Upload weights - GeoFractalDavid-Basin-k4 - Run 20251015_230643 - Acc 80.51%

weights/GeoFractalDavid-Basin-k4/20251015_230643/README.md

@@ -23,7 +23,7 @@ model-index:
       type: imagenet-1k
     metrics:
     - type: accuracy
-      value: 79.00
+      value: 80.51
       name: Validation Accuracy
 ---
 
@@ -34,18 +34,18 @@ Features must "fit" geometric signatures: k-simplex shapes, Cantor positions, an
 
 ## 🎯 Performance
 
-- **Best Validation Accuracy**: 79.00%
-- **Epoch**: 2/10
-- **Training Time**: 5m
+- **Best Validation Accuracy**: 80.51%
+- **Epoch**: 4/10
+- **Training Time**: 10m
 
 ### Per-Scale Performance
-- **Scale 576D**: 78.33%
-- **Scale 640D**: 78.40%
-- **Scale 704D**: 78.29%
-- **Scale 768D**: 78.44%
-- **Scale 832D**: 78.29%
-- **Scale 896D**: 77.50%
-- **Scale 960D**: 76.88%
+- **Scale 576D**: 76.82%
+- **Scale 640D**: 76.96%
+- **Scale 704D**: 76.89%
+- **Scale 768D**: 79.47%
+- **Scale 832D**: 76.11%
+- **Scale 896D**: 70.58%
+- **Scale 960D**: 68.24%
 
 
 ## 🏗️ Architecture
@@ -73,8 +73,8 @@ Each scale learns to weight these components differently.
 The alpha parameter controls middle-interval weighting in the Cantor staircase.
 
 - **Initial**: 0.4817
-- **Final**: 0.4149
-- **Change**: -0.0668
+- **Final**: 0.3009
+- **Change**: -0.1808
 - **Converged to 0.5**: False
 
 The Cantor staircase uses soft triadic decomposition with learnable alpha to map
@@ -86,39 +86,39 @@ Each class has a learned scalar Cantor prototype. The model pulls features towar
 their class's Cantor position.
 
 **Scale 576D**:
-- Mean: 0.4246
-- Std: 0.1212
-- Range: [0.0008, 0.5805]
+- Mean: 0.3247
+- Std: 0.1574
+- Range: [-0.0008, 0.5011]
 
 **Scale 640D**:
-- Mean: 0.4246
-- Std: 0.1212
-- Range: [0.0011, 0.5805]
+- Mean: 0.3242
+- Std: 0.1579
+- Range: [-0.0004, 0.5008]
 
 **Scale 704D**:
-- Mean: 0.4256
-- Std: 0.1205
-- Range: [0.0018, 0.5809]
+- Mean: 0.3246
+- Std: 0.1577
+- Range: [-0.0007, 0.5009]
 
 **Scale 768D**:
-- Mean: 0.4255
-- Std: 0.1118
-- Range: [0.0306, 0.5832]
+- Mean: 0.3301
+- Std: 0.1542
+- Range: [-0.0007, 0.5011]
 
 **Scale 832D**:
-- Mean: 0.4254
-- Std: 0.1205
-- Range: [0.0013, 0.5810]
+- Mean: 0.3249
+- Std: 0.1574
+- Range: [-0.0006, 0.5012]
 
 **Scale 896D**:
-- Mean: 0.4243
-- Std: 0.1170
-- Range: [0.0052, 0.5829]
+- Mean: 0.3270
+- Std: 0.1561
+- Range: [-0.0007, 0.5013]
 
 **Scale 960D**:
-- Mean: 0.4252
-- Std: 0.1174
-- Range: [0.0020, 0.5841]
+- Mean: 0.3274
+- Std: 0.1560
+- Range: [-0.0004, 0.5011]
 
 
 Most classes cluster around 0.5 (middle Cantor region), with smooth spread across [0,1].
@@ -128,13 +128,13 @@ This creates a continuous manifold rather than discrete bins.
 
 Each scale learns optimal weights for combining geometric components:
 
-**Scale 576D**: Feature=0.681, Cantor=0.073, Crystal=0.246
-**Scale 640D**: Feature=0.659, Cantor=0.075, Crystal=0.266
-**Scale 704D**: Feature=0.625, Cantor=0.073, Crystal=0.302
-**Scale 768D**: Feature=0.849, Cantor=0.028, Crystal=0.123
-**Scale 832D**: Feature=0.632, Cantor=0.061, Crystal=0.307
-**Scale 896D**: Feature=0.742, Cantor=0.038, Crystal=0.220
-**Scale 960D**: Feature=0.688, Cantor=0.041, Crystal=0.271
+**Scale 576D**: Feature=0.729, Cantor=0.046, Crystal=0.225
+**Scale 640D**: Feature=0.695, Cantor=0.048, Crystal=0.257
+**Scale 704D**: Feature=0.637, Cantor=0.049, Crystal=0.314
+**Scale 768D**: Feature=0.957, Cantor=0.005, Crystal=0.037
+**Scale 832D**: Feature=0.634, Cantor=0.041, Crystal=0.325
+**Scale 896D**: Feature=0.794, Cantor=0.011, Crystal=0.195
+**Scale 960D**: Feature=0.643, Cantor=0.017, Crystal=0.340
 
 
 **Pattern**: Lower scales rely on feature similarity, higher scales use crystal geometry.

weights/GeoFractalDavid-Basin-k4/20251015_230643/model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:4245eb6954d05b5b524dcde0e7b2af05bcf65c85d9ba7b11a83294cf4b141456
 size 174686092

weights/GeoFractalDavid-Basin-k4/20251015_230643/model_metadata.json

@@ -1,21 +1,21 @@
 {
-  "epoch": 1,
+  "epoch": 3,
   "metrics": {
-    "val_acc": 79.002,
-    "train_acc": 80.24426167704912,
+    "val_acc": 80.514,
+    "train_acc": 81.97502745543711,
     "scale_accuracies": {
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-      "640": 78.396,
-      "704": 78.294,
-      "768": 78.442,
-      "832": 78.292,
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-      "960": 76.88
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+      "640": 76.962,
+      "704": 76.888,
+      "768": 79.468,
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+      "896": 70.584,
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-    "best_val_acc": 79.002,
-    "best_epoch": 1,
-    "final_train_acc": 80.24426167704912,
-    "training_time": "5m"
+    "best_val_acc": 80.514,
+    "best_epoch": 3,
+    "final_train_acc": 81.97502745543711,
+    "training_time": "10m"
   },
   "config": {
     "name": "geofractal_david_basin",
@@ -72,126 +72,136 @@
     "alpha_summary": {
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weights/GeoFractalDavid-Basin-k4/20251015_230643/training_history.json

@@ -2,91 +2,133 @@
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 }