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| title: Illumination-Invariant Fish Classification | |
| emoji: 🐟 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| python_version: "3.10" | |
| app_file: app.py | |
| pinned: false | |
| # Illumination-Invariant Identification of Wild and Farmed Larimichthys crocea | |
| via Cross-Modal 3D Shape Priors | |
| ## Overview | |
| This Space demonstrates an illumination-invariant fish classification system that identifies wild and farmed Larimichthys crocea (yellow croaker) using cross-modal 3D shape priors. | |
| ## Features | |
| - **Multi-modal Fusion**: Integrates RGB and depth information for enhanced feature representation | |
| - **Shape-Guided Attention**: Leverages depth-estimated shape information to guide RGB feature learning | |
| - **Real-time Inference**: Fast classification with pre-trained models | |
| - **Grad-CAM Visualization**: Visualize model attention with heatmaps | |
| ## Usage | |
| Upload an image of a fish, and the system will: | |
| 1. Detect if it's a yellow croaker (confidence threshold: 0.5) | |
| 2. If detected, classify it as Wild or Farmed | |
| 3. Display detection mask and Grad-CAM heatmap for interpretability | |
| ## Model Architecture | |
| - **Backbone**: ResNet50 | |
| - **Depth Decoder**: UNet-style decoder with attention gates | |
| - **Classification Head**: Fully connected layers with dropout | |
| ## Citation | |
| If you use this code in your research, please cite: | |