Update README.md

README.md

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-license: mit
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+---
+license: mit
+language:
+- ja
+pipeline_tag: image-to-text
+---
+# Steganography Neural Network Model Card
+
+## Model Overview
+**Task:** Image Steganography (Message Embedding and Extraction)  
+**Architecture Type:** Encoder-Decoder Neural Network  
+**Primary Use Case:** Embedding and recovering hidden messages in images  
+**Developer:** Not specified  
+**License:** Not specified  
+
+## Technical Specifications
+- **Parameters:** 980,548
+- **Model Size:** 3.74 MB
+- **Precision:** torch.float32
+- **FLOPs:** 1,954,176
+- **Input Resolution:** 512 × 512 pixels
+- **Framework:** PyTorch
+
+## Architecture Details
+
+### Encoder Network
+- **Input:** 4 channels (RGB + message), 512×512px
+- **Output:** 3 channels (RGB stego image), 512×512px
+- **Key Components:**
+  - Initial Conv (4→64 channels)
+  - Backbone with SE blocks and dilated convolutions
+  - Residual connections
+  - Final weighted combination (0.9 × original + 0.1 × encoded)
+
+### Decoder Network
+- **Input:** 3 channels (stego image), 512×512px
+- **Output:** 1 channel (recovered message), 512×512px
+- **Key Components:**
+  - Feature extraction (3→64→128 channels)
+  - SE blocks and residual connections
+  - Message extraction pathway
+
+## Training Details
+- **Hardware:** GTX 1080 GPU
+- **Epochs:** 600
+- **Optimizer:** AdamW (lr=0.001, weight_decay=0.01)
+- **Scheduler:** Cosine Annealing (min_lr=1e-6)
+- **Loss Functions:**
+  - Image Loss: 0.95×MSE + 0.05×(1-SSIM)
+  - Message Loss: MSE
+  - Combined with dynamic alpha weighting
+
+## Key Features
+- Group Normalization for batch-size independence
+- SiLU activation functions throughout
+- Squeeze-and-Excitation blocks for channel attention
+- Dilated convolutions in encoder
+- Skip connections for detail preservation
+
+## Performance Characteristics
+- Maintains visual image quality while embedding messages
+- Optimized for both image fidelity and message recovery
+- Lightweight architecture (<1M parameters)
+
+## Limitations and Biases
+- Fixed input resolution of 512×512 pixels
+
+
+## Technical Requirements
+- PyTorch environment
+- GPU recommended for optimal performance
+- Standard deep learning dependencies
+- Sufficient memory for 3.74 MB model
+
+## Citation and Contact
+- Model source and citation information not provided
+- Contact information for maintainers not specified