:5.3.0-deploy | https://github.com/NVIDIA/tao_deploy |\n| TAO Data Service | Container for AI-assisted annotation and few other data services | nvcr.io/nvidia/tao/tao-toolkit:5.3.0-data-services | https://github.com/NVIDIA/tao_dataset_suite |\n| TAO Services | Container for TAO services | nvcr.io/nvidia/tao/tao-toolkit:5.3.0-api | https://github.com/NVIDIA/tao_front_end_services |\n\n### Resources\n\n| **Container Name** | **Description** | **Location**|\n| :-- | :-- | :--|\n| TAO Toolkit Getting Started | Resource to help get started with TAO Toolkit. | [nvidia/tao/tao-getting-started](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/resources/tao-getting-started) |\n\n### Helm Chart\nThe TAO Toolkit Services Helm chart is hosted on NGC at [nvidia/tao/tao-toolkit-api](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/helm-charts/tao-toolkit-api)\n\n### Models\nThe following models are released as part of the TAO Toolkit.\n\n| **Model name** | **Description** | **NGC Instance |\n|-------------------------------------------------|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| EmotionNet | Network to classify emotions from face. | [nvidia/tao/emotionnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/emotionnet) |\n| TrafficCamNet | 4 class object detection network to detect cars in an image. | [nvidia/tao/trafficcamnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/trafficcamnet) |\n| Optical Character Detection | Network to detect characters in an image. | [nvidia/tao/ocdnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet) |\n| CitySemSegformer | Model to segment objects in an image. | [nvidia/tao/citysemsegformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer) |\n| DashCamNet | 4 class object detection network to detect cars in an image. | [nvidia/tao/dashcamnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/dashcamnet) |\n| PeopleSemSegnet | Semantic segmentation of persons in an image. | [nvidia/tao/peoplesemsegnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegnet) |\n| PeopleNet | 3 class object detection network to detect people in an image. | [nvidia/tao/peoplenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet) |\n| CenterPose - Isaac ROS | Single-stage, keypoint-based method for category-level object pose estimation. | [nvidia/tao/centerpose_ros](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/centerpose_ros) |\n| Re-Identification | Re-Identification network to generate embeddings for identifying persons in different scenes. | [nvidia/tao/reidentificationnet--discoverable](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/reidentificationnet--discoverable) |\n| Deformable DETR | Model trained on COCO2017 to detect and classify objects. | [nvidia/tao/pretrained_deformable_detr_coco](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_deformable_detr_coco) |\n| VehicleTypeNet | Resnet18 model to classify a car crop into 1 out 6 car types. | [nvidia/tao/vehicletypenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/vehicletypenet) |\n| Action Recognition Net | 5 class action recognition network to recognize what people do in an image. | [nvidia/tao/actionrecognitionnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/actionrecognitionnet) |\n| LPDNet | Object Detection network to detect license plates in an image of a car. | [nvidia/tao/lpdnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/lpdnet) |\n| GestureNet | Classify gestures from hand crop images. | [nvidia/tao/gesturenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/gesturenet) |\n| BodyPoseNet | Detect body pose from an image. | [nvidia/tao/bodyposenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/bodyposenet) |\n| License Plate Recognition | Model to recognize characters from the image crop of a License Plate. | [nvidia/tao/lprnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/lprnet) |\n| FaceDetectIR | 1 class object detection network to detect faces in an image. | [nvidia/tao/facedetectir](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/facedetectir) |\n| Re-Identification | Re-Identification network to generate embeddings for identifying persons in different scenes. | [nvidia/tao/reidentificationnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/reidentificationnet) |\n| Visual ChangeNet Segmentation - (Research-only) | Visual ChangeNet-Segmentation (Research-only) | [nvidia/tao/visual_changenet_segmentation_levircd](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_segmentation_levircd) |\n| Visual ChangeNet Segmentation - (Commercial) | Visual ChangeNet-Segmentation (Commercial) | [nvidia/tao/visual_changenet_segmentation_landsatscd](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_segmentation_landsatscd) |\n| DINO | Model trained on COCO2017 to detect and classify objects. | [nvidia/tao/pretrained_dino_coco](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_dino_coco) |\n| PointPillarNet | Model to detect one or more objects from a LIDAR point cloud file and return 3D bounding boxes. | [nvidia/tao/pointpillarnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet) |\n| PeopleSemSegformer | Model to segment persons in an image. | [nvidia/tao/peoplesemsegformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer) |\n| Optical Character Recognition | Model to recognise characters from a preceding OCDNet model. | [nvidia/tao/ocrnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet) |\n| HeartRateNet | Estimate heart-rate non-invasively from RGB facial videos. | [nvidia/tao/heartratenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/heartratenet) |\n| Retail Object Detection | EfficientDet based object detection network to detect retail objects on a checkout counter. | [nvidia/tao/retail_object_detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection) |\n| FaceDetect | Detect faces from an image. | [nvidia/tao/facenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/facenet) |\n| Optical Inspection | Model to detect defects in soldered components on a Printed Circuit Board. | [nvidia/tao/optical_inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection) |\n| PeopleNet - AMR | 3 class object detection network to detect people in an image. | [nvidia/tao/peoplenet_amr](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_amr) |\n| Mask Auto Label | Pretrained model to generate semantic segmentation labels. | [nvidia/tao/mask_auto_label](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/mask_auto_label) |\n| Retail Object Recognition | Embedding generator model to recognize objects on a checkout counter. | [nvidia/tao/retail_object_recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition) |\n| PeopleNet Transformer | 3 class object detection network to detect people in an image. | [nvidia/tao/peoplenet_transformer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplenet_transformer) |\n| Visual ChangeNet Classification | Visual ChangeNet - Classification Models | [nvidia/tao/visual_changenet_classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/visual_changenet_classification) |\n| PeopleSegNet | 1 class instance segmentation network to detect and segment instances of people in an image. | [nvidia/tao/peoplesegnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesegnet) |\n| VehicleMakeNet | Resnet18 model to classify a car crop into 1 out 20 car brands. | [nvidia/tao/vehiclemakenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/vehiclemakenet) |\n| Facial Landmarks Estimation | Detect fiducial keypoints from an image of a face. | [nvidia/tao/fpenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/fpenet) |\n| Pose Classification | Pose classification network to classify poses of people from their skeletons. | [nvidia/tao/poseclassificationnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/poseclassificationnet) |\n| BodyPose3DNet | 3D human pose estimation network to predict 34 keypoints in 3D of a person in an image. | [nvidia/tao/bodypose3dnet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/bodypose3dnet) |\n| PeopleSemSegNet AMR | Semantic segmentation of persons in an image. | [nvidia/tao/peoplesemsegnet_amr](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegnet_amr) |\n| PCB Defect Classification | Model to classify defects in soldered components on a Printed Circuit Board. | [nvidia/tao/pcb_classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification) |\n| Gaze Estimation | Detect a persons eye gaze point of regard and gaze vector. | [nvidia/tao/gazenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/gazenet) |\n\n| **Model name** | **Description** | **NGC Instance |\n|---------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| TAO Pretrained EfficientDet-TF2 | Pretrained efficientnet backbones for TAO Toolkit's efficientdet-tf2 | [nvidia/tao/pretrained_efficientdet_tf2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_efficientdet_tf2) |\n| Pre-trained Deformable DETR ImageNet weights | Pre-trained deformable_detr weights trained on ImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_deformable_detr_imagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_deformable_detr_imagenet) |\n| Pre-trained SegFormer ImageNet weights | Pre-trained SegFormer weights trained on ImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_segformer_imagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_segformer_imagenet) |\n| Pre-trained SegFormer NvImageNet weights | Pre-trained SegFormer weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_segformer_nvimagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_segformer_nvimagenet) |\n| Pre-trained DINO ImageNet weights | Pre-trained DINO weights trained on ImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_dino_imagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_dino_imagenet) |\n| Pre-trained FAN based NVImageNet Classification weights | Pre-trained FAN weights trained on NVImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_fan_classification_nvimagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_fan_classification_nvimagenet) |\n| Pre-trained ImageNet Back bones weights | Pre-trained weights trained on ImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_imagenet_backbones]() |\n| Pre-trained DINO NvImageNet weights | Pre-trained DINO weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_dino_nvimagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_dino_nvimagenet) |\n| TAO Pretrained Classification-TF2 | Pretrained backbones for TAO Toolkit TF2 image classification | [nvidia/tao/pretrained_classification_tf2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_classification_tf2) |\n| Pre-trained Segformer - CityScapes | Pre-trained segformer models trained on CityScapes. | [nvidia/tao/pretrained_segformer_cityscapes](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_segformer_cityscapes) |\n| Pre-trained GCViT NVImageNet Classification weights | Pre-trained GcViT weights trained on NVImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_gcvit_classification_nvimagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_gcvit_classification_nvimagenet) |\n| TAO Pretrained EfficientDet | Pretrained weights to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_efficientdet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_efficientdet) |\n| TAO Pretrained Semantic Segmentation | Pretrained weights to facilitate transfer learning using Transfer Learning Toolkit. | [nvidia/tao/pretrained_semantic_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_semantic_segmentation) |\n| Pre-trained FAN based ImageNet Classification weights | Pre-trained FAN weights trained on ImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_fan_classification_imagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_fan_classification_imagenet) |\n| Pre-trained Deformable DETR NvImageNet weights | Pre-trained deformable_detr weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_deformable_detr_nvimagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_deformable_detr_nvimagenet) |\n| Pre-trained GCViT ImageNet Classification weights | Pre-trained GCViT weights trained on ImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_gcvit_classification_imagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_gcvit_classification_imagenet) |\n| Pre-trained NVImageNet Back bones weights | Pre-trained weights trained on NVImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_nvimagenet_backbones]() |\n| Pre-trained EfficientDet Model trained on COCO | Pre-trained EfficientDet models trained on COCO to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_efficientdet_tf2_coco](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_efficientdet_tf2_coco) |\n| Pretrained Mask Auto Label | Pretrained model to facilitate transfer learning for MAL on TAO Toolkit | [nvidia/tao/pretrained_mask_auto_label](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_mask_auto_label) |\n| TAO Pretrained Object Detection | Pretrained weights to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_object_detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_object_detection) |\n| TAO Pretrained DetectNet V2 | Pretrained weights to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_detectnet_v2](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_detectnet_v2) |\n| Pre-trained EfficientDet NvImageNet backbones | Pre-trained EfficientNet bacbone weights trained on NvImageNet to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_efficientdet_tf2_nvimagenet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_efficientdet_tf2_nvimagenet) |\n| TAO Pretrained Instance Segmentation | Pretrained weights to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_instance_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_instance_segmentation) |\n| TAO Pretrained Classification | Pretrained weights to facilitate transfer learning using TAO Toolkit. | [nvidia/tao/pretrained_classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pretrained_classification) |\n## Requirements\n\n### Recommended Hardware requirements\n\nThe following system configuration is recommended to achieve reasonable training performance with TAO Toolkit and supported models provided:\n\n* 16 GB system RAM\n* 16 GB of GPU RAM\n* 8 core CPU\n* 1 NVIDIA GPU\n* 100 GB of SSD space\n\nTAO Toolkit is supported on discrete GPUs, such as H100, A100, A40, A30, A2, A16, A100x, A30x, V100, T4, Titan-RTX and Quadro-RTX.\n\n> Note: TAO Toolkit is not supported on GPU's before the Pascal generation\n\n### Software requirements\n\n| **Software** | **Version** | **Comment** |\n| :--- | :--- | :-- |\n| Ubuntu LTS | 20.04 ||\n| python | >=3.6.9<3.7 | Not needed if you are using TAO API \$See #3 below\$ |\n| docker-ce | >19.03.5 | Not needed if you are using TAO API \$See #3 below\$ |\n| docker-API | 1.40 | Not needed if you are using TAO API \$See #3 below\$ |\n| `nvidia-container-toolkit` | >1.3.0-1 | Not needed if you are using TAO API \$See #3 below\$ |\n| nvidia-container-runtime | 3.4.0-1 | Not needed if you are using TAO API \$See #3 below\$ |\n| nvidia-docker2 | 2.5.0-1 | Not needed if you are using TAO API \$See #3 below\$ |\n| nvidia-driver | >525.85 | Not needed if you are using TAO API \$See #3 below\$ |\n| python-pip | >21.06 | Not needed if you are using TAO API \$See #3 below\$ |\n\n## Package Content\n\nDownload the TAO package which contains startup scripts, Jupyter notebooks and config files.
\nTAO is supported on Google Colab; if you want to try on Colab, you can skip this step and directly scroll down to [#4 in the How to run TAO](#colab) section.\n\n ngc registry resource download-version nvidia/tao/tao-getting-started:5.3.0 --dest ./\n cd ./getting_started_v5.3.0\n\n\n### File Hierarchy\n\n setup\n |--> quickstart_launcher.sh\n |--> quickstart_api_bare_metal\n |--> quickstart_api_aws_eks\n |--> quickstart_api_azure_aks\n |--> quickstart_api_gcp_gke\n notebooks\n |--> tao_api_starter_kit\n |--> api\n |--> automl\n |--> end2end\n |--> dataset_prepare\n |--> client\n |--> automl\n |--> end2end\n |--> dataset_prepare\n |--> tao_launcher_starter_kit\n |--> dino\n |--> deformable_detr\n |--> classification_pyt\n |--> ocdnet\n |--> ...\n |--> tao_data_services\n |--> data\n |--> ...\n\n## How to run TAO?\n\nTAO is available as a docker container or as a collection of Python wheels.\n\nThere are 4 ways to run TAO depending on user preference and their setup. See the full list below.\n\n#### 1. Launcher CLI\n\nThe TAO Launcher is a lightweight Python based CLI application to run TAO. The launcher basically acts as a front-end for the multiple TAO Toolkit containers built on both PyTorch and Tensorflow. The multiple containers essentially get launched automatically based on the type of model you plan to use for your computer vision or conversational AI use-cases. \n\n

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TAO Launcher
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\n\nTo get started, use the `setup/quickstart_launcher.sh` to validate your setup and install TAO launcher. Jupyter notebooks to train using the Launcher is provided under `notebooks/launcher_starter_kit`.\n\nDetail instructions on installing pre-requisite and setup is provided in [TAO documentation - Launcher](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html)\n\n#### 2. Directly from Container\n\nUsers have option to also run TAO directly using the docker container. To use container directly, user needs to know which container to pull. There are multiple containers under TAO, and depending on the model that you want to train you will need to pull the appropriate container. This is not required when using the Launcher CLI.\n\n export DOCKER_REGISTRY=\"nvcr.io\"\n export DOCKER_NAME=\"nvidia/tao/tao-toolkit\"\n export DOCKER_TAG=\"***\" ## for TensorFlow docker\n export DOCKER_TAG=\"***\" ## for PyTorch docker\n export DOCKER_CONTAINER=$DOCKER_REGISTRY/$DOCKER_NAME:$DOCKER_TAG\n\n docker run -it --rm --gpus all -v /path/in/host:/path/in/docker $DOCKER_CONTAINER \\\n detectnet_v2 train -e /path/to/experiment/spec.txt -r /path/to/results/dir -k $KEY --gpus 4\n\nMore information about running directly from docker is provided in [TAO documentation - Container](https://docs.nvidia.com/tao/tao-toolkit/text/working_with_the_containers.html)\n\n#### 3. TAO APIs\n\nTAO Toolkit API is a Kubernetes service that enables building end-to-end AI models using REST APIs. The API service can be installed on a Kubernetes cluster (local / AWS EKS) using a Helm chart along with minimal dependencies. TAO toolkit jobs can be run using GPUs available on the cluster and can scale to a multi-node setting. Users can use a TAO client CLI to interact with TAO services remotely or can integrate it in their own apps and services directly using REST APIs.\n\n

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TAO API
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\n\nTo get started, use the provided one-click deploy script to deploy either on bare-metal setup or on managed Kubernetes service like Amazon EKS. Jupyter notebooks to train using the APIs directly or using the client app is provided under `notebooks/api_starter_kit`
\n`setup/quickstart_api_bare_metal`
\n`setup/quickstart_api_aws_eks`
\n\nMore information about setting up the API services and the API is provided in [TAO documentation - API](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_api/api_overview.html)\n\n#### 4. Python Wheel \n\nUsers can also run TAO directly on bare-metal without docker or K8s. Users can deploy TAO notebooks directly on Google Colab without having to configure infrastructure. The full instructions are provided in the Colab notebook below.\n\n| **CV Task** | **Model Arch** | **One-click Deploy** |\n| :--- | :--- | :-- |\n| Classification | ResNet18 | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/classification/classification.ipynb) |\n| Multi-task Classification | ResNet18 | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/multitask_classification/multitask_classification.ipynb) |\n| Object Detection | DSSD | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/dssd/dssd.ipynb) |\n| Object Detection | EfficientDet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/efficientdet/efficientdet.ipynb) |\n| Object Detection | RetinaNet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/retinanet/retinanet.ipynb) |\n| Object Detection | SSD | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/ssd/ssd.ipynb) |\n| Object Detection | YOLOv3 | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/yolo_v3/yolo_v3.ipynb) |\n| Object Detection | YOLOv4 | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/yolo_v4/yolo_v4.ipynb) |\n| Object Detection | YoloV4 Tiny | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/yolo_v4_tiny/yolo_v4_tiny.ipynb) |\n| Action Recognition | ActionRecognition | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/pytorch/cv_notebooks/action_recognition_net/actionrecognitionnet.ipynb) |\n| OCR | LPRNet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/lprnet/lprnet.ipynb) |\n| Pose Action Classification | PoseClassificationNet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/pytorch/cv_notebooks/pose_classification_net/poseclassificationnet.ipynb) |\n| Emotion Recognition | EmotionNet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/emotionnet/emotionnet.ipynb) |\n| Gesture Recognition | GestureNet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/gesturenet/gesturenet.ipynb) |\n| Heart Rate Estimation | HeartRateNet | [Train on Colab](https://colab.research.google.com/github/NVIDIA-AI-IOT/nvidia-tao/blob/main/tensorflow/heartratenet/heartratenet.ipynb) |\n\nAfter starting TAO service locally or remotely, start Jupyter notebook\n\n jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n\nOpen an internet browser on localhost and navigate to the following URL: \n\nOpen the notebook that you are interested in training and start training.\n\n> Note: All the instructions to train, prune, optimize and download pretrained models are provided in the notebook.\n\n## Jupyter notebooks\n\nAll Notebooks and required spec files are provided in this package. The table below maps which notebook to use for fine-tuning either a purpose-build models like PeopleNet or an open model architecture like YOLO.\n\n|**Purpose-built Model** | **Launcher CLI notebook** |\n|:--|:--|\n| PCB Classification | notebooks/tao_launcher_starter_kit/classification_pyt/classification.ipynb |\n| Retail Object Recognition | notebooks/tao_launcher_starter_kit/metric_learning_recognition/metric_learning_recognition.ipynb |\n| Optical Inspection | notebook/tao_launcher_starter_kit/optical_inspection/OpticalInspection.ipynb |\n| Mask Auto Label | notebooks/tao_launcher_starter_kit/mal/mal.ipynb |\n| OCRNet | notebooks/tao_launcher_starter_kit/ocrnet/ocrnet.ipynb |\n| OCDNet | notebooks/tao_launcher_starter_kit/ocdnet/ocdnet.ipynb |\n| PeopleSemSegFormer | notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb |\n| PeopleNet | notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb |\n| TrafficCamNet | notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb |\n| DashCamNet | notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb |\n| FaceDetectIR| notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb |\n| VehicleMakeNet | notebooks/tao_launcher_starter_kit/classification/classification.ipynb |\n| VehicleTypeNet | notebooks/tao_launcher_starter_kit/classification/classification.ipynb |\n| PeopleSegNet | notebooks/tao_launcher_starter_kit/mask_rcnn/mask_rcnn.ipynb |\n| PeopleSemSegNet | notebooks/tao_launcher_starter_kit/unet/unet_isbi.ipynb |\n| Bodypose Estimation | notebooks/tao_launcher_starter_kit/bpnet/bpnet.ipynb |\n| License Plate Detection | notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb |\n| License Plate Recognition | notebooks/tao_launcher_starter_kit/lprnet/lprnet.ipynb |\n| Facial Landmark | notebooks/tao_launcher_starter_kit/fpenet/fpenet.ipynb |\n| FaceDetect | notebooks/tao_launcher_starter_kit/facenet/facenet.ipynb |\n| ActionRecognitionNet | notebooks/tao_launcher_starter_kit/action_recognition_net/actionrecognitionnet.ipynb |\n| PoseClassificationNet | notebooks/tao_launcher_starter_kit/pose_classification_net/pose_classificationnet.ipynb |\n| ReIdentificationNet | notebooks/tao_launcher_starter_kit/re_identification_net/reidentificationnet.ipynb |\n| Visual ChangeNet Classification | notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_classification.ipynb |\n| Visual ChangeNet Segmentation - Research Only (LevirCD) | notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation.ipynb |\n| Visual ChangeNet Segmentation - Commercial (LandSATSCD) | notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation.ipynb |\n
\n\n|**Open model architecture**| **Jupyter notebook** |\n|:--|:--|\n| Deformable DETR | notebooks/tao_launcher_starter_kit/deformable_detr/deformable_detr.ipynb |\n| DINO | notebooks/tao_launcher_starter_kit/dino/dino.ipynb |\n| Image Classification | notebooks/tao_launcher_starter_kit/classification_pyt/classification_pyt.ipynb |\n| Image Classification | notebooks/tao_launcher_starter_kit/classification_tf2/classification.ipynb |\n| Image Classification | notebooks/tao_launcher_starter_kit/classification_tf1/classification.ipynb |\n| Optical Inspection | notebooks/tao_launcher_starter_kit/optical_inspection/optical_inspection.ipynb |\n| Metric Learning Recognition | notebooks/tao_launcher_starter_kit/metric_learning_recognition/metric_learning_recognition.ipynb |\n| Segformer | notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb |\n| DetectNet_v2 | notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb |\n| FasterRCNN | notebooks/tao_launcher_starter_kit/faster_rcnn/faster_rcnn.ipynb |\n| YOLOV3 | notebooks/tao_launcher_starter_kit/yolo_v3/yolo_v3.ipynb |\n| YOLOV4 | notebooks/tao_launcher_starter_kit/yolo_v4/yolo_v4.ipynb |\n| YOLOv4-Tiny | notebooks/tao_launcher_starter_kit/yolo_v4_tiny/yolo_v4_tiny.ipynb |\n| SSD | notebooks/tao_launcher_starter_kit/ssd/ssd.ipynb |\n| DSSD | notebooks/tao_launcher_starter_kit/dssd/dssd.ipynb |\n| RetinaNet | notebooks/tao_launcher_starter_kit/retinanet/retinanet.ipynb |\n| MaskRCNN | notebooks/tao_launcher_starter_kit/mask_rcnn/mask_rcnn.ipynb |\n| UNET | notebooks/tao_launcher_starter_kit/unet/unet_isbi.ipynb |\n| EfficientDet | notebooks/tao_launcher_starter_kit/efficientdet/efficientdet.ipynb |\n| Mask Auto Label | notebooks/tao_launcher_starter_kit/mal/mal.ipynb |\n| Visual ChangeNet Classification | notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_classification.ipynb |\n| Visual ChangeNet Segmentation | notebooks/tao_launcher_starter_kit/visual_changenet/visual_changenet_segmentation.ipynb |\n
\n\n\n## Important Links\n\n* [TAO Toolkit Documentation](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html)\n\n## Blogs\n\n[Train like a 'pro' with AutoML in TAO](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)
\n[Deploy TAO on Azure ML](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)
\n[Synthetic Data and TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)
\n[Action Recognition Blog](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)
\n[Real-time License Plate Detection](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app/)
\n[2 Pose Estimation: Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1/)
\n[Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2/)
\n[Building ConvAI with TAO Toolkit](https://developer.nvidia.com/blog/building-and-deploying-conversational-ai-models-using-nvidia-tao-toolkit/)
\n\n## License \n\n[TAO Toolkit getting Started](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html)\nLicense for TAO containers is included in the banner of the container. License for the pre-trained models are available with the model cards on NGC. By pulling and using the Train Adapt Optimize (TAO) Toolkit container to download models, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).","displayName","TAO Toolkit Getting Started",["D",1724712941355],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/tao-getting-started","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_190":1298,"_192":1299},"ngcApps",[1251],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1232":1233,"_1234":1235,"_1236":1237,"_1238":1239,"_1240":332,"_111":356,"_1241":1242,"_1243":60,"_1244":92,"_1245":92,"_1246":1247,"_1248":92,"_1249":1250},"amplitude",{"_1229":1230,"_868":-7,"_1231":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1223":1224,"_1225":1226,"_1227":1228},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1108":256,"_1109":1110,"_1111":1112,"_1113":1114},"announcements",[],"consentState",{"_394":1098,"_1099":92,"_1100":35,"_1101":35,"_1102":1103},"consentConfig",{"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395},"showContactAdmin",1785218909606,[],[],[370,371,372,373,374,375],{"_49":64,"_51":75,"_53":185,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":185,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":69,"_53":185,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":185,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":185,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":185,"_55":-5,"_57":-5,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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	Files	Compressed Size
5.3.0	531	18.54 MB	March 16, 2024 UTC
5.2.0	481	3.36 MB	December 7, 2023 UTC
5.1.0	492	13.72 MB	October 19, 2023 UTC
5.0.0	485	13.6 MB	July 25, 2023 UTC
4.0.2Selected	378	10.71 MB	April 14, 2023 UTC
4.0.1	378	10.7 MB	March 7, 2023 UTC
4.0.0	375	10.7 MB	December 14, 2022 UTC