add readme

README.md

@@ -59,7 +59,8 @@ Check out the latest notebooks that show how to optimize and deploy popular mode
 | [Kosmos-2: Grounding Multimodal Large Language Models](notebooks/281-kosmos2-multimodal-large-language-model)<br> | Kosmos-2: Grounding Multimodal Large Language Model and OpenVINO™ | <img src=https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg width=225> |
 | [PhotoMaker](notebooks/283-photo-maker)<br> | Text-to-image generation using PhotoMaker and OpenVINO | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/91237924/88bccc4a-5789-42ca-8a68-f402c3e7c5a4 width=300> |
 | [OpenVoice](notebooks/284-openvoice)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F284-openvoice%2F284-openvoice.ipynb)[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/284-openvoice/284-openvoice.ipynb) | OpenVoice a versatile instant voice tone transferring and generating speech in various languages. |<img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/5703039/ca7eab80-148d-45b0-84e8-a5a279846b51 width=300> |
-| [InstantID](notebooks/286-instant-id)<br> | InstantID: Zero-shot Identity-Preserving Image Generation using OpenVINO| <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/082b3da7-6bb6-4551-bfa6-0e43d8e80b51 width=300> |
+| [InstantID](notebooks/286-instant-id)<br> | InstantID: Zero-shot Identity-Preserving Image Generation using OpenVINO| <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/082b3da7-6bb6-4551-bfa6-0e43d8e80b51 width=300> | |
+| [YOLOv9 Optimization](notebooks/287-yolov9-optimization)<br> | Optimize YOLOv9 using NNCF | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/ae3a7653-eead-4c41-9cad-a7c95d3a4578 width=300> | |
 
 ## Table of Contents
 
@@ -246,6 +247,7 @@ Demos that demonstrate inference on a particular model.
 | [284-openvoice](notebooks/284-openvoice)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F284-openvoice%2F284-openvoice.ipynb)[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/284-openvoice/284-openvoice.ipynb) | OpenVoice a versatile instant voice tone transferring and generating speech in various languages. |<img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/5703039/ca7eab80-148d-45b0-84e8-a5a279846b51 width=300> |
 | [285-surya-line-level-text-detection](notebooks/285-surya-line-level-text-detection)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/285-surya-line-level-text-detection/285-surya-line-level-text-detection.ipynb) | Line-level text detection with Surya | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/67365453/7672eb6d-fafb-4ae3-b894-9f98acfeb53a width=225> |
 | [286-instant-id](notebooks/286-instant-id)<br> | InstantID: Zero-shot Identity-Preserving Image Generation using OpenVINO| <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/082b3da7-6bb6-4551-bfa6-0e43d8e80b51 width=225> |
+| [287-yolov9-optimization](notebooks/287-yolov9-optimization)<br> | Optimize YOLOv9 using NNCF | <img src=https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/ae3a7653-eead-4c41-9cad-a7c95d3a4578 width=225> |
 
 <div id='-model-training'></div>
 

notebooks/287-yolov9-optimization/README.md

@@ -0,0 +1,27 @@
+# Convert and Optimize YOLOv9 with OpenVINO™
+
+<p align="center">
+    <img src="https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/ae3a7653-eead-4c41-9cad-a7c95d3a4578"/>
+</p>
+
+YOLOv9 marks a significant advancement in real-time object detection, introducing groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). This model demonstrates remarkable improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the MS COCO dataset. More details about model can be found in [paper](https://arxiv.org/abs/2402.13616) and [original repository](https://github.com/WongKinYiu/yolov9).
+
+## Notebook Contents
+
+This tutorial demonstrates step-by-step instructions on how to run and optimize PyTorch YOLO V9 with OpenVINO.
+
+The tutorial consists of the following steps:
+
+- Prepare PyTorch model
+- Convert PyTorch model to OpenVINO IR
+- Run model inference with OpenVINO
+- Prepare and run optimization pipeline
+- Compare performance of the FP32 and quantized models.
+- Run optimized model inference on video
+
+
+## Installation Instructions
+
+This is a self-contained example that relies solely on its own code.</br>
+We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.
+For details, please refer to [Installation Guide](../../README.md).