Added yolo detection notebook

notebooks/yolo-detection-classification/README.md

@@ -0,0 +1,60 @@
+# Object Detection and Classification Pipeline with YOLO and OpenVINO™
+
+This notebook demonstrates how to build a complete object detection and classification pipeline using YOLO models with OpenVINO™. The pipeline includes object detection using YOLOv11, cropping detected objects, classification using YOLO classification model, and performance comparison across different devices (CPU, GPU, NPU).
+
+![Sample detection result](grocery_detect.jpg)
+
+## Notebook Contents
+
+The notebook is organized into the following sections:
+
+1. **Prerequisites**  
+   Install required packages for the notebook.
+
+2. **Imports**  
+   Import necessary Python libraries and initialize OpenVINO.
+
+3. **Download Models**  
+   Download YOLOv11n for object detection and YOLOv11n (classification variant) for classification.
+
+4. **Basic Inference without OpenVINO**  
+   Run detection using the PyTorch model to establish a baseline.
+
+5. **Convert to OpenVINO Format**  
+   - Convert Detection Model to OpenVINO IR format  
+   - Convert Classification Model to OpenVINO IR format
+
+6. **Select Inference Device**  
+   Choose the hardware device (CPU, GPU, NPU) for inference.
+
+7. **Run Object Detection**  
+   Perform object detection using OpenVINO on the selected device.
+
+8. **Extract Detected Objects**  
+   Crop detected objects from the original image.
+
+9. **Classify Detected Objects**  
+   Run classification on each cropped object using the YOLO classification model.
+
+10. **Complete Pipeline**  
+    Combine detection, cropping, and classification into a single optimized pipeline.
+
+11. **Performance Comparison**  
+    Compare pipeline performance across different device configurations.
+
+## Key Features
+
+- **Device Flexibility**: Run inference on CPU, GPU, or NPU
+- **Model Conversion**: Convert YOLO models to OpenVINO IR format for optimized performance
+- **Complete Pipeline**: Demonstrates end-to-end object detection and classification workflow
+- **Performance Analysis**: Measure and compare inference times across different hardware accelerators
+- **Interactive Widgets**: Use dropdown menus to easily select inference devices
+
+## Installation Instructions
+
+This is a self-contained example that relies solely on its own code.
+
+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).
+
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/yolo-detection-classification/README.md" />