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📚 OpenVINO™ Notebooks

A collection of ready-to-run Jupyter notebooks for learning and experimenting with the OpenVINO™ Toolkit. The notebooks provide an introduction to OpenVINO basics and teach developers how to leverage our API for optimized deep learning inference.

NOTE: The main branch of this repository was updated to support the new OpenVINO 2022.1 release. To upgrade to the new release version, please run pip install --upgrade -r requirements.txt in your openvino_env virtual environment. If you need to install for the first time, see the Installation Guide section below. If you wish to use the previous Long Term Support (LTS) version of OpenVINO check out the 2021.4 branch.

If you need help, please start a GitHub Discussion.

Table of Contents

• ➤ 📝 Installation Guide
  • Windows
  • Ubuntu
  • macOS
  • Red Hat
  • CentOS
  • Azure ML
  • Docker
• ➤ 🚀 Getting Started
  • First steps with OpenVINO
  • Convert & Optimize
  • Model Demos
  • Model Training
  • Live Demos
• ➤ ⚙️ System Requirements
• ➤ 💻 Run the Notebooks
• ➤ 🧹 Cleaning Up
• ➤ ⚠️ Troubleshooting
• ➤ 🧑‍💻 Contributors
• ➤ ❓ FAQ

📝 Installation Guide

OpenVINO Notebooks require Python and Git. To get started, select the guide for your operating system or environment:

🚀 Getting Started

The Jupyter notebooks are categorized into four classes, select one related to your needs or give them all a try. Good Luck!

💻 First steps

Brief tutorials that demonstrate how to use OpenVINO's Python API for inference.

001-hello-world	002-openvino-api	003-hello-segmentation	004-hello-detection
Classify an image with OpenVINO	Learn the OpenVINO Python API	Semantic segmentation with OpenVINO	Text detection with OpenVINO

⌚ Convert & Optimize

Tutorials that explain how to optimize and quantize models with OpenVINO tools.

101-tensorflow-to-openvino	102-pytorch-onnx-to-openvino	103-paddle-onnx-to-openvino	104-model-tools
Convert TensorFlow models to OpenVINO IR	Convert PyTorch models to OpenVINO IR	Convert PaddlePaddle models to OpenVINO IR	Download, convert and benchmark models from Open Model Zoo

More amazing notebooks here!

Click here to show complete list!

Notebook	Description
101-tensorflow-to-openvino	Convert TensorFlow models to OpenVINO IR
102-pytorch-onnx-to-openvino	Convert PyTorch models to OpenVINO IR
103-paddle-onnx-to-openvino	Convert PaddlePaddle models to OpenVINO IR
104-model-tools	Download, convert and benchmark models from Open Model Zoo
105-language-quantize-bert	Optimize and quantize a pre-trained BERT model
106-auto-device	Demonstrate how to use AUTO Device
107-speech-recognition-quantization	Optimize and quantize a pre-trained Wav2Vec2 speech model
110-ct-segmentation-quantize	Quantize a kidney segmentation model and show live inference
111-detection-quantization	Quantize an object detection model
112-pytorch-post-training-quantization-nncf	Use Neural Network Compression Framework (NNCF) to quantize PyTorch model in post-training mode (without model fine-tuning)
113-image-classification-quantization	Quantize mobilenet image classification
114-quantization-simplified-mode	Quantize Image Classification Models with POT in Simplified Mode
115-async-api	Use Asynchronous Execution to Improve Data Pipelining

🎯 Model Demos

Demos that demonstrate inference on a particular model.

210-ct-scan-live-inference	211-speech-to-text	213-question-answering	208-optical-character-recognition	209-handwritten-ocr
Show live inference on segmentation of CT-scan data	Run inference on speech-to-text recognition model	Answer your questions basing on a context	Annotate text on images using text recognition resnet	OCR for handwritten simplified Chinese and Japanese
				的人不一了是他有为在责新中任自之我们

More amazing notebooks here!

Click here to show complete list!

Notebook	Description	Preview
201-vision-monodepth	Monocular depth estimation with images and video
202-vision-superresolution-image	Upscale raw images with a super resolution model	→
202-vision-superresolution-video	Turn 360p into 1080p video using a super resolution model	→
203-meter-reader	PaddlePaddle pre-trained models to read industrial meter's value
204-named-entity-recognition	Perform named entity recognition on simple text
205-vision-background-removal	Remove and replace the background in an image using salient object detection
206-vision-paddlegan-anime	Turn an image into anime using a GAN	→
207-vision-paddlegan-superresolution	Upscale small images with superresolution using a PaddleGAN model
208-optical-character-recognition	Annotate text on images using text recognition resnet
209-handwritten-ocr	OCR for handwritten simplified Chinese and Japanese	的人不一了是他有为在责新中任自之我们
210-ct-scan-live-inference	Show live inference on segmentation of CT-scan data
211-speech-to-text	Run inference on speech-to-text recognition model
212-onnx-style-transfer	Transform images to five different styles with neural style transfer	→
213-question-answering	Answer your questions basing on a context
214-vision-paddle-classification	PaddlePaddle Image Classification with OpenVINO
215-image-inpainting	Fill missing pixels with image in-painting
216-license-plate-recognition	Recognize Chinese license plates in traffic
217-vision-deblur	Deblur Images with DeblurGAN-v2
218-vehicle-detection-and-recognition	Use pre-trained models to detect and recognize vehicles and their attributes with OpenVINO
219-knowledge-graphs-conve	Optimize the knowledge graph embeddings model (ConvE) with OpenVINO
220-yolov5-accuracy-check-and-quantization	Quantize the Ultralytics YOLOv5 model and check accuracy using the OpenVINO POT API
221-machine-translation	Real-time translation from English to German
222-vision-image-colorization	Use pre-trained models to colorize black & white images using OpenVINO

🏃 Model Training

Tutorials that include code to train neural networks.

Notebook	Description	Preview
301-tensorflow-training-openvino	Train a flower classification model from TensorFlow, then convert to OpenVINO IR
301-tensorflow-training-openvino-pot	Use Post-training Optimization Tool (POT) to quantize the flowers model
302-pytorch-quantization-aware-training	Use Neural Network Compression Framework (NNCF) to quantize PyTorch model
305-tensorflow-quantization-aware-training	Use Neural Network Compression Framework (NNCF) to quantize TensorFlow model

📺 Live Demos

Live inference demos that run on a webcam or video files.

401-object-detection-webcam	402-pose-estimation-webcam	403-action-recognition-webcam	405-paddle-ocr-webcam
Object detection with a webcam or video file	Human pose estimation with a webcam or video file	Human action recognition with a webcam or video file	OCR with a webcam or video file

If you run into issues, please check the troubleshooting section, FAQs or start a GitHub discussion.

Notebooks with a button can be run without installing anything. Binder is a free online service with limited resources. For the best performance, please follow the Installation Guide and run the notebooks locally.

You will have a lot of fun with this section:

Vision-monodepth	CT-scan-live-inference	Object-detection-webcam	Pose-estimation-webcam	Action-recognition-webcam

⚙️ System Requirements

The notebooks run almost anywhere — your laptop, a cloud VM, or even a Docker container. The table below lists the supported operating systems and Python versions. Note: Python 3.10 is not supported yet.

💻 Run the Notebooks

To Launch a Single Notebook

If you wish to launch only one notebook, like the Monodepth notebook, run the command below.

jupyter 201-vision-monodepth.ipynb

To Launch all Notebooks

jupyter lab notebooks

In your browser, select a notebook from the file browser in Jupyter Lab using the left sidebar. Each tutorial is located in a subdirectory within the notebooks directory.

🧹 Cleaning Up

rm -rf openvino_env

rmdir /s openvino_env

jupyter kernelspec remove openvino_env

⚠️ Troubleshooting

If these tips do not solve your problem, please open a discussion topic or create an issue!

• To check some common installation problems, run python check_install.py. This script is located in the openvino_notebooks directory. Please run it after activating the openvino_env virtual environment.
• If you get an ImportError, doublecheck that you installed the Jupyter kernel. If necessary, choose the openvino_env kernel from the Kernel->Change Kernel menu) in Jupyter Lab or Jupyter Notebook
• If OpenVINO is installed globally, do not run installation commands in a terminal where setupvars.bat or setupvars.sh are sourced.
• For Windows installation, we recommend using Command Prompt (cmd.exe), not PowerShell.

🧑‍💻 Contributors

Made with contributors-img.

❓ FAQ

• Which devices does OpenVINO support?
• What is the first CPU generation you support with OpenVINO?
• Are there any success stories about deploying real-world solutions with OpenVINO?

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