The webinar "Using NVIDIA pre-trained models and Transfer Learning Toolkit 3.0 to create gesture-based interactions with a robot" is now available on demand, with sample codes available in github repo. Watch now.

TLT 3.0

    Highlights:
  • New purpose-built pretrained models for computer vision:
  • Emotion recognition
  • Facial landmark
  • License plate detection and recognition
  • Heart rate estimation
  • Gesture recognition
  • Gaze estimation
  • People segmentation
  • Introducing ASR and NLP models with inference samples for:
  • Named Entity Recognition (NER)
  • Question/Answering
  • Punctuation
  • Text classification
  • TLT 3.0 brings support for NVIDIA Ampere GPUs with third generation tensor core additions and various performance optimizations
  • Improved PeopleNet model to detect difficult scenarios such as people sitting down, rotated/ warped objects
  • Quickly kickstart training models with the hassle free TLT launcher tool for pulling compatible containers to initialize
  • Train with popular networks: EfficientNet, ResNet, YOLOV3/V4, FasterRCNN, SSD, DetectNet_v2, MaskRCNN and UNET
  • Out of the box compatibility with DeepStream SDK for vision AI deployment
  • Out of the box compatibility with Jarvis for conversational AI deployment
  • Enable faster training with jobs split up across multi-GPUs

Share TLT and AI models update with your network:
Developer news article for computer vision
Developer news article for conversational AI

Operating System
  • Ubuntu 18.04
Dependencies
  • Driver version >= 455
  • Docker-ce > 19.03
  • Nvidia-docker2
  • Docker-API 1.40

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Learn how to train State-Of-The-Art Models for classification and object detection.

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Developer Tutorial

Learn how to create a real-time number plate detection and recognition app.

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Developer Webinar

Learn how to create a gesture recognition application with robot interactions.

Watch Now ACCESS TO GITHUB REPO

Getting Started Resources


Install TLT launcher Python package

Conversational AI

Vision AI

Platform Compute Download
x86 + GPU CUDA 10.2 / cuDNN 8.0 / TensorRT 7.1 Download
x86 + GPU CUDA 10.2 / cuDNN 8.0 / TensorRT 7.2 Download
x86 + GPU CUDA 11.0 / cuDNN 8.0 / TensorRT 7.1 Download
x86 + GPU CUDA 11.0 / cuDNN 8.0 / TensorRT 7.2 Download
x86 + GPU CUDA 11.1 / cuDNN 8.0 / TensorRT 7.2 Download
Jetson Jetpack 4.4 Download
Jetson Jetpack 4.5 Download

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Ethical AI

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.