sandy-1202/Road_Hazard_Detection

title: YOLOv8 Road Safety Detector (Streamlit/Docker) emoji: 🚧 colorFrom: red colorTo: yellow sdk: docker # This is the crucial line specifying a Docker Space app_port: 7860 # This must match the port set in your run.sh file ---

🚧 YOLOv8 Road Safety Object Detection App

This Hugging Face Space hosts a Streamlit application deployed via Docker.

The application uses a YOLOv8 model, trained on your custom dataset (detecting Animals, Garbage, Manholes, and Potholes), to perform object detection on uploaded images.

🛠️ App Details

• —Model: YOLOv8 (weights file: best.pt)
• —Framework: PyTorch/Ultralytics
• —Frontend: Streamlit
• —Deployment: Docker

How to use

1. 1.Upload an image of a road with a potential issue (e.g., a pothole, or garbage).
2. 2.Click the "Run Detection" button.
3. 3.The model will process the image and display the results with bounding boxes around the detected objects.