Update README.md

README.md

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-license: apache-2.0
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+Pothole Detection using YOLOv8
+
+Overview
+
+This project implements a pothole detection system using the YOLOv8 object detection model. The system is designed to identify potholes in road images or video streams, providing an efficient solution for road maintenance and safety monitoring.
+
+Features
+
+Uses YOLOv8 for real-time pothole detection.
+
+Can process both images and video streams.
+
+Provides bounding box annotations for detected potholes.
+
+Supports model fine-tuning with custom datasets.
+
+Dataset
+
+The dataset consists of images containing potholes along with labeled annotations in YOLO format. The dataset is structured as follows:
+
+Dataset/
+  ├── images/
+  │   ├── train/
+  │   ├── val/
+  │   ├── test/
+  ├── labels/
+  │   ├── train/
+  │   ├── val/
+  │   ├── test/
+  ├── data.yaml
+
+Installation
+
+Clone the repository:
+
+git clone https://github.com/yourusername/pothole-detection-yolov8.git
+cd pothole-detection-yolov8
+
+Install dependencies:
+
+pip install ultralytics opencv-python numpy torch torchvision
+
+Download or prepare your dataset and place it in the Dataset/ folder.
+
+Training the Model
+
+To train YOLOv8 on the custom pothole dataset:
+
+!yolo task=detect mode=train model=yolov8n.pt data=Dataset/data.yaml epochs=50 imgsz=640
+
+Running Inference
+
+For image inference:
+
+!yolo task=detect mode=predict model=best.pt source=images/test.jpg
+
+For video inference:
+
+!yolo task=detect mode=predict model=best.pt source=video.mp4
+
+Results
+
+The trained model will generate detection results with bounding boxes around potholes. The inference images and videos will be saved in the runs/detect/ directory.
+
+Future Improvements
+
+Improve model accuracy with additional training data.
+
+Deploy the model on an edge device for real-time detection.
+
+Integrate a reporting system for detected potholes.
+
+License
+
+This project is licensed under the apache 2.0 License.
+