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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.
- ➤ 📝 Installation Guide
- ➤ 🚀 Getting Started
- ➤ ⚙️ System Requirements
- ➤ 💻 Run the Notebooks
- ➤ 🧹 Cleaning Up
- ➤
⚠️ Troubleshooting - ➤ 🧑💻 Contributors
- ➤ ❓ FAQ
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 |
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The Jupyter notebooks are categorized into four classes, select one related to your needs or give them all a try. Good Luck!
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 |
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| Classify an image with OpenVINO | Learn the OpenVINO Python API | Semantic segmentation with OpenVINO | Text detection with OpenVINO |
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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 |
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| 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 |
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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 |
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 |
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| 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 |
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 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 |  |
| 223-gpt2-text-prediction |
Use GPT-2 to perform text prediction on an input sequence |  |
Tutorials that include code to train neural networks.
| Notebook | Description | Preview |
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| 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 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 |
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| 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 |
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If you run into issues, please check the troubleshooting section, FAQs or start a GitHub discussion.
Notebooks with a
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 |
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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.
| Supported Operating System | Python Version (64-bit) |
|---|---|
| Ubuntu 18.04 LTS, 64-bit | 3.6, 3.7, 3.8, 3.9 |
| Ubuntu 20.04 LTS, 64-bit | 3.6, 3.7, 3.8, 3.9 |
| Red Hat Enterprise Linux 8, 64-bit | 3.6, 3.8, 3.9 |
| CentOS 7, 64-bit | 3.6, 3.7, 3.8, 3.9 |
| macOS 10.15.x versions | 3.6, 3.7, 3.8, 3.9 |
| Windows 10, 64-bit Pro, Enterprise or Education editions | 3.6, 3.7, 3.8, 3.9 |
| Windows Server 2016 or higher | 3.6, 3.7, 3.8, 3.9 |
If you wish to launch only one notebook, like the Monodepth notebook, run the command below.
jupyter 201-vision-monodepth.ipynbjupyter lab notebooksIn 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.
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
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 theopenvino_envvirtual 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.
Made with contributors-img.
- 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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