| title: Landslide Detection Models | |
| emoji: 🌍 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| app_port: 8501 | |
| tags: | |
| - streamlit | |
| - pytorch | |
| - deep-learning | |
| - landslide-detection | |
| pinned: false | |
| short_description: Landslide detection using various deep learning models | |
| license: apache-2.0 | |
| # Landslide Detection Models Demo | |
| This Space demonstrates various deep learning models for landslide detection, using models trained with PyTorch. The models are served directly from our [Kaggle Models Repository](https://www.kaggle.com/models/harshshinde8/sims/). | |
| ## Available Models | |
| - DeepLabV3+ | |
| - DenseNet121 | |
| - EfficientNetB0 | |
| - InceptionResNetV2 | |
| - InceptionV4 | |
| - MiT-B1 | |
| - MobileNetV2 | |
| - ResNet34 | |
| - ResNeXt50_32X4D | |
| - SE-ResNet50 | |
| - SE-ResNeXt50_32X4D | |
| - SegFormer | |
| - VGG16 | |
| ## How to Use | |
| 1. Select a model from the sidebar | |
| 2. Upload one or more `.h5` files containing satellite imagery | |
| 3. View the landslide detection results and predictions | |
| 4. Download the results if needed | |
| ## Model Information | |
| All models are trained on satellite imagery data and are optimized for landslide detection. Each model has its own strengths and characteristics, which are described in the app interface when you select them. | |
| ## Technical Details | |
| - Python 3.9 | |
| - PyTorch 1.9.0 | |
| - Streamlit 1.28.0 | |
| - Models are automatically downloaded from Kaggle when needed | |
| ## Author | |
| - Harsh Shinde | |