TAO#
Introduction
Migration Guides
- Migrating to TAO 7.0
- From the TAO CLI to Agent Prompts
- 1. What Changed
- 2. Quick Start
- 3. Required Environment Variables
- 4. The New Mental Model
- 5. How to Read Each Entry
- 6. Authentication, Workspaces, and Datasets
- 7. Inspecting Models, Schemas, and AutoML Defaults
- 8. Training and the Experiment Action Chain
- 9. AutoML
- 10. Dataset Preparation Jobs
- 11. Monitoring Runs and Downloading Artifacts
- 12. Inference Serving (Microservices)
- 13. Publishing Models
- 14. End-to-End Example
- 15. CLI Commands with No Equivalent
- 16. Gotchas During Migration
- 17. Reference
- 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
Model Zoo
Dataset
- Data Annotation Format
- Image Classification Format
- Optical Inspection Format
- Multiple Golden VisualChangeNet Classification Format
- Change Detection (Segmentation) Format
- CenterPose Format
- Image Classification Format PyTorch
- Object Detection – KITTI Format
- Object Detection – COCO Format
- Open Vocabulary Object Detection/Segmentation – ODVG Format
- Instance Segmentation – COCO format
- Semantic Segmentation - PNG Mask Format
- Gesture Recognition – Custom Format
- Heart Rate Estimation – Custom Format
- EmotionNet, FPENET, GazeNet – JSON Label Data Format
- BodyposeNet – COCO Format
- Re-Identification – Market-1501 Format
- Sparse4D Detection & Tracking – OVPKL Format
- Annotations
- Offline Data Augmentation
- Auto-Label
- Data Analytics
- Data Input for Data Analytics
- Creating Experiment Specification File for the Analyze Task
- Creating an Experiment Specification File for the Validate Task
- Running the Analyze Task on the Data
- Running the Validate Task on the Data
- Creating an Experiment Specification File for the KPI Analyze Task
- Running the KPI Analyze Task on the Data
Embedding Model Fine-tuning
VLM Model Fine-tuning
Computer Vision
Model Optimization
AutoML
Training Pipeline Features
Deploying to Inference SDKs
- TAO Deploy Overview
- 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
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
- nitime
- OpenSSL
- JsonCpp
- Python
- libcurl
- OpenCV
- zlib
- TensorFlow
- Keras
- PyTorch
- ssd_keras
- Yamale
- PyCUDA
- protobuf
- onnx
- PIL
- PyYAML
- addict
- argcomplete
- bto3
- cryptography
- docker
- dockerpty
- gRPC
- h5py
- jupyter
- numba
- numpy
- pandas
- posix_ipc
- prettytable
- arrow
- PyJWT
- requests
- retrying
- seaborn
- scikit-image
- scikit-learn
- semver
- Shapely
- simplejson
- six
- python-tabulate
- toposort
- tqdm
- uplink
- xmltodict
- recordclass
- cocoapi
- mpi4py
- Open MPI
- lazy_object_proxy
- onnxruntime
- pytorch-lightning
- KenLM
- Eigen
- google/automl
- open-mmlab/OpenPCDet
- VainF/Torch-Pruning
- gmalivenko/onnx2keras
- open-mmlab/mmskeleton
- NVIDIA Synthetic Dataset License Agreement