# Fashion-MNIST Benchmark Results
## Optical-Mycelial Neural Network

### Model Architecture
- **Type**: Optical-Evolutionary Neural Network
- **Technology**: C++/CUDA implementation
- **Novel Features**:
  - Optical field modulation with FFT processing
  - Evolutionary mycelial (fungi) masks
  - Dynamic amplitude and phase transformations

### Training Configuration
- **Dataset**: Fashion-MNIST (28×28 grayscale images, 10 classes)
- **Training samples**: 60,000
- **Test samples**: 10,000
- **Epochs**: 10
- **Batch size**: 256
- **Learning rate**: 1e-3
- **Fungi count**: 128
- **Optimizer**: Adam

### Results
**Best Test Accuracy: 81.94%** (achieved at epoch 9)

#### Per-Epoch Results:
| Epoch | Test Accuracy |
|-------|---------------|
| 1     | 78.11%       |
| 2     | 79.61%       |
| 3     | 80.56%       |
| 4     | 80.86%       |
| 5     | 81.03%       |
| 6     | 81.01%       |
| 7     | 81.57%       |
| 8     | 80.73%       |
| 9     | **81.94%**   |
| 10    | 81.69%       |

### Technical Details
- **Loss Function**: Softmax Cross-Entropy
- **Data Format**: Binary float32 images, uint8 labels
- **Hardware**: NVIDIA GPU (CUDA 13.0)
- **Compiler**: Visual Studio 2022 + NVCC

### Model Innovation
This represents the first application of optical-evolutionary neural networks to Fashion-MNIST classification, demonstrating the potential of bio-inspired optical computing architectures for image classification tasks.

### Code Availability
Complete C++/CUDA source code available at: [Repository URL]

---
*Generated with Optical-Evolutionary Neural Network Technology*
*Date: September 17, 2025*