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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 2022.3 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

• Table of Contents
• 📝 Installation Guide
• 🚀 Getting Started
  • 💻 First steps
  • ⌚ Convert & Optimize
  • 🎯 Model Demos
  • 🏃 Model Training
  • 📺 Live Demos
• ⚙️ System Requirements
• 💻 Run the Notebooks
  • To Launch a Single Notebook
  • To Launch all 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:

Windows	Ubuntu	macOS	Red Hat	CentOS	Azure ML	Docker	Amazon SageMaker

🚀 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.

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-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 speech recognition models
108-gpu-device	Working with GPUs in OpenVINO™
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
116-sparsity-optimization	Improve performance of sparse Transformer models
117-model-server	Introduction to model serving with OpenVINO™ Model Server (OVMS)
118-optimize-preprocessing	Improve performance of image preprocessing step

🎯 Model Demos

Demos that demonstrate inference on a particular model.

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
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
211-speech-to-text	Run inference on speech-to-text recognition model
212-pyannote-speaker-diarization	Run inference on speaker diarization pipeline
213-question-answering	Answer your questions basing on a context
214-grammar-correction	Grammatical Error Correction with OpenVINO	input text: I'm working in campany for last 2 yeas Generated text: I'm working in a company for the last 2 years.
215-image-inpainting	Fill missing pixels with image in-painting
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
223-gpt2-text-prediction	Use GPT-2 to perform text prediction on an input sequence
224-3D-segmentation-point-clouds	Process point cloud data and run 3D Part Segmentation with OpenVINO
225-stable-diffusion-text-to-image	Text-to-image generation with Stable Diffusion method
226-yolov7-optimization	Optimize YOLOv7 using NNCF PTQ API
227-whisper-subtitles-generation	Generate subtitles for video with OpenAI Whisper and OpenVINO
228-clip-zero-shot-image-classification	Perform Zero-shot Image Classification with CLIP and OpenVINO
229-distilbert-sequence-classification	Sequence Classification with OpenVINO
230-yolov8-optimization	Optimize YOLOv8 using NNCF PTQ API
231-instruct-pix2pix-image-editing	Image editing with InstructPix2Pix
232-clip-language-saliency-map	Language-Visual Saliency with CLIP and OpenVINO™
233-blip-visual-language-processing	Visual Question Answering and Image Captioning using BLIP and OpenVINO™
234-encodec-audio-compression	Audio compression with EnCodec and OpenVINO™
235-controlnet-stable-diffusion	A Text-to-Image Generation with ControlNet Conditioning and OpenVINO™
236-stable-diffusion-v2	Text-to-Image Generation and Infinite Zoom with Stable Diffusion v2 and 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.

Notebook	Description	Preview
401-object-detection-webcam	Object detection with a webcam or video file
402-pose-estimation-webcam	Human pose estimation with a webcam or video file
403-action-recognition-webcam	Human action recognition with a webcam or video file
404-style-transfer-webcam	Style Transfer with a webcam or video file
405-paddle-ocr-webcam	OCR with a webcam or video file
406-3D-pose-estimation-webcam	3D display of human pose estimation with a webcam or video file
407-person-tracking-webcam	Person tracking 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.

⚙️ 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.

Supported Operating System	Python Version (64-bit)
Ubuntu 20.04 LTS, 64-bit	3.7, 3.8, 3.9, 3.10
Ubuntu 22.04 LTS, 64-bit	3.7, 3.8, 3.9, 3.10
Red Hat Enterprise Linux 8, 64-bit	3.8, 3.9, 3.10
CentOS 7, 64-bit	3.7, 3.8, 3.9, 3.10
macOS 10.15.x versions or higher	3.7, 3.8, 3.9, 3.10
Windows 10, 64-bit Pro, Enterprise or Education editions	3.7, 3.8, 3.9, 3.10
Windows Server 2016 or higher	3.7, 3.8, 3.9, 3.10

💻 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

• Shut Down Jupyter Kernel

To end your Jupyter session, press Ctrl-c. This will prompt you to Shutdown this Jupyter server (y/[n])? enter y and hit Enter.

• Deactivate Virtual Environment

To deactivate your virtualenv, simply run deactivate from the terminal window where you activated openvino_env. This will deactivate your environment.

To reactivate your environment, run source openvino_env/bin/activate on Linux or openvino_env\Scripts\activate on Windows, then type jupyter lab or jupyter notebook to launch the notebooks again.

• Delete Virtual Environment _(Optional)_

To remove your virtual environment, simply delete the openvino_env directory:

• On Linux and macOS:

rm -rf openvino_env

• On Windows:

rmdir /s openvino_env

• Remove openvino_env Kernel from Jupyter

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, it is recommended to use 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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