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AbstractPhil/
david-shared-space

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David: Multi-Scale Feature Classifier

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

This version is using multiple variations of clip-vit inputs simultaneously into shared space. The experiment will determine if entirely deviant variations such as clip-vit-b-patch32 and patch16 can exist simultaneously in the same shared space with the correct checks and spacings applied.

Model Details

Architecture

Preset: gated_expert_team
Sharing Mode: decoupled
Fusion Mode: deep_efficiency
Scales: [128, 256, 384, 448, 512, 576, 640, 768, 896]
Feature Dim: 512
Parameters: 22,133,801

Training Configuration

Dataset: AbstractPhil/imagenet-clip-features-orderly
Model Variant: ['clip_vit_b16', 'clip_vit_laion_b32', 'clip_vit_b32']
Epochs: 10
Batch Size: 1024
Learning Rate: 0.01
Rose Loss Weight: 0.1 → 0.8
Cayley Loss: False

Performance

Best Results

Validation Accuracy: 62.94%
Best Epoch: 9
Final Train Accuracy: 61.07%

Per-Scale Performance

Scale 128: 62.94%
Scale 256: 71.08%
Scale 384: 73.44%
Scale 448: 74.29%
Scale 512: 74.61%
Scale 576: 75.04%
Scale 640: 75.18%
Scale 768: 75.58%
Scale 896: 75.90%

Usage

Quick Model Lookup

Check MODELS_INDEX.json in the repo root - it lists all trained models sorted by accuracy with links to weights and configs.

Repository Structure

AbstractPhil/david-shared-space/
├── MODELS_INDEX.json                # 📊 Master index of all models (sorted by accuracy)
├── README.md                         # This file
├── best_model.json                   # Latest best model info
├── weights/
│   └── david_gated_expert_team/
│       └── 20251013_004438/
│           ├── MODEL_SUMMARY.txt     # 🎯 Human-readable performance summary
│           ├── training_history.json # 📈 Epoch-by-epoch training curve
│           ├── best_model_acc62.94.safetensors  # ⭐ Accuracy in filename!
│           ├── best_model_acc62.94_metadata.json
│           ├── final_model.safetensors
│           ├── checkpoint_epoch_X_accYY.YY.safetensors
│           ├── david_config.json
│           └── train_config.json
└── runs/
    └── david_gated_expert_team/
        └── 20251013_004438/
            └── events.out.tfevents.* # TensorBoard logs

Loading the Model

python

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

# Browse available models in MODELS_INDEX.json first!

# Specify model variant and run
model_name = "david_gated_expert_team"
run_id = "20251013_004438"
accuracy = "62.94"  # From MODELS_INDEX.json

# Download config
config_path = hf_hub_download(
    repo_id="AbstractPhil/david-shared-space", 
    filename=f"weights/{model_name}/{run_id}/david_config.json"
)
config = DavidArchitectureConfig.from_json(config_path)

# Download weights (accuracy in filename!)
weights_path = hf_hub_download(
    repo_id="AbstractPhil/david-shared-space", 
    filename=f"weights/{model_name}/{run_id}/best_model_acc{accuracy}.safetensors"
)

# Download training history (optional - see full training curve)
history_path = hf_hub_download(
    repo_id="AbstractPhil/david-shared-space", 
    filename=f"weights/{model_name}/{run_id}/training_history.json"
)

# Load model
from safetensors.torch import load_file
david = David.from_config(config)
david.load_state_dict(load_file(weights_path))
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 (128, 256, 384, 448, 512, 576, 640, 768, 896), allowing it to capture both coarse and fine-grained features.

Shared Representation Space

This variation shares multiple versions of clip-vit models in the same representation space.

Feature 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→0.8)
Cayley Loss: Geometric regularization (disabled)

Optimization

Optimizer: AdamW
Weight Decay: 1e-05
Scheduler: cosine_restarts
Gradient Clip: 10.0
Mixed Precision: False

Citation

bibtex

@software{david_classifier_2025,
  title = {David: Multi-Scale Feature Classifier},
  author = {AbstractPhil},
  year = {2025},
  url = {https://huggingface.co/AbstractPhil/david-shared-space},
  note = {Run ID: 20251013_004438}
}

License

MIT License

Acknowledgments

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

Generated on 2025-10-13 01:33:26