🫘💎 DavidBeans: Unified Vision-to-Crystal Architecture

This repository contains training runs for DavidBeans - a unified geometric deep learning architecture combining:

• BEANS (ViT Backbone): Cantor-routed sparse attention
• DAVID (Classifier): Multi-scale crystal projection with Cayley-Menger geometric regularization

Repository Structure

AbstractPhil/geovit-david-beans/
├── README.md (this file)
└── weights/
    ├── run_001_baseline_YYYYMMDD_HHMMSS/
    │   ├── best.safetensors
    │   ├── epoch_010.safetensors
    │   ├── config.json
    │   ├── training_config.json
    │   └── tensorboard/
    ├── run_002_5expert_5scale_YYYYMMDD_HHMMSS/
    │   └── ...
    └── ...

Usage

from safetensors.torch import load_file
from david_beans import DavidBeans, DavidBeansConfig
import json

# Pick a run
run_path = "weights/run_002_5expert_5scale_20251129_171229"

# Load config
with open(f"{run_path}/config.json") as f:
    config_dict = json.load(f)
config = DavidBeansConfig(**config_dict)

# Load model
model = DavidBeans(config)
state_dict = load_file(f"{run_path}/best.safetensors")
model.load_state_dict(state_dict)

# Inference
model.eval()
with torch.no_grad():
    output = model(images)
    predictions = output['logits'].argmax(dim=-1)

Training Runs

Architecture

Image [B, 3, 32, 32]
       │
       ▼
┌─────────────────────────────────────────┐
│  BEANS BACKBONE                         │
│  ├─ Patch Embed → [64 patches, dim]     │
│  ├─ Hybrid Cantor Router                │
│  ├─ N × Attention Blocks                │
│  └─ N × Pentachoron Expert Layers       │
└─────────────────────────────────────────┘
       │
       ▼
┌─────────────────────────────────────────┐
│  DAVID HEAD                             │
│  ├─ Multi-scale projection              │
│  ├─ Per-scale Crystal Heads             │
│  └─ Geometric Fusion                    │
└─────────────────────────────────────────┘
       │
       ▼
    [num_classes]

License

Apache 2.0