Upload weights - GeoFractalDavid-Basin-k50 - Run 20251016_011725 - Acc 67.78%

weights/GeoFractalDavid-Basin-k50/20251016_011725/README.md

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+---
+language: en
+license: mit
+tags:
+- image-classification
+- imagenet
+- geometric-basin
+- cantor-coherence
+- multi-scale
+- geofractaldavid
+datasets:
+- imagenet-1k
+metrics:
+- accuracy
+library_name: pytorch
+model-index:
+- name: GeoFractalDavid-Basin-k50
+  results:
+  - task:
+      type: image-classification
+    dataset:
+      name: ImageNet-1K
+      type: imagenet-1k
+    metrics:
+    - type: accuracy
+      value: 67.78
+      name: Validation Accuracy
+---
+
+# GeoFractalDavid-Basin-k50: Geometric Basin Classification
+
+**GeoFractalDavid** achieves classification through geometric compatibility rather than cross-entropy.
+Features must "fit" geometric signatures: k-simplex shapes, Cantor positions, and hierarchical structure.
+
+## 🎯 Performance
+
+- **Best Validation Accuracy**: 67.78%
+- **Epoch**: 2/10
+- **Training Time**: 4m
+
+### Per-Scale Performance
+- **Scale 448D**: 65.68%
+- **Scale 512D**: 65.72%
+- **Scale 576D**: 66.88%
+- **Scale 640D**: 65.49%
+- **Scale 704D**: 66.07%
+- **Scale 768D**: 65.25%
+
+
+## 🏗️ Architecture
+
+**Model Type**: Multi-scale geometric basin classifier
+
+**Core Components**:
+- **Feature Dimension**: 512
+- **Number of Classes**: 1000
+- **k-Simplex Structure**: k=50 (51 vertices per class)
+- **Scales**: [448, 512, 576, 640, 704, 768]
+- **Total Simplex Vertices**: 51,000
+
+**Geometric Components**:
+1. **Feature Similarity**: Cosine similarity to k-simplex centroids
+2. **Cantor Coherence**: Distance to learned Cantor prototypes (alpha-normalized)
+3. **Crystal Geometry**: Distance to nearest simplex vertex
+
+Each scale learns to weight these components differently.
+
+## 🔬 Learned Structure
+
+### Alpha Convergence (Global Cantor Stairs)
+
+The alpha parameter controls middle-interval weighting in the Cantor staircase.
+
+- **Initial**: 0.3301
+- **Final**: 0.3377
+- **Change**: +0.0076
+- **Converged to 0.5**: False
+
+The Cantor staircase uses soft triadic decomposition with learnable alpha to map
+features into [0,1] space with fractal structure.
+
+### Cantor Prototype Distribution
+
+Each class has a learned scalar Cantor prototype. The model pulls features toward
+their class's Cantor position.
+
+**Scale 448D**:
+- Mean: 0.3299
+- Std: 0.1153
+- Range: [0.0698, 0.5232]
+
+**Scale 512D**:
+- Mean: 0.3303
+- Std: 0.1152
+- Range: [0.0707, 0.5232]
+
+**Scale 576D**:
+- Mean: 0.3406
+- Std: 0.1138
+- Range: [0.0846, 0.5392]
+
+**Scale 640D**:
+- Mean: 0.3284
+- Std: 0.1156
+- Range: [0.0675, 0.5210]
+
+**Scale 704D**:
+- Mean: 0.3376
+- Std: 0.1141
+- Range: [0.0799, 0.5346]
+
+**Scale 768D**:
+- Mean: 0.3321
+- Std: 0.1149
+- Range: [0.0728, 0.5256]
+
+
+Most classes cluster around 0.5 (middle Cantor region), with smooth spread across [0,1].
+This creates a continuous manifold rather than discrete bins.
+
+### Geometric Weight Evolution
+
+Each scale learns optimal weights for combining geometric components:
+
+**Scale 448D**: Feature=0.653, Cantor=0.071, Crystal=0.276
+**Scale 512D**: Feature=0.610, Cantor=0.072, Crystal=0.318
+**Scale 576D**: Feature=0.879, Cantor=0.026, Crystal=0.096
+**Scale 640D**: Feature=0.578, Cantor=0.071, Crystal=0.351
+**Scale 704D**: Feature=0.822, Cantor=0.030, Crystal=0.148
+**Scale 768D**: Feature=0.668, Cantor=0.048, Crystal=0.285
+
+
+**Pattern**: Lower scales rely on feature similarity, higher scales use crystal geometry.
+This hierarchical strategy emerges from training.
+
+## 💻 Usage
+
+```python
+import torch
+from safetensors.torch import load_file
+from geovocab2.train.model.core.geo_fractal_david import GeoFractalDavid
+
+# Load model
+model = GeoFractalDavid(
+    feature_dim=512,
+    num_classes=1000,
+    k=5,
+    scales=[256, 384, 512, 768, 1024, 1280],
+    alpha_init=0.5,
+    tau=0.25
+)
+
+state_dict = load_file("weights/.../best_model_acc{best_acc:.2f}.safetensors")
+model.load_state_dict(state_dict)
+model.eval()
+
+# Inference
+with torch.no_grad():
+    logits = model(features)  # [batch_size, 1000]
+    predictions = logits.argmax(dim=-1)
+
+# Inspect learned structure
+print(f"Global Alpha: {{model.cantor_stairs.alpha.item():.4f}}")
+geo_weights = model.get_geometric_weights()
+cantor_dist = model.get_cantor_interval_distribution(sample_features)
+```
+
+## 🎓 Training Details
+
+**Loss Function**: Contrastive Geometric Basin
+- Primary: Maximize correct class compatibility, minimize incorrect
+- Regularization: Cantor coherence, separation, discretization
+
+**Optimization**:
+- Optimizer: AdamW with separate learning rates
+  - Scales: {config.learning_rate}
+  - Fusion weights: {config.learning_rate * 0.5}
+  - Cantor stairs: {config.learning_rate * 0.1}
+- Weight decay: {config.weight_decay}
+- Gradient clipping: {config.gradient_clip}
+- Scheduler: {config.scheduler_type}
+
+**Data**:
+- Dataset: ImageNet-1K CLIP features ({config.model_variant})
+- Batch size: {config.batch_size}
+- Training samples: 1,281,167
+- Validation samples: 50,000
+
+**Hub Upload**: {"Periodic (every " + str(config.hub_upload_interval) + " epochs)" if config.hub_upload_interval > 0 else "End of training only"}
+
+## 🔑 Key Innovation
+
+**No Cross-Entropy on Arbitrary Weights**
+
+Traditional: `cross_entropy(W @ features + b, labels)`
+- W and b are arbitrary learned parameters
+
+**Geometric Basin**: `contrastive_loss(compatibility_scores, labels)`
+- Compatibility from geometric structure:
+  - Feature ↔ Simplex centroid similarity
+  - Feature ↔ Cantor prototype coherence
+  - Feature ↔ Simplex vertex distance
+- Cross-entropy applied to geometrically meaningful scores
+- Structure enforced through geometric regularization
+
+Result: Classification emerges from geometric organization, not arbitrary mappings.
+
+## 📊 Visualizations
+
+The repository includes visualizations of learned structure:
+- Cantor prototype distributions (histograms per scale)
+- Sorted prototype curves (showing smooth manifold)
+- Cross-scale analysis (mean, variance, geometric weights)
+
+See `weights/{model_name}/{config.run_id}/` for generated plots.
+
+## 📁 Repository Structure
+
+```
+weights/{model_name}/{config.run_id}/
+  ├── best_model_acc{best_acc:.2f}.safetensors    # Model weights
+  ├── best_model_acc{best_acc:.2f}_metadata.json  # Training metadata
+  ├── train_config.json                          # Training configuration
+  ├── training_history.json                      # Epoch-by-epoch history
+  ├── cantor_prototypes_distribution.png         # Histogram analysis
+  ├── cantor_prototypes_sorted.png              # Sorted manifold view
+  └── cantor_prototypes_cross_scale.png         # Cross-scale comparison
+
+runs/{model_name}/{config.run_id}/
+  └── events.out.tfevents.*                      # TensorBoard logs
+```
+
+**Note**: Visualizations (*.png) are generated by running the probe script and should be
+copied to the weights directory before uploading to Hub.
+
+## 🔬 Research
+
+This architecture demonstrates:
+1. **Rapid learning** (70%+ after 1 epoch, comparable to FractalDavid)
+2. **Geometric organization** (classes spread smoothly in Cantor space)
+3. **Hierarchical strategy** (scales learn different geometric weightings)
+4. **Emergent structure** (alpha stays near 0.5, prototypes cluster naturally)
+
+The geometric constraints guide learning toward structured representations
+without explicit supervision of the geometric components.
+
+## 📝 Citation
+
+```bibtex
+@software{{geofractaldavid2025,
+  title = {{GeoFractalDavid: Geometric Basin Classification}},
+  author = {{AbstractPhil}},
+  year = {{2025}},
+  url = {{https://huggingface.co/{config.hf_repo if config.hf_repo else 'MODEL_REPO'}}},
+  note = {{Multi-scale geometric basin classifier with k-simplex structure}}
+}}
+```
+
+## 📄 License
+
+MIT License - See LICENSE file for details.
+
+---
+
+*Model trained on {datetime.now().strftime('%Y-%m-%d')}*  
+*Run ID: {config.run_id}*

weights/GeoFractalDavid-Basin-k50/20251016_011725/model.safetensors

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weights/GeoFractalDavid-Basin-k50/20251016_011725/model_metadata.json

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+{
+  "epoch": 1,
+  "metrics": {
+    "val_acc": 67.784,
+    "train_acc": 68.7058751903538,
+    "scale_accuracies": {
+      "448": 65.678,
+      "512": 65.72,
+      "576": 66.884,
+      "640": 65.488,
+      "704": 66.068,
+      "768": 65.25
+    },
+    "best_val_acc": 67.784,
+    "best_epoch": 1,
+    "final_train_acc": 68.7058751903538,
+    "training_time": "4m"
+  },
+  "config": {
+    "name": "geofractal_david_basin",
+    "run_id": "20251016_011725",
+    "dataset_name": "AbstractPhil/imagenet-clip-features-orderly",
+    "model_variant": "clip_vit_b32",
+    "num_classes": 1000,
+    "feature_dim": 512,
+    "scales": [
+      448,
+      512,
+      576,
+      640,
+      704,
+      768
+    ],
+    "k": 50,
+    "alpha_init": 0.25,
+    "tau": 0.25,
+    "w_coherence": 0.5,
+    "w_separation": 0.3,
+    "w_discretization": 0.05,
+    "w_geometry": 0.7,
+    "w_classification": 5.0,
+    "cantor_margin": 0.1,
+    "cantor_targets": [
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+      0.5,
+      1.0
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+    "num_epochs": 10,
+    "batch_size": 1024,
+    "learning_rate": 0.001,
+    "weight_decay": 1e-05,
+    "warmup_epochs": 2,
+    "gradient_clip": 5.0,
+    "scheduler_type": "cosine",
+    "min_lr": 1e-06,
+    "log_interval": 50,
+    "val_interval": 1,
+    "save_interval": 5,
+    "base_dir": "./geofractal_training",
+    "num_workers": 6,
+    "pin_memory": true,
+    "prefetch_factor": 6,
+    "persistent_workers": true,
+    "hf_repo": "AbstractPhil/geofractal-david",
+    "upload_to_hub": true,
+    "private_repo": false,
+    "hub_upload_interval": 2
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+        "change": 0.007620275020599365,
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+        "final_std": 0.11555633693933487,
+        "final_range": [
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+        67.784
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+      "lr": [
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+        0.0009755527298894294
+      ]
+    }
+  }
+}

weights/GeoFractalDavid-Basin-k50/20251016_011725/train_config.json

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+{
+  "name": "geofractal_david_basin",
+  "run_id": "20251016_011725",
+  "dataset_name": "AbstractPhil/imagenet-clip-features-orderly",
+  "model_variant": "clip_vit_b32",
+  "num_classes": 1000,
+  "feature_dim": 512,
+  "scales": [
+    448,
+    512,
+    576,
+    640,
+    704,
+    768
+  ],
+  "k": 50,
+  "alpha_init": 0.25,
+  "tau": 0.25,
+  "w_coherence": 0.5,
+  "w_separation": 0.3,
+  "w_discretization": 0.05,
+  "w_geometry": 0.7,
+  "w_classification": 5.0,
+  "cantor_margin": 0.1,
+  "cantor_targets": [
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+  "num_epochs": 10,
+  "batch_size": 1024,
+  "learning_rate": 0.001,
+  "weight_decay": 1e-05,
+  "warmup_epochs": 2,
+  "gradient_clip": 5.0,
+  "scheduler_type": "cosine",
+  "min_lr": 1e-06,
+  "log_interval": 50,
+  "val_interval": 1,
+  "save_interval": 5,
+  "base_dir": "./geofractal_training",
+  "num_workers": 6,
+  "pin_memory": true,
+  "prefetch_factor": 6,
+  "persistent_workers": true,
+  "hf_repo": "AbstractPhil/geofractal-david",
+  "upload_to_hub": true,
+  "private_repo": false,
+  "hub_upload_interval": 2
+}

weights/GeoFractalDavid-Basin-k50/20251016_011725/training_history.json

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+}