README.md

---
language: en
license: mit
tags:
- image-classification
- imagenet
- multi-scale
- crystal-geometry
- david
datasets:
- imagenet-1k
metrics:
- accuracy
model-index:
- name: David-decoupled-deep_efficiency
  results:
  - task:
      type: image-classification
    dataset:
      name: ImageNet-1K
      type: imagenet-1k
    metrics:
    - type: accuracy
      value: 72.36
---

# David: Multi-Scale Crystal Classifier

**David** is a multi-scale deep learning classifier that uses crystal geometry (pentachora/4-simplexes) 
as class prototypes with role-weighted similarity computation (Rose Loss).

## Model Details

### Architecture
- **Preset**: high_accuracy
- **Sharing Mode**: decoupled
- **Fusion Mode**: deep_efficiency
- **Scales**: [256, 512, 768, 1024, 1280]
- **Feature Dim**: 512
- **Parameters**: ~8.8M

### Training Configuration
- **Dataset**: AbstractPhil/imagenet-clip-features-orderly
- **Model Variant**: clip_vit_b16
- **Epochs**: 20
- **Batch Size**: 1024
- **Learning Rate**: 0.01
- **Rose Loss Weight**: 0.1 → 0.5
- **Cayley Loss**: False

## Performance

### Best Results
- **Validation Accuracy**: 72.36%
- **Best Epoch**: 0
- **Final Train Accuracy**: 66.25%

### Per-Scale Performance
- **Scale 256**: 72.36%


## Usage

### Repository Structure

```
AbstractPhil/gated-david/
├── weights/
│   ├── best_model.pth              # Best model weights (PyTorch)
│   ├── best_model.safetensors      # Best model weights (SafeTensors)
│   ├── best_model_metadata.json    # Training metadata
│   ├── final_model.pth             # Final epoch weights
│   ├── final_model.safetensors
│   ├── david_config.json           # Model architecture config
│   └── train_config.json           # Training configuration
├── runs/
│   └── events.out.tfevents.*       # TensorBoard logs
├── README.md                        # This file
└── best_model.json                 # Performance summary
```

### Loading the Model

```python
from geovocab2.train.model.core.david import David, DavidArchitectureConfig
from huggingface_hub import hf_hub_download

# Download config
config_path = hf_hub_download(repo_id="AbstractPhil/gated-david", 
                               filename="weights/david_config.json")
config = DavidArchitectureConfig.from_json(config_path)

# Download weights
weights_path = hf_hub_download(repo_id="AbstractPhil/gated-david", 
                                filename="weights/best_model.pth")

# Initialize model
david = David.from_config(config)
checkpoint = torch.load(weights_path)
david.load_state_dict(checkpoint['model_state_dict'])
david.eval()
```

### Inference

```python
import torch
import torch.nn.functional as F

# Assuming you have CLIP features (512-dim for ViT-B/16)
features = get_clip_features(image)  # [1, 512]

# Load anchors
anchors_dict = torch.load("anchors.pth")

# Forward pass
with torch.no_grad():
    logits, _ = david(features, anchors_dict)
    predictions = logits.argmax(dim=-1)
```

## Architecture Overview

### Multi-Scale Processing
David processes inputs at multiple scales (256, 512, 768, 1024, 1280), 
allowing it to capture both coarse and fine-grained features.

### Crystal Geometry
Each class is represented by a pentachoron (4-simplex) in embedding space with 5 vertices:
- **Anchor**: Primary class representative
- **Need**: Complementary direction
- **Relation**: Contextual alignment
- **Purpose**: Functional direction
- **Observer**: Meta-perspective

### Rose Loss
Similarity computation uses role-weighted cosine similarities:
```
score = w_anchor * sim(z, anchor) + w_need * sim(z, need) + ...
```

### Fusion Strategy
**deep_efficiency**: Intelligently combines predictions from multiple scales.

## Training Details

### Loss Components
- **Cross-Entropy**: Standard classification loss
- **Rose Loss**: Pentachora role-weighted margin loss (weight: 0.1→0.5)
- **Cayley Loss**: Geometric regularization (disabled)

### Optimization
- **Optimizer**: AdamW
- **Weight Decay**: 1e-05
- **Scheduler**: cosine_restarts
- **Gradient Clip**: 5.0
- **Mixed Precision**: False

## Citation

```bibtex
@software{david_classifier_2025,
  title = {David: Multi-Scale Crystal Classifier},
  author = {AbstractPhil},
  year = {2025},
  url = {https://huggingface.co/AbstractPhil/gated-david},
  note = {Run ID: 20251012_032356}
}
```

## License

MIT License

## Acknowledgments

Built with crystal lattice geometry and multi-scale deep learning.
Special thanks to Claude (Anthropic) for debugging assistance.

---

*Generated on 2025-10-12 03:25:36*