File size: 4,620 Bytes
7fa9242 6074a3a 7fa9242 6074a3a 7fa9242 6074a3a 7fa9242 6074a3a 7fa9242 6074a3a 7fa9242 6074a3a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | ---
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*
|