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📚 OpenVINO™ Notebooks

Apache License Version 2.0 CI CI

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.

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Table of Contents

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📝 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

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🚀 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
Binder
002-openvino-api
Binder
003-hello-segmentation
Binder
004-hello-detection
Binder
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
Binder
Convert TensorFlow models to OpenVINO IR
102-pytorch-onnx-to-openvino Convert PyTorch models to OpenVINO IR
103-paddle-to-openvino
Binder
Convert PaddlePaddle models to OpenVINO IR
104-model-tools
Binder
Download, convert and benchmark models from Open Model Zoo
105-language-quantize-bert Optimize and quantize a pre-trained BERT model
106-auto-device
Binder
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
Binder
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
Binder
Quantize mobilenet image classification
114-quantization-simplified-mode
Binder
Quantize Image Classification Models with POT in Simplified Mode
115-async-api
Binder
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
Binder
Monocular depth estimation with images and video
202-vision-superresolution-image
Binder
Upscale raw images with a super resolution model
202-vision-superresolution-video
Binder
Turn 360p into 1080p video using a super resolution model
203-meter-reader
Binder
PaddlePaddle pre-trained models to read industrial meter's value
205-vision-background-removal
Binder
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
Binder
OCR for handwritten simplified Chinese and Japanese handwritten_simplified_chinese_test
的人不一了是他有为在责新中任自之我们
211-speech-to-text
Binder
Run inference on speech-to-text recognition model
212-pyannote-speaker-diarization
Run inference on speaker diarization pipeline
213-question-answering
Binder
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
Binder
Fill missing pixels with image in-painting
216-license-plate-recognition
Binder
Recognize Chinese license plates in traffic
217-vision-deblur
Binder
Deblur Images with DeblurGAN-v2
218-vehicle-detection-and-recognition
Binder
Use pre-trained models to detect and recognize vehicles and their attributes with OpenVINO
219-knowledge-graphs-conve
Binder
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
Binder
Real-time translation from English to German
222-vision-image-colorization
Binder
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
Binder
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™

🏃 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
Binder
Object detection with a webcam or video file
402-pose-estimation-webcam
Binder
Human pose estimation with a webcam or video file
403-action-recognition-webcam
Binder
Human action recognition with a webcam or video file
404-style-transfer-webcam
Binder
Style Transfer with a webcam or video file
405-paddle-ocr-webcam
Binder
OCR with a webcam or video file
406-3D-pose-estimation-webcam
Binder
3D display of human pose estimation with a webcam or video file
407-person-tracking-webcam
Binder
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 binder logo 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.

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⚙️ 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

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💻 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.

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🧹 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

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⚠️ 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.

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