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| title: Road Surface Classification | |
| emoji: 🌍 | |
| colorFrom: yellow | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 5.33.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Road Surface Classification | |
| This application classifies road surface conditions into four categories: | |
| - Good Condition | |
| - Potholes | |
| - Cracked Road | |
| - Flooded/Muddy | |
| ## Models | |
| Multiple model architectures are implemented and compared, including: | |
| - EfficientNet B0 | |
| - MobileNet V2 | |
| - ResNet-18 | |
| - Random Forest (using features extracted from CNNs) | |
| ## How to Use | |
| 1. Upload a road image | |
| 2. Select which model to use (EfficientNet, MobileNet, ResNet, or Random Forest) | |
| 3. Click "Classify" to get the prediction and confidence scores |