Added yolo detection notebook

notebooks/yolo-detection-classification/README.md

@@ -0,0 +1,42 @@
+# YOLO Detection & Classification with Device Selection
+
+This notebook demonstrates how to build an object detection and classification pipline using a YOLO model with OpenVINO™. It also highlights how to select and run inference on different hardware devices.
+
+![Sample detection result](grocery_detect.jpg)
+
+## Notebook Contents
+
+The notebook is organized into the following sections:
+
+1. **Environment Setup**  
+   Verify installation of OpenVINO and required packages.
+
+2. **Basic Inference**  
+   - Download a YOLO model for object detection.  
+   - Run detection without OpenVINO  
+
+
+3. **OpenVINO Inferenece**  
+   - Convert to OpenVINO format
+   - Run on selected devices - CPU, GPU, NPU
+
+4. **Crop Images**  
+   - Extract detected objects as single images
+
+5. **Classification of Single Images**  
+   - Run classification on cropped images  
+
+6. **Create full pipeline**  
+   - Combine Detection, Cropping, Classification into single pipeline
+
+7. **Compare Performance**
+   - Compare performance for pipeline for CPU and GPU inference  
+
+
+## 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).
+
+> **Note:** After installing `ipywidgets` and `jupyterlab_widgets`, you may need to **restart the Jupyter server** (not just the kernel) to enable widget rendering in JupyterLab.

notebooks/yolov10-optimization/yolov10-optimization.ipynb

@@ -1494,7 +1494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.13.6"
   },
   "openvino_notebooks": {
    "imageUrl": "https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/81ff3233-9c8d-4fe8-ab21-baf9ce530cff",