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