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Tao Toolkit

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Tao Toolkit

Table of Contents

Introduction

  • Overview

Migration Guides

  • Migration Guides
    • Migrating to TAO 7.0
    • Migrating to TAO 6.0
    • Migrating to TAO 5.5
    • Migrating to the TAO API from TAO 5.3.0
    • Migrating from TAO 4.0.x to TAO 5.0.0
    • Migrating from TAO 3.x to TAO 4.0
    • Migrating from Legacy TLT to TAO

Getting Started

  • Getting Started with NVIDIA TAO Toolkit

Model Zoo

  • Overview
  • Foundation Models

Dataset

  • Data Annotation Format
  • Data Services
    • Annotations
    • Offline Data Augmentation
    • Auto-Label
    • Data Analytics

Embedding Model Fine-tuning

  • Embedding Model Fine-Tuning
    • CLIP
      • CLIP Introduction
      • CLIP Training, Evaluation, Inference, and Export
      • Using CLIP Embeddings
    • Cosmos-Embed1

VLM Model Fine-tuning

  • Vision-Language Model (VLM) Fine-Tuning
    • Cosmos-Reason

CV Model Fine-tuning

  • Optimizing the Training Pipeline and Models
  • Visualizing Training
  • Computer Vision Finetuning
    • PyTorch
      • Self-Supervised Learning
        • Self-Supervised Learning
        • Nv-DINOv2
        • Masked Autoencoders (MAE)
      • Metric Learning Recognition
        • Metric Learning Recognition
      • Instance Segmentation
        • Data Input for Instance Segmentation
        • Mask2former
        • Mask Auto Labeler
        • Mask Grounding DINO
        • OneFormer
      • CenterPose
        • CenterPose
      • Character Recognition
        • OCRNet
      • VisualChangeNet
        • Visual ChangeNet-Segmentation
        • Visual ChangeNet-Classification
      • 3D Object Detection
        • PointPillars
      • ReIdentificationNet Transformer
        • ReIdentificationNet Transformer
      • Optical Inspection
        • SiameseOI
      • Pose Classification
        • PoseClassificationNet
      • Object Detection
        • Grounding DINO
        • DINO
        • OCDNet
        • Deformable DETR
        • RT-DETR
      • ReIdentificationNet
        • ReIdentificationNet
      • ActionRecognitionNet
      • BEVFusion
      • Image Classification PyT
      • SegFormer
      • Depth Estimation with NVIDIA TAO Toolkit
        • Monocular Depth Estimation
        • Stereo Depth Estimation
        • Fast Foundation Stereo
      • Sparse4D
        • Sparse4D
      • Panoptic 3D Reconstruction with NVIDIA TAO Toolkit
        • NvPanoptix3D
      • Bring Your Own Model (BYOM)[DEPRECATED]
    • TensorFlow 2.x [Deprecated]
      • Image Classification (TF2)
      • EfficientDet (TF2)
    • TensorFlow 1.x [Deprecated]
      • Body Pose Estimation
        • Body Pose Estimation
      • Character Recognition
        • LPRNet
      • Emotion Classification
      • Facial Landmarks Estimation
        • Facial Landmarks Estimation
      • Gaze Estimation
      • Gesture Recognition
      • HeartRate Estimation
      • Instance Segmentation
        • Data Input for Instance Segmentation
        • MaskRCNN
      • Image Classification (TF1)
      • Multitask Image Classification
      • Object Detection
        • DetectNet_v2
        • FasterRCNN
        • YOLOv3
        • YOLOv4
        • YOLOv4-tiny
        • SSD
        • DSSD
        • RetinaNet
        • EfficientDet (TF1)
      • Semantic Segmentation
        • UNET
  • AutoML
  • Quantizing a model in TAO (TAO Quant)
    • 1. Terminology
    • 2. Getting Started
    • 3. Choosing a Backend
    • 4. Configuration
    • 6. Skipping Layers From Quantization
    • 7. TorchAO Backend (Weight-Only PTQ)
    • 8. ModelOpt PyTorch Backend (Static PTQ)
    • 9. ModelOpt ONNX Backend (Static PTQ)
    • 10. API Reference
    • 11. Extending TAO Quant With a Custom Backend
    • 12. Limitations and Current Status

MLOPS integration

  • Overview
  • TAO WandB Integration
  • TAO Clearml Integration

Deploying to Inference SDKs

  • TAO Deploy Overview
    • CenterPose with TAO Deploy
    • CLIP with TAO Deploy
    • Classification (PyTorch) with TAO Deploy
    • Classification (TF1) with TAO Deploy
    • Classification (TF2) with TAO Deploy
    • Deformable DETR with TAO Deploy
    • DINO with TAO Deploy
    • Grounding DINO with TAO Deploy
    • DetectNet_v2 with TAO Deploy
    • DSSD with TAO Deploy
    • EfficientDet (TF1) with TAO Deploy
    • EfficientDet (TF2) with TAO Deploy
    • Faster RCNN with TAO Deploy
    • LPRNet with TAO Deploy
    • MAE with TAO Deploy
    • Mask RCNN with TAO Deploy
    • Mask2former with TAO Deploy
    • MLRecogNet with TAO Deploy
    • Monocular Depth with TAO Deploy
    • Multitask Image Classification with TAO Deploy
    • OCDNet with TAO Deploy
    • RetinaNet with TAO Deploy
    • RT-DETR with TAO Deploy
    • SSD with TAO Deploy
    • Segformer with TAO Deploy
    • UNet with TAO Deploy
    • YOLOv3 with TAO Deploy
    • YOLOv4 with TAO Deploy
    • YOLOv4-tiny with TAO Deploy
    • OCRNet with TAO Deploy
    • SiameseOI with TAO Deploy
    • Stereo Depth with TAO Deploy
    • VisualChangeNet-Classification with TAO Deploy
    • VisualChangeNet-Segmentation with TAO Deploy
    • Mask Grounding DINO with TAO Deploy
    • PointPillars with TAO Deploy
    • Integrating TAO Models with NSight DL Designer
  • Integrating TAO CV Models with Triton Inference Server
  • Optimizing and Profiling with TensorRT
    • TRTEXEC with ActionRecognitionNet
    • TRTEXEC with BodyPoseNet
    • TRTEXEC with CenterPose
    • TRTEXEC with CLIP
    • TRTEXEC with Classification TF1/TF2/PyT
    • TRTEXEC with Deformable-DETR
    • TRTEXEC with DetectNet-v2
    • TRTEXEC with DINO
    • TRTEXEC with DSSD
    • TRTEXEC with EfficientDet TF1/TF2
    • TRTEXEC with Facial Landmarks Estimation
    • TRTEXEC with Faster RCNN
    • TRTEXEC with Grounding DINO
    • TRTEXEC with LPRNet
    • TRTEXEC with MAE
    • TRTEXEC with Metric Learning Recognition
    • TRTEXEC with Mask RCNN
    • TRTEXEC with Multitask Classification
    • TRTEXEC with OCDNet
    • TRTEXEC with OCRNet
    • TRTEXEC with PointPillars
    • TRTEXEC with PoseClassificationNet
    • TRTEXEC with ReIdentificationNet
    • TRTEXEC with ReIdentificationNet Transformer
    • TRTEXEC with RetinaNet
    • TRTEXEC with RT-DETR
    • TRTEXEC with Segformer
    • TRTEXEC with SiameseOI
    • TRTEXEC with SSD
    • TRTEXEC with UNet
    • TRTEXEC with YOLO_v3
    • TRTEXEC with YOLO_v4
    • TRTEXEC with YOLO_v4_tiny
    • TRTEXEC with VisualChangeNet
    • TRTEXEC with Mask2former
    • TRTEXEC with Mask Grounding DINO
  • Integrating TAO Models into DeepStream
    • Deploying to DeepStream for Classification TF1/TF2/PyTorch
    • Deploying to DeepStream for Multitask Classification
    • Deploying to DeepStream for DetectNet_v2
    • Deploying to DeepStream for Deformable DETR
    • Deploying to DeepStream for DINO
    • Deploying to DeepStream for DSSD
    • Deploying to DeepStream for EfficientDet
    • Deploying to DeepStream for FasterRCNN
    • Deploying to DeepStream for RetinaNet
    • Deploying to DeepStream for SSD
    • Deploying to DeepStream for YOLOv3
    • Deploying to DeepStream for YOLOv4
    • Deploying to DeepStream for YOLOv4-tiny
    • Deploying to DeepStream for MaskRCNN
    • Deploying to Deepstream for UNet
    • Deploying to Deepstream for Segformer
    • Deploying nvOCDR to DeepStream

More Information

  • Release Notes
  • Frequently Asked Questions
  • Troubleshooting Guide
  • Tutorial Videos
    • Getting started with NVIDIA TAO
    • Create Custom Multi-Modal Fusion Models
    • Use Visual Prompt for In-Context Segmentation with NVIDIA TAO
    • Estimate and Track Object Poses with the NVIDIA TAO FoundationPose Model
    • Open Vocabulary Object Detection with NVIDIA Grounding-DINO
    • Use Text Prompts for Auto-Labeling with NVIDIA TAO
    • Visualize Model Training with TensorBoard
  • Support Information
  • Acknowledgements
  • Computer Vision Finetuning
  • PyTorch
  • 3D Object Detection

3D Object Detection#

  • PointPillars
    • Preparing the Dataset
    • Creating an Experiment Specification File
    • Evaluating the model
    • Pruning and Retrain a PointPillars Model
    • Deploying the Model

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Last updated on Jun 23, 2026.