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OpenVINO 2022.1 introduces a new version of OpenVINO API (API 2.0). For more information on the changes and transition steps, see the transition guide

Notebooks

  • OpenVINO notebooks documentation
    • Hello Object Detection
    • Hello Image Classification
    • OpenVINO API Tutorial
    • Hello Image Segmentation
    • Post-Training Quantization of PyTorch models with NNCF
    • Convert a PyTorch Model to ONNX and OpenVINO IR
    • Convert a TensorFlow Model to OpenVINO
    • Quantize NLP models with OpenVINO Post-Training Optimization Tool ​
    • Convert a PaddlePaddle Model to ONNX and OpenVINO IR
    • Quantize a Segmentation Model and Show Live Inference
    • Quantization of Image Classification Models
    • Working with Open Model Zoo Models
    • Object Detection Quantization
    • Speech to Text with OpenVINO
    • Optical Character Recognition (OCR) with OpenVINO
    • Single Image Super Resolution with OpenVINO
    • Super Resolution with PaddleGAN and OpenVINO
    • Photos to Anime with PaddleGAN and OpenVINO
    • Video Super Resolution with OpenVINO
    • Image Background Removal with U^2-Net and OpenVINO
    • Live Inference and Benchmark CT-scan Data with OpenVINO
    • Monodepth Estimation with OpenVINO
    • Style Transfer on ONNX Models with OpenVINO
    • Handwritten Chinese and Japanese OCR
    • Quantization Aware Training with NNCF, using PyTorch framework
    • Post-Training Quantization with TensorFlow Classification Model
    • From Training to Deployment with TensorFlow and OpenVINO
    • Quantization Aware Training with NNCF, using TensorFlow Framework
    • Live Object Detection with OpenVINO
    • Human Action Recognition with OpenVINO
    • Live Human Pose Estimation with OpenVINO
On this page
  • Getting Started
  • Convert & Optimize
  • Model Demos
  • Model Training
  • Live Demos
.pdf .zip

OpenVINO notebooks documentation¶

Getting Started¶

  • Hello Object Detection
  • Hello Image Classification
  • OpenVINO API Tutorial
  • Hello Image Segmentation

Convert & Optimize¶

  • Post-Training Quantization of PyTorch models with NNCF
  • Convert a PyTorch Model to ONNX and OpenVINO IR
  • Convert a TensorFlow Model to OpenVINO
  • Quantize NLP models with OpenVINO Post-Training Optimization Tool ​
  • Convert a PaddlePaddle Model to ONNX and OpenVINO IR
  • Quantize a Segmentation Model and Show Live Inference
  • Quantization of Image Classification Models
  • Working with Open Model Zoo Models
  • Object Detection Quantization

Model Demos¶

  • Speech to Text with OpenVINO
  • Optical Character Recognition (OCR) with OpenVINO
  • Single Image Super Resolution with OpenVINO
  • Super Resolution with PaddleGAN and OpenVINO
  • Photos to Anime with PaddleGAN and OpenVINO
  • Video Super Resolution with OpenVINO
  • Image Background Removal with U^2-Net and OpenVINO
  • Live Inference and Benchmark CT-scan Data with OpenVINO
  • Monodepth Estimation with OpenVINO
  • Style Transfer on ONNX Models with OpenVINO
  • Handwritten Chinese and Japanese OCR

Model Training¶

  • Quantization Aware Training with NNCF, using PyTorch framework
  • Post-Training Quantization with TensorFlow Classification Model
  • From Training to Deployment with TensorFlow and OpenVINO
  • Quantization Aware Training with NNCF, using TensorFlow Framework

Live Demos¶

  • Live Object Detection with OpenVINO
  • Human Action Recognition with OpenVINO
  • Live Human Pose Estimation with OpenVINO
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