| # 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* |