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Workshops around Google AI specifically on Building AI Agents with Google's Agent Development Kit (ADK) and Google Live APIs

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<h1> LabDotGDE™ Workshops and Notebooks</h1>

Workshops around Google AI specifically on Building AI Agents with Google's Agent Development Kit (ADK) and Google Live APIs

+ A collection of ready-to-run Jupyter notebooks for learning and experimenting with Deep Learning.

[![-----------------------------------------------------](https://user-images.githubusercontent.com/10940214/155750931-fc094349-b6ec-4e1f-9f9a-113e67941119.jpg)]()

## 2025 - Labs and Notebooks
Check out the latest notebooks that show how to optimize and deploy popular models on Intel CPU and GPU.

| **Notebook / Workshop** | **Description** | **Complementary Materials** | **Quiz / Exercise / Labs** |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Unlocking the power of Generative AI with Gemini Campus Connect - CUEA [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lnjeo-pk4XHp1ZBwm7DTV2mXnt_lBN23)| The hands-on session was based on Generative AI. Beginning with an introduction to Deep Learning and the foundational principles of neural networks using Keras and TensorFlow, participants gained insight into the building blocks of modern AI. I then transitioned into Large Language Models (LLMs), neural networks trained on massive datasets, unleashing capabilities for content generation. Developers were then introduced to the landscape of available foundational models, with a special focus on Gemini. Through live demonstrations, developers were guided through the practical application of Gemini, both within Google AI Studio and through APIs in VS Code and Colab. | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |
| Building Agentic Solutions with Gemini and ReACT Kisumu [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LkY6OG9H7R2NvsgzdCYVJ7QKIfStYgSr)| This live-coding session will explore the powerful Gemini API and walk through the best practices for function calling. You will learn how to write simple Python functions as tools, and build a ReAct agent that can choose the right tool for a task, including scraping websites or generating content. We will also get an intro into prompt engineering, a new state-of-the-art practice to transform raw data, and how to create a conversational assistant with LlamaIndex.| <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |
| Unlocking the power of Generative AI with Gemini Campus Connect - CUEA [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lnjeo-pk4XHp1ZBwm7DTV2mXnt_lBN23)| The hands-on session was based on Generative AI. Beginning with an introduction to Deep Learning and the foundational principles of neural networks using Keras and TensorFlow, participants gained insight into the building blocks of modern AI. I then transitioned into Large Language Models (LLMs), neural networks trained on massive datasets, unleashing capabilities for content generation. Developers were then introduced to the landscape of available foundational models, with a special focus on Gemini. Through live demonstrations, developers were guided through the practical application of Gemini, both within Google AI Studio and through APIs in VS Code and Colab. | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |
| Keras 3 - A Spectrum of Workflows Zindi [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1T8LA5bygK4p2L5GyR8V1xSF9tu6KxFxJ)| The workshop will explore the powerful Keras 3 API and walk through deep learning best practices, how to write a simple model, and run it with a backend of your choice, including PyTorch, TensorFlow, or JAX. We will also got an intro into Gemma, the new state-of-the-art open models and how to install it and manage the LLM with Keras 3. | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |
| Working with Gemini Pro and LangChains Buld With AI [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1q2TcaoXqxNz92bAQaBisomZUUk1VRqY6)| This tutorial offers an exploration of Gemini and LangChains. Our primary goal is to demystify complex concepts, help you learn how to effectively load & store documents using LangChain and build a question-answering Gemini bot that answers questions based on your documents. | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |
| Creating beautiful art with Stable Diffusion AMLD Africa - Nairobi [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1aYlso-T-9DkswERrGl3omrR-n2zvczOs) Lab 2 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Uulic4so-RHJfpFaCzbnc1cs1QymtRkv) Lab 3 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1jN-AksZQr7lBDJi5Q7Y5ah9fTY1vMv2q)| The hands-on session was based on Generative AI. Beginning with an introduction to Deep Learning and the foundational principles of neural networks using Keras and TensorFlow, participants gained insight into the building blocks of modern AI. I then transitioned into Large Language Models (LLMs), neural networks trained on massive datasets, unleashing capabilities for content generation. Developers were then introduced to the landscape of available foundational models, with a special focus on Gemini. Through live demonstrations, developers were guided through the practical application of Gemini, both within Google AI Studio and through APIs in VS Code and Colab. | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |
| Unlocking the power of Generative AI with Gemini Campus Connect - CUEA [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lnjeo-pk4XHp1ZBwm7DTV2mXnt_lBN23)| The hands-on session was based on Generative AI. Beginning with an introduction to Deep Learning and the foundational principles of neural networks using Keras and TensorFlow, participants gained insight into the building blocks of modern AI. I then transitioned into Large Language Models (LLMs), neural networks trained on massive datasets, unleashing capabilities for content generation. Developers were then introduced to the landscape of available foundational models, with a special focus on Gemini. Through live demonstrations, developers were guided through the practical application of Gemini, both within Google AI Studio and through APIs in VS Code and Colab. | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=300> | [Blog - How to get YOLOv8 Over 1000 fps with Intel GPUs?](https://medium.com/openvino-toolkit/how-to-get-yolov8-over-1000-fps-with-intel-gpus-9b0eeee879) |

## Table of Contents

- [🚀 AI Trends - Notebooks](#-ai-trends---notebooks)
- [Table of Contents](#table-of-contents)
- [📝 Installation Guide](#-installation-guide)
- [🚀 Getting Started](#-getting-started)
	- [💻 First steps](#-first-steps)
	- [⌚ Convert \& Optimize](#-convert--optimize)
	- [🎯 Model Demos](#-model-demos)
	- [🏃 Model Training](#-model-training)
	- [📺 Live Demos](#-live-demos)
- [⚙️ System Requirements](#️-system-requirements)
- [💻 Run the Notebooks](#-run-the-notebooks)
	- [To Launch a Single Notebook](#to-launch-a-single-notebook)
	- [To Launch all Notebooks](#to-launch-all-notebooks)
- [🧹 Cleaning Up](#-cleaning-up)
- [⚠️ Troubleshooting](#️-troubleshooting)
- [🧑‍💻 Contributors](#-contributors)
- [❓ FAQ](#-faq)

[![-----------------------------------------------------](https://user-images.githubusercontent.com/10940214/155750931-fc094349-b6ec-4e1f-9f9a-113e67941119.jpg)]()

## 📝 Installation Guide

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

| [Windows](https://github.com/openvinotoolkit/openvino_notebooks/wiki/Windows) | [Ubuntu](https://github.com/openvinotoolkit/openvino_notebooks/wiki/Ubuntu) | [macOS](https://github.com/openvinotoolkit/openvino_notebooks/wiki/macOS) | [Red Hat](https://github.com/openvinotoolkit/openvino_notebooks/wiki/Red-Hat-and-CentOS) | [CentOS](https://github.com/openvinotoolkit/openvino_notebooks/wiki/Red-Hat-and-CentOS) | [Azure ML](https://github.com/openvinotoolkit/openvino_notebooks/wiki/AzureML) | [Docker](https://github.com/openvinotoolkit/openvino_notebooks/wiki/Docker) | [Amazon SageMaker](https://github.com/openvinotoolkit/openvino_notebooks/wiki/SageMaker) |
| ----------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | --------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |

[![-----------------------------------------------------](https://user-images.githubusercontent.com/10940214/155750931-fc094349-b6ec-4e1f-9f9a-113e67941119.jpg)]()

## 🚀 Getting Started

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

**NOTE: The main branch of this repository was updated to support the new OpenVINO 2023.3 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](#-installation-guide) section below. If you wish to use the previous release version of OpenVINO, please checkout the [2023.2 branch](https://github.com/openvinotoolkit/openvino_notebooks/tree/2023.2). If you wish to use the previous Long Term Support (LTS) version of OpenVINO check out the [2022.3 branch](https://github.com/openvinotoolkit/openvino_notebooks/tree/2022.3).

If you need help, please start a GitHub [Discussion](https://github.com/openvinotoolkit/openvino_notebooks/discussions).

### 💻 First steps

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

| [001-hello-world](notebooks/001-hello-world/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F001-hello-world%2F001-hello-world.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/001-hello-world/001-hello-world.ipynb) | [002-openvino-api](https://raw.githubusercontent.com/ngesa254/labsdotgde/main/notebooks/002-openvino-api/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F002-openvino-api%2F002-openvino-api.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/002-openvino-api/002-openvino-api.ipynb) | [003-hello-segmentation](https://raw.githubusercontent.com/ngesa254/labsdotgde/main/notebooks/003-hello-segmentation/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F003-hello-segmentation%2F003-hello-segmentation.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/003-hello-segmentation/003-hello-segmentation.ipynb) | [004-hello-detection](https://raw.githubusercontent.com/ngesa254/labsdotgde/main/notebooks/004-hello-detection/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F004-hello-detection%2F004-hello-detection.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/004-hello-detection/004-hello-detection.ipynb) | 
| -------------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | 
| Classify an image with OpenVINO | Learn the OpenVINO Python API | Semantic segmentation with OpenVINO | Text detection with OpenVINO | 
| <img src="https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg" width=140> | <img src="https://user-images.githubusercontent.com/15709723/127787560-d8ec4d92-b4a0-411f-84aa-007e90faba98.png" width=250> | <img src="https://user-images.githubusercontent.com/15709723/128290691-e2eb875c-775e-4f4d-a2f4-15134044b4bb.png" width=150> | <img src="https://user-images.githubusercontent.com/36741649/128489933-bf215a3f-06fa-4918-8833-cb0bf9fb1cc7.jpg" width=150> |

### ⌚ Convert & Optimize

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

| Notebook | Description |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |:----------------------------------------------------------------------------------------------------------------------------|
| [101-tensorflow-classification-to-openvino](notebooks/101-tensorflow-classification-to-openvino/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F101-tensorflow-classification-to-openvino%2F101-tensorflow-classification-to-openvino.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb) | Convert TensorFlow models to OpenVINO IR |
| [102-pytorch-to-openvino](notebooks/102-pytorch-to-openvino/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/102-pytorch-to-openvino/102-pytorch-to-openvino.ipynb) | Convert PyTorch models to OpenVINO IR |
| [103-paddle-to-openvino](notebooks/103-paddle-to-openvino/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F103-paddle-to-openvino%2F103-paddle-to-openvino-classification.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/103-paddle-to-openvino/103-paddle-to-openvino-classification.ipynb) | Convert PaddlePaddle models to OpenVINO IR |
| [104-model-tools](notebooks/104-model-tools/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F104-model-tools%2F104-model-tools.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/104-model-tools/104-model-tools.ipynb) | Download, convert and benchmark models from Open Model Zoo |
| [105-language-quantize-bert](notebooks/105-language-quantize-bert/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/105-language-quantize-bert/105-language-quantize-bert.ipynb) | Optimize and quantize a pre-trained BERT model |
| [106-auto-device](notebooks/106-auto-device/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F106-auto-device%2F106-auto-device.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/106-auto-device/106-auto-device.ipynb) | Demonstrate how to use AUTO Device |
| [107-speech-recognition-quantization](notebooks/107-speech-recognition-quantization/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/107-speech-recognition-quantization/107-speech-recognition-quantization-data2vec.ipynb) | Quantize speech recognition models using NNCF PTQ API |
| [108-gpu-device](notebooks/108-gpu-device/) | Working with GPUs in OpenVINO™ |
| [109-performance-tricks](notebooks/109-performance-tricks/)| Performance tricks in OpenVINO™ |
| [110-ct-segmentation-quantize](notebooks/110-ct-segmentation-quantize/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F110-ct-segmentation-quantize%2F110-ct-scan-live-inference.ipynb) | Quantize a kidney segmentation model and show live inference | 
| [112-pytorch-post-training-quantization-nncf](notebooks/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](notebooks/113-image-classification-quantization/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F113-image-classification-quantization%2F113-image-classification-quantization.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/113-image-classification-quantization/113-image-classification-quantization.ipynb) | Quantize Image Classification model |
| [115-async-api](notebooks/115-async-api/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F115-async-api%2F115-async-api.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/115-async-api/115-async-api.ipynb) | Use Asynchronous Execution to Improve Data Pipelining | |
| [116-sparsity-optimization](notebooks/116-sparsity-optimization/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/116-sparsity-optimization/116-sparsity-optimization.ipynb) | Improve performance of sparse Transformer models |
| [117-model-server](notebooks/117-model-server/)| Introduction to model serving with OpenVINO™ Model Server (OVMS) |
| [118-optimize-preprocessing](notebooks/118-optimize-preprocessing/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/118-optimize-preprocessing/118-optimize-preprocessing.ipynb) | Improve performance of image preprocessing step |
| [119-tflite-to-openvino](notebooks/119-tflite-to-openvino/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/119-tflite-to-openvino/119-tflite-to-openvino.ipynb) | Convert TensorFlow Lite models to OpenVINO IR | |
| [120-tensorflow-object-detection-to-openvino](notebooks/120-tensorflow-object-detection-to-openvino/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F120-tensorflow-object-detection-to-openvino%2F120-tensorflow-object-detection-to-openvino.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/120-tensorflow-object-detection-to-openvino/120-tensorflow-object-detection-to-openvino.ipynb) | Convert TensorFlow Object Detection models to OpenVINO IR |
| [121-convert-to-openvino](notebooks/121-convert-to-openvino/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F121-convert-to-openvino%2F121-convert-to-openvino.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/121-convert-to-openvino/121-convert-to-openvino.ipynb) | Learn OpenVINO model conversion API |
| [122-quantizing-model-with-accuracy-control](notebooks/122-quantizing-model-with-accuracy-control/)| Quantizing with Accuracy Control using NNCF |
| [123-detectron2-to-openvino](notebooks/123-detectron2-to-openvino/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F123-detectron2-to-openvino%2F123-detectron2-to-openvino.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/123-detectron2-to-openvino/123-detectron2-to-openvino.ipynb) | Convert Detectron2 models to OpenVINO IR |
| [124-hugging-face-hub](notebooks/124-hugging-face-hub/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F124-hugging-face-hub%2F124-hugging-face-hub.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/124-hugging-face-hub/124-hugging-face-hub.ipynb) | Load models from Hugging Face Model Hub with OpenVINO™ |
| [125-torchvision-zoo-to-openvino](notebooks/125-torchvision-zoo-to-openvino/) Classification [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F125-torchvision-zoo-to-openvino%2F125-convnext-classification.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/125-torchvision-zoo-to-openvino/125-convnext-classification.ipynb) Semantic Segmentation [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F125-torchvision-zoo-to-openvino%2F125-lraspp-segmentation.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/125-torchvision-zoo-to-openvino/125-lraspp-segmentation.ipynb)| Convert torchvision classification and semantic segmentation models to OpenVINO IR |
| [126-tensorflow-hub](notebooks/126-tensorflow-hub/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F126-tensorflow-hub%2F126-tensorflow-hub.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/126-tensorflow-hub/126-tensorflow-hub.ipynb) | Convert TensorFlow Hub models to OpenVINO IR |

### 🎯 Model Demos

Demos that demonstrate inference on a particular model.

| Notebook | Description | Preview |
| :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| [201-vision-monodepth](notebooks/201-vision-monodepth/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F201-vision-monodepth%2F201-vision-monodepth.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/201-vision-monodepth/201-vision-monodepth.ipynb) | Monocular depth estimation with images and video | <img src="https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif" width=250> |
| [202-vision-superresolution-image](notebooks/202-vision-superresolution/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F202-vision-superresolution%2F202-vision-superresolution-image.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/202-vision-superresolution/202-vision-superresolution-image.ipynb) | Upscale raw images with a super resolution model | <img src="https://user-images.githubusercontent.com/36741649/170005347-e4409f9e-ec34-416b-afdf-a9d8185929ca.jpg" width="70">→<img src="https://user-images.githubusercontent.com/36741649/170005347-e4409f9e-ec34-416b-afdf-a9d8185929ca.jpg" width="130"> |
| [202-vision-superresolution-video](notebooks/202-vision-superresolution/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F202-vision-superresolution%2F202-vision-superresolution-video.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/202-vision-superresolution/202-vision-superresolution-video.ipynb) | Turn 360p into 1080p video using a super resolution model | <img src="https://user-images.githubusercontent.com/15709723/127269258-a8e2c03e-731e-4317-b5b2-ed2ee767ff5e.gif" width=80>→<img src="https://user-images.githubusercontent.com/15709723/127269258-a8e2c03e-731e-4317-b5b2-ed2ee767ff5e.gif" width="125"> |
| [203-meter-reader](notebooks/203-meter-reader/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F203-meter-reader%2F203-meter-reader.ipynb) | PaddlePaddle pre-trained models to read industrial meter's value | <img src="https://user-images.githubusercontent.com/91237924/166135627-194405b0-6c25-4fd8-9ad1-83fb3a00a081.jpg" width=225> |
| [204-segmenter-semantic-segmentation](notebooks/204-segmenter-semantic-segmentation/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/204-segmenter-semantic-segmentation/204-segmenter-semantic-segmentation.ipynb) | Semantic Segmentation with OpenVINO™ using Segmenter | <img src=https://user-images.githubusercontent.com/61357777/223854308-d1ac4a39-cc0c-4618-9e4f-d9d4d8b991e8.jpg width=225> |
| [205-vision-background-removal](notebooks/205-vision-background-removal/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F205-vision-background-removal%2F205-vision-background-removal.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/205-vision-background-removal/205-vision-background-removal.ipynb) | Remove and replace the background in an image using salient object detection | <img src="https://user-images.githubusercontent.com/15709723/125184237-f4b6cd00-e1d0-11eb-8e3b-d92c9a728372.png" width=455> |
| [206-vision-paddlegan-anime](notebooks/206-vision-paddlegan-anime/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/206-vision-paddlegan-anime/206-vision-paddlegan-anime.ipynb) | Turn an image into anime using a GAN | <img src="https://user-images.githubusercontent.com/15709723/127788059-1f069ae1-8705-4972-b50e-6314a6f36632.jpeg" width=100>→<img src="https://user-images.githubusercontent.com/15709723/125184441-b4584e80-e1d2-11eb-8964-d8131cd97409.png" width=100> |
| [207-vision-paddlegan-superresolution](notebooks/207-vision-paddlegan-superresolution/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/207-vision-paddlegan-superresolution/207-vision-paddlegan-superresolution.ipynb) | Upscale small images with superresolution using a PaddleGAN model| |
| [208-optical-character-recognition](notebooks/208-optical-character-recognition/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/208-optical-character-recognition/208-optical-character-recognition.ipynb) | Annotate text on images using text recognition resnet | <img src="https://user-images.githubusercontent.com/36741649/129315292-a37266dc-dfb2-4749-bca5-2ac9c1e93d64.jpg" width=225> |
| [209-handwritten-ocr](notebooks/209-handwritten-ocr/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F209-handwritten-ocr%2F209-handwritten-ocr.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/209-handwritten-ocr/209-handwritten-ocr.ipynb) | OCR for handwritten simplified Chinese and Japanese | <img width="425" alt="handwritten_simplified_chinese_test" src="https://user-images.githubusercontent.com/36741649/132660640-da2211ec-c389-450e-8980-32a75ed14abb.png"> 的人不一了是他有为在责新中任自之我们 |
| [210-slowfast-video-recognition](notebooks/210-slowfast-video-recognition/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F210-slowfast-video-recognition%2F210-slowfast-video-recognition.ipynb) | Video Recognition using SlowFast and OpenVINO™ | <img src=https://github.com/facebookresearch/SlowFast/raw/main/demo/ava_demo.gif width=225> | 
| [211-speech-to-text](notebooks/211-speech-to-text/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F211-speech-to-text%2F211-speech-to-text.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/211-speech-to-text/211-speech-to-text.ipynb) | Run inference on speech-to-text recognition model | <img src="https://user-images.githubusercontent.com/36741649/140987347-279de058-55d7-4772-b013-0f2b12deaa61.png" width=225>|
| [212-pyannote-speaker-diarization](notebooks/212-pyannote-speaker-diarization/) | Run inference on speaker diarization pipeline | <img src="https://user-images.githubusercontent.com/29454499/218432101-0bd0c424-e1d8-46af-ba1d-ee29ed6d1229.png" width=225>|
| [213-question-answering](notebooks/213-question-answering/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F213-question-answering%2F213-question-answering.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/213-question-answering/213-question-answering.ipynb) | Answer your questions basing on a context | <img src="https://user-images.githubusercontent.com/4547501/152571639-ace628b2-e3d2-433e-8c28-9a5546d76a86.gif" width=225> |
| [214-grammar-correction](notebooks/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](notebooks/215-image-inpainting/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F215-image-inpainting%2F215-image-inpainting.ipynb)| Fill missing pixels with image in-painting | <img src="https://user-images.githubusercontent.com/4547501/167121084-ec58fbdb-b269-4de2-9d4c-253c5b95de1e.png" width=225> |
| [216-attention-center](notebooks/216-attention-center/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/216-attention-center/216-attention-center.ipynb) | The attention center model with OpenVINO™ | |
| [217-vision-deblur](notebooks/217-vision-deblur/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F217-vision-deblur%2F217-vision-deblur.ipynb) | Deblur Images with DeblurGAN-v2 | <img src="https://user-images.githubusercontent.com/41332813/158430181-05d07f42-cdb8-4b7a-b7dc-e7f7d9391877.png" width=225> |
| [218-vehicle-detection-and-recognition](notebooks/218-vehicle-detection-and-recognition/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F218-vehicle-detection-and-recognition%2F218-vehicle-detection-and-recognition.ipynb) | Use pre-trained models to detect and recognize vehicles and their attributes with OpenVINO | <img src = "https://user-images.githubusercontent.com/47499836/163544861-fa2ad64b-77df-4c16-b065-79183e8ed964.png" width=225> |
| [219-knowledge-graphs-conve](notebooks/219-knowledge-graphs-conve/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F219-knowledge-graphs-conve%2F219-knowledge-graphs-conve.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/219-knowledge-graphs-conve/219-knowledge-graphs-conve.ipynb) | Optimize the knowledge graph embeddings model (ConvE) with OpenVINO ||
| [220-books-alignment-labse](notebooks/220-cross-lingual-books-alignment/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F220-cross-lingual-books-alignment%2F220-cross-lingual-books-alignment.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/220-cross-lingual-books-alignment/220-cross-lingual-books-alignment.ipynb)| Cross-lingual Books Alignment With Transformers and OpenVINO™ | |
| [221-machine-translation](notebooks/221-machine-translation) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F221-machine-translation%2F221-machine-translation.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/221-machine-translation/221-machine-translation.ipynb) | Real-time translation from English to German | |
| [222-vision-image-colorization](notebooks/222-vision-image-colorization/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F222-vision-image-colorization%2F222-vision-image-colorization.ipynb) | Use pre-trained models to colorize black \& white images using OpenVINO | <img src = "https://user-images.githubusercontent.com/18904157/166343139-c6568e50-b856-4066-baef-5cdbd4e8bc18.png" width=225> |
| [223-text-prediction](notebooks/223-text-prediction/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/223-text-prediction/223-text-prediction.ipynb) | Use pretrained models to perform text prediction on an input sequence | <img src=https://user-images.githubusercontent.com/91228207/185105225-0f996b0b-0a3b-4486-872d-364ac6fab68b.png width=225> |
| [224-3D-segmentation-point-clouds](notebooks/224-3D-segmentation-point-clouds/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/224-3D-segmentation-point-clouds/224-3D-segmentation-point-clouds.ipynb) | Process point cloud data and run 3D Part Segmentation with OpenVINO | <img src = "https://user-images.githubusercontent.com/91237924/185752178-3882902c-907b-4614-b0e6-ea1de08bf3ef.png" width=225> |
| [225-stable-diffusion-text-to-image](notebooks/225-stable-diffusion-text-to-image) | Text-to-image generation with Stable Diffusion method | <img src=https://user-images.githubusercontent.com/29454499/216524089-ed671fc7-a78b-42bf-aa96-9f7c791a9419.png width=225>|
| [226-yolov7-optimization](notebooks/226-yolov7-optimization/) | Optimize YOLOv7 using NNCF PTQ API | <img src=https://raw.githubusercontent.com/WongKinYiu/yolov7/main/figure/horses_prediction.jpg width=225> |
| [227-whisper-subtitles-generation](notebooks/227-whisper-subtitles-generation/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/227-whisper-subtitles-generation/227-whisper-convert.ipynb) | Generate subtitles for video with OpenAI Whisper and OpenVINO | <img src=https://user-images.githubusercontent.com/29454499/204548693-1304ef33-c790-490d-8a8b-d5766acb6254.png width=225> |
| [228-clip-zero-shot-image-classification](notebooks/228-clip-zero-shot-image-classification) | Perform Zero-shot Image Classification with CLIP and OpenVINO | <img src=https://user-images.githubusercontent.com/29454499/207795060-437b42f9-e801-4332-a91f-cc26471e5ba2.png width=500> |
| [229-distilbert-sequence-classification](notebooks/229-distilbert-sequence-classification/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F229-distilbert-sequence-classification%2F229-distilbert-sequence-classification.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/229-distilbert-sequence-classification/229-distilbert-sequence-classification.ipynb) | Sequence Classification with OpenVINO | <img src = "https://user-images.githubusercontent.com/95271966/206130638-d9847414-357a-4c79-9ca7-76f4ae5a6d7f.png" width=225> |
| [230-yolov8-object-detection](notebooks/230-yolov8-optimization/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-object-detection.ipynb) | Optimize YOLOv8 object detection using NNCF PTQ API | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=225> |
| [230-yolov8-instance-segmentation](notebooks/230-yolov8-optimization/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-instance-segmentation.ipynb) | Optimize YOLOv8 instance segmentation using NNCF PTQ API | <img src = "https://user-images.githubusercontent.com/29454499/272966666-b4c05f7b-1b64-4335-a125-c20105da7cc4.png" width=225> |
| [230-yolov8-keypoint-detection](notebooks/230-yolov8-optimization/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-keypoint-detection.ipynb) | Optimize YOLOv8 keypoint detection using NNCF PTQ API | <img src = "https://user-images.githubusercontent.com/29454499/272967083-a36d1072-e2f6-4dd9-85ad-4b2b3b4341d5.png" width=225> |
| [231-instruct-pix2pix-image-editing](notebooks/231-instruct-pix2pix-image-editing/) | Image editing with InstructPix2Pix | <img src=https://user-images.githubusercontent.com/29454499/219943222-d46a2e2d-d348-4259-8431-37cf14727eda.png width=225> |
| [232-clip-language-saliency-map](notebooks/232-clip-language-saliency-map/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/232-clip-language-saliency-map/232-clip-language-saliency-map.ipynb) | Language-Visual Saliency with CLIP and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/218967961-9858efd5-fff2-4eb0-bde9-60852f4b31cb.JPG width=225> | 
| [233-blip-visual-language-processing](notebooks/233-blip-visual-language-processing/) | Visual Question Answering and Image Captioning using BLIP and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/221933762-4ff32ecb-5e5d-4484-80e1-e9396cb3c511.png width=225> |
| [234-encodec-audio-compression](notebooks/234-encodec-audio-compression/) | Audio compression with EnCodec and OpenVINO™ | <img src=https://github.com/facebookresearch/encodec/raw/main/thumbnail.png width=225> |
| [235-controlnet-stable-diffusion](notebooks/235-controlnet-stable-diffusion/) | A Text-to-Image Generation with ControlNet Conditioning and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/224541412-9d13443e-0e42-43f2-8210-aa31820c5b44.png width=225> |
| [236-stable-diffusion-v2](notebooks/236-stable-diffusion-v2/) | Text-to-Image Generation and Infinite Zoom with Stable Diffusion v2 and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/228882108-25c1f65d-4c23-4e1d-8ba4-f6164280a3e3.gif width=225> |
| [237-segment-anything](notebooks/237-segment-anything/) | Prompt based segmentation using Segment Anything and OpenVINO™.| <img src=https://user-images.githubusercontent.com/29454499/231468849-1cd11e68-21e2-44ed-8088-b792ef50c32d.png width=225> |
| [238-deep-floyd-if](notebooks/238-deepfloyd-if/) | Text-to-Image Generation with DeepFloyd IF and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/241643886-dfcf3c48-8d50-4730-ae28-a21595d9504f.png width=225> |
| [239-image-bind](notebooks/239-image-bind/) | Binding multimodal data using ImageBind and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/240364108-39868933-d221-41e6-9b2e-dac1b14ef32f.png width=225> |
| [240-dolly-2-instruction-following](notebooks/240-dolly-2-instruction-following/) | Instruction following using Databricks Dolly 2.0 and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/237291423-022f07d2-966b-4be2-9a1c-98f1cf0691c2.png width=225> |
| [241-riffusion-text-to-music](notebooks/241-riffusion-text-to-music/) | Text-to-Music generation using Riffusion and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/244291912-bbc6e08c-c0a9-41fe-bc2d-5f89a0d2463b.png width=225> | 
| [242-freevc-voice-conversion](notebooks/242-freevc-voice-conversion/) | High-Quality Text-Free One-Shot Voice Conversion with FreeVC and OpenVINO™ ||
| [243-tflite-selfie-segmentation](notebooks/243-tflite-selfie-segmentation/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F243-tflite-selfie-segmentation%2F243-tflite-selfie-segmentation.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/243-tflite-selfie-segmentation/243-tflite-selfie-segmentation.ipynb)| Selfie Segmentation using TFLite and OpenVINO™ | <img src="https://user-images.githubusercontent.com/29454499/251085926-14045ebc-273b-4ccb-b04f-82a3f7811b87.gif" width=400>|
| [244-named-entity-recognition](notebooks/244-named-entity-recognition/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/244-named-entity-recognition/244-named-entity-recognition.ipynb) | Named entity recognition with OpenVINO™ | |
| [245-typo-detector](notebooks/245-typo-detector/) | English Typo Detection in sentences with OpenVINO™ | <img src=https://user-images.githubusercontent.com/80534358/224564463-ee686386-f846-4b2b-91af-7163586014b7.png width=225> |
| [246-depth-estimation-videpth](notebooks/246-depth-estimation-videpth/) | Monocular Visual-Inertial Depth Estimation with OpenVINO™ | <img src=https://raw.githubusercontent.com/alexklwong/void-dataset/master/figures/void_samples.png width=225> |
| [247-code-language-id](notebooks/247-code-language-id/247-code-language-id.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F247-code-language-id%2F247-code-language-id.ipynb) | Identify the programming language used in an arbitrary code snippet | ||
| [248-stable-diffusion-xl](notebooks/248-stable-diffusion-xl/) | Image generation with Stable Diffusion XL and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/258651862-28b63016-c5ff-4263-9da8-73ca31100165.jpeg width=225> |
| [249-oneformer-segmentation](notebooks/249-oneformer-segmentation/) | Universal segmentation with OneFormer and OpenVINO™ | <img src="https://github-production-user-asset-6210df.s3.amazonaws.com/76161256/258640713-f801bd09-e927-4abd-aa2f-9990de4caf8d.gif" width=225> |
| [250-music-generation](notebooks/250-music-generation/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F250-music-generation%2F250-music-generation.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/250-music-generation/250-music-generation.ipynb) | Controllable Music Generation with MusicGen and OpenVINO™ | <img src="https://user-images.githubusercontent.com/76463150/260439306-81c81c8d-1f9c-41d0-b881-9491766def8e.png" width=225> |
| [251-tiny-sd-image-generation](notebooks/251-tiny-sd-image-generation/) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/251-tiny-sd-image-generation/251-tiny-sd-image-generation.ipynb) | Image Generation with Tiny-SD and OpenVINO™ | <img src="https://user-images.githubusercontent.com/29454499/260904650-274fc2f9-24d2-46a3-ac3d-d660ec3c9a19.png" width=225> |
| [252-fastcomposer-image-generation](notebooks/252-fastcomposer-image-generation/) | Image generation with FastComposer and OpenVINO™ | |
| [253-zeroscope-text2video](notebooks/253-zeroscope-text2video) | Text-to-video synthesis with ZeroScope and OpenVINO™ | A panda eating bamboo on a rock <img src="https://github.com/itrushkin/openvino_notebooks/assets/76161256/500956d5-4aac-4710-a77c-4df34bcda3be" width=225> |
| [254-llm-chatbot](notebooks/254-llm-chatbot) | Create LLM-powered Chatbot using OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/255799218-611e7189-8979-4ef5-8a80-5a75e0136b50.png width=600> |
| [255-mms-massively-multilingual-speech](notebooks/255-mms-massively-multilingual-speech/) | MMS: Scaling Speech Technology to 1000+ languages with OpenVINO™ | |
| [256-bark-text-to-audio](notebooks/256-bark-text-to-audio) | Text-to-Speech generation using Bark and OpenVINO™ | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/9a770279-0045-480e-95f2-1a2f2d0a5115 width=225> |
| [257-llava-multimodal-chatbot](notebooks/257-llava-multimodal-chatbot) | Visual-language assistant with LLaVA and OpenVINO™ | <img src=https://raw.githubusercontent.com/haotian-liu/LLaVA/main/images/llava_logo.png width=225> |
| [258-blip-diffusion-subject-generation](notebooks/258-blip-diffusion-subject-generation) | Subject-driven image generation and editing using BLIP Diffusion and OpenVINO™ | <img src="https://github.com/itrushkin/openvino_notebooks/assets/76161256/0ecf621f-b544-44ae-8258-8a49be704989" width=225 /> |
| [259-decidiffusion-image-generation](notebooks/259-decidiffusion-image-generation) | Image generation with DeciDiffusion and OpenVINO™ | <img src=https://github.com/huggingface/optimum-intel/assets/29454499/cd734349-9954-4656-ab96-08a903e846ef width=225> |
| [260-pix2struct-docvqa](notebooks/260-pix2struct-docvqa) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/260-pix2struct-docvqa/260-pix2struct-docvqa.ipynb) | Document Visual Question Answering using Pix2Struct and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/276283074-df7464e6-8293-4c6c-8f77-8e95d8f94c11.png width=400>
| [261-fast-segment-anything](notebooks/261-fast-segment-anything/) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F261-fast-segment-anything%2F261-fast-segment-anything.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/261-fast-segment-anything/261-fast-segment-anything.ipynb) | Object segmentations with FastSAM and OpenVINO™ | <img src="https://user-images.githubusercontent.com/26833433/248551984-d98f0f6d-7535-45d0-b380-2e1440b52ad7.jpg" width=225> |
| [262-softvc-voice-conversion](notebooks/262-softvc-voice-conversion) | SoftVC VITS Singing Voice Conversion and OpenVINO™ | |
| [263-latent-consistency-models-image-generation](notebooks/263-latent-consistency-models-image-generation) | Image generation with Latent Consistency Models (LCM) and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/277367065-13a8f622-8ea7-4d12-b3f8-241d4499305e.png width=225> |
| [264-qrcode-monster](notebooks/264-qrcode-monster/) | Generate creative QR codes with ControlNet QR Code Monster and OpenVINO™ | <img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/76463150/1a5978c6-e7a0-4824-9318-a3d8f4912c47" width=225> |
| [265-wuerstchen-image-generation](notebooks/265-wuerstchen-image-generation) | Text-to-image generation with Würstchen and OpenVINO™ | <img src="https://github.com/itrushkin/openvino_notebooks/assets/76161256/6917c558-d74c-4cc9-b81a-679ce0a299ee" width=225> |
| [266-speculative-sampling](notebooks/266-speculative-sampling) | Text Generation via Speculative Sampling, KV Caching, and OpenVINO™ | <img src=https://user-images.githubusercontent.com/29454499/280659301-49a38beb-e6f3-4a2c-858e-be4ca4491016.png width=225> |
| [267-distil-whisper-asr](notebooks/267-distil-whisper-asr) | Automatic speech recognition using Distil-Whisper and OpenVINO™ | |
| [268-table-question-answering](notebooks/268-table-question-answering) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/268-table-question-answering/268-table-question-answering.ipynb) | Table Question Answering using TAPAS and OpenVINO™ ||
| [269-film-slowmo](notebooks/269-film-slowmo) | Frame interpolation with FILM and OpenVINO™ | <img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/1720147/8ac5178d-4a92-4a86-a3df-dd494434fed6" width=225> |
| [270-sound-generation-audioldm2](notebooks/270-sound-generation-audioldm2/) | Sound Generation with AudioLDM2 and OpenVINO™ | <img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/76463150/c93a0f86-d9cf-4bd1-93b9-e27532170d75" width=225> |
| [271-sdxl-turbo](notebooks/271-sdxl-turbo/) | Single-step image generation using SDXL-turbo and OpenVINO | <img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/79b625c7-0f0a-4f19-8e38-e6f896f75c3e" width=225> |
| [272-paint-by-example](notebooks/272-paint-by-example/) | Exemplar based image editing using diffusion models, [Paint-by-Example](https://github.com/Fantasy-Studio/Paint-by-Example), and OpenVINO™ | <img width="225" alt="ui_example" src="https://user-images.githubusercontent.com/103226580/236958011-0550ba7b-19b3-4dc6-8b50-eaedb84c1681.png"> |
| [273-stable-zephyr-3b-chatbot](notebooks/273-stable-zephyr-3b-chatbot) | Use Stable-Zephyr as chatbot assistant with OpenVINO | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/cfac6ddb-6f22-4343-855c-e513269cf2bf width=225> |
| [274-efficient-sam](notebooks/274-efficient-sam/) | Object segmentation with EfficientSAM and OpenVINO™ | <img width="225" src="https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/15d0a23a-0550-43c6-9ca9-f689e772a79a"> |
| [275-llm-question-answering](notebooks/275-llm-question-answering) | LLM Instruction following pipeline | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/daafd702-5a42-4f54-ae72-2e4480d73501 width=225> | 
| [276-stable-diffusion-torchdynamo-backend](notebooks/276-stable-diffusion-torchdynamo-backend/) | Image generation with Stable Diffusion and OpenVINO™ `torch.compile` feature | <img src=https://user-images.githubusercontent.com/32199725/270969617-ee88eb42-3b0d-42f4-ac71-992c40451849.png width=225> |
| [277-amused-lightweight-text-to-image](notebooks/277-amused-lightweight-text-to-image) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/277-amused-lightweight-text-to-image/277-amused-lightweight-text-to-image.ipynb) | Lightweight image generation with aMUSEd and OpenVINO™ | <img src=https://huggingface.co/amused/amused-256/resolve/main/assets/collage_small.png width=225> | 
| [278-stable-diffusion-ip-adapter](notebooks/278-stable-diffusion-ip-adapter) | Image conditioning in Stable Diffusion pipeline using IP-Adapter | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/182657d9-2aa3-40b3-9fc4-a90b803419fe width=300> |
| [279-mobilevlm-language-assistant](notebooks/279-mobilevlm-language-assistant) | Mobile language assistant with MobileVLM and OpenVINO | |
| [280-depth-anything](notebooks/280-depth-anything) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F280-depth-anythingh%2F280-depth-anything.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/280-depth-anything/280-depth-anything.ipynb) | Monocular Depth Estimation with DepthAnything and OpenVINO | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/a9a16658-512f-470c-a33c-0e1f9d0ae72c width=225> |
| [281-kosmos2-multimodal-large-language-model](notebooks/281-kosmos2-multimodal-large-language-model) | Kosmos-2: Multimodal Large Language Model and OpenVINO™ | <img src=https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg width=225> |
| [282-siglip-zero-shot-image-classification](notebooks/282-siglip-zero-shot-image-classification) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/282-siglip-zero-shot-image-classification/282-siglip-zero-shot-image-classification.ipynb) | Zero-shot Image Classification with SigLIP | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/67365453/c4eb782c-0fef-4a89-a5c6-5cc43518490b width=500> |
| [283-photo-maker](notebooks/283-photo-maker) | Text-to-image generation using PhotoMaker and OpenVINO | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/88bccc4a-5789-42ca-8a68-f402c3e7c5a4 width=225> |

### 🏃 Model Training

Tutorials that include code to train neural networks.

### 📺 Live Demos

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

If you run into issues, please check the [troubleshooting section](#-troubleshooting), [FAQs](#-faq) or start a GitHub [discussion](https://github.com/openvinotoolkit/openvino_notebooks/discussions).

Notebooks with ![binder logo](https://mybinder.org/badge_logo.svg) and ![colab logo](https://colab.research.google.com/assets/colab-badge.svg) buttons can be run without installing anything. [Binder](https://mybinder.org/) and [Google Colab](https://colab.research.google.com/) are free online services with limited resources. For the best performance, please follow the [Installation Guide](https://raw.githubusercontent.com/ngesa254/labsdotgde/main/) and run the notebooks locally.

[![-----------------------------------------------------](https://user-images.githubusercontent.com/10940214/155750931-fc094349-b6ec-4e1f-9f9a-113e67941119.jpg)]()
<div id='-system-requirements'></div>

## ⚙️ System Requirements

The notebooks run almost anywhere &mdash; 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)](https://www.python.org/) |
| :--------------------------------------------------------- |:---------------------------------------------------|
| Ubuntu 20.04 LTS, 64-bit                                   | 3.8 - 3.10                                         |
| Ubuntu 22.04 LTS, 64-bit                                   | 3.8 - 3.10                                         |
| Red Hat Enterprise Linux 8, 64-bit                         | 3.8 - 3.10                                         |
| CentOS 7, 64-bit                                           | 3.8 - 3.10                                         |
| macOS 10.15.x versions or higher                           | 3.8 - 3.10                                         |
| Windows 10, 64-bit Pro, Enterprise or Education editions   | 3.8 - 3.10                                         |
| Windows Server 2016 or higher                              | 3.8 - 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.

```bash
jupyter 201-vision-monodepth.ipynb
```

### To Launch all Notebooks

```bash
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.

[![-----------------------------------------------------](https://user-images.githubusercontent.com/10940214/155750931-fc094349-b6ec-4e1f-9f9a-113e67941119.jpg)]()
<div id='-cleaning-up'></div>

## ⚠️ Troubleshooting

If these tips do not solve your problem, please open a [discussion topic](https://github.com/openvinotoolkit/openvino_notebooks/discussions)
or create an [issue](https://github.com/openvinotoolkit/openvino_notebooks/issues)!

- 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`, double-check 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 [`contrib.rocks`](https://contrib.rocks).

[![-----------------------------------------------------](https://user-images.githubusercontent.com/10940214/155750931-fc094349-b6ec-4e1f-9f9a-113e67941119.jpg)]()
<div id='-faq'></div>

## ❓ FAQ

* [Which devices does OpenVINO support?](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html#doxid-openvino-docs-o-v-u-g-supported-plugins-supported-devices)
* [What is the first CPU generation you support with OpenVINO?](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/system-requirements.html)
* [Are there any success stories about deploying real-world solutions with OpenVINO?](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html)

---

\* Other names and brands may be claimed as the property of others.

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