Upload weights - GeoFractalDavid-Basin-k7 - Run 20251016_001155 - Acc 71.14%

weights/GeoFractalDavid-Basin-k7/20251016_001155/README.md

@@ -23,7 +23,7 @@ model-index:
       type: imagenet-1k
     metrics:
     - type: accuracy
-      value: 70.22
+      value: 71.14
       name: Validation Accuracy
 ---
 
@@ -34,18 +34,18 @@ Features must "fit" geometric signatures: k-simplex shapes, Cantor positions, an
 
 ## 🎯 Performance
 
-- **Best Validation Accuracy**: 70.22%
-- **Epoch**: 6/10
-- **Training Time**: 11m
+- **Best Validation Accuracy**: 71.14%
+- **Epoch**: 8/10
+- **Training Time**: 15m
 
 ### Per-Scale Performance
-- **Scale 320D**: 63.85%
-- **Scale 384D**: 63.49%
-- **Scale 448D**: 63.14%
-- **Scale 512D**: 66.34%
-- **Scale 576D**: 62.38%
-- **Scale 640D**: 55.39%
-- **Scale 704D**: 48.11%
+- **Scale 320D**: 61.99%
+- **Scale 384D**: 61.65%
+- **Scale 448D**: 60.79%
+- **Scale 512D**: 67.11%
+- **Scale 576D**: 62.68%
+- **Scale 640D**: 53.92%
+- **Scale 704D**: 49.10%
 
 
 ## 🏗️ 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**: 0.1869
-- **Final**: -0.0273
-- **Change**: -0.2142
+- **Final**: -0.0716
+- **Change**: -0.2585
 - **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 320D**:
-- Mean: 0.0355
-- Std: 0.0788
-- Range: [-0.1229, 0.2003]
+- Mean: 0.0227
+- Std: 0.0776
+- Range: [-0.1331, 0.1908]
 
 **Scale 384D**:
-- Mean: 0.0355
-- Std: 0.0788
-- Range: [-0.1233, 0.2003]
+- Mean: 0.0227
+- Std: 0.0776
+- Range: [-0.1329, 0.1907]
 
 **Scale 448D**:
-- Mean: 0.0355
-- Std: 0.0788
-- Range: [-0.1228, 0.2002]
+- Mean: 0.0226
+- Std: 0.0777
+- Range: [-0.1328, 0.1908]
 
 **Scale 512D**:
-- Mean: 0.0360
-- Std: 0.0788
-- Range: [-0.1219, 0.2003]
+- Mean: 0.0228
+- Std: 0.0776
+- Range: [-0.1330, 0.1908]
 
 **Scale 576D**:
-- Mean: 0.0360
-- Std: 0.0788
-- Range: [-0.1217, 0.2006]
+- Mean: 0.0228
+- Std: 0.0776
+- Range: [-0.1326, 0.1908]
 
 **Scale 640D**:
-- Mean: 0.0357
-- Std: 0.0788
-- Range: [-0.1221, 0.2004]
+- Mean: 0.0227
+- Std: 0.0777
+- Range: [-0.1327, 0.1907]
 
 **Scale 704D**:
-- Mean: 0.0358
-- Std: 0.0788
-- Range: [-0.1222, 0.2002]
+- Mean: 0.0227
+- Std: 0.0776
+- Range: [-0.1327, 0.1907]
 
 
 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 320D**: Feature=0.854, Cantor=0.037, Crystal=0.108
-**Scale 384D**: Feature=0.828, Cantor=0.040, Crystal=0.132
-**Scale 448D**: Feature=0.765, Cantor=0.044, Crystal=0.191
-**Scale 512D**: Feature=0.991, Cantor=0.002, Crystal=0.007
-**Scale 576D**: Feature=0.980, Cantor=0.004, Crystal=0.016
-**Scale 640D**: Feature=0.902, Cantor=0.009, Crystal=0.089
-**Scale 704D**: Feature=0.560, Cantor=0.005, Crystal=0.435
+**Scale 320D**: Feature=0.884, Cantor=0.029, Crystal=0.087
+**Scale 384D**: Feature=0.860, Cantor=0.031, Crystal=0.109
+**Scale 448D**: Feature=0.797, Cantor=0.036, Crystal=0.167
+**Scale 512D**: Feature=0.995, Cantor=0.001, Crystal=0.004
+**Scale 576D**: Feature=0.990, Cantor=0.002, Crystal=0.008
+**Scale 640D**: Feature=0.942, Cantor=0.006, Crystal=0.052
+**Scale 704D**: Feature=0.523, Cantor=0.003, Crystal=0.475
 
 
 **Pattern**: Lower scales rely on feature similarity, higher scales use crystal geometry.

weights/GeoFractalDavid-Basin-k7/20251016_001155/model.safetensors

@@ -1,3 +1,3 @@
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 size 145102700

weights/GeoFractalDavid-Basin-k7/20251016_001155/model_metadata.json

@@ -1,21 +1,21 @@
 {
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+  "epoch": 7,
   "metrics": {
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-    "train_acc": 72.30673284591315,
+    "val_acc": 71.136,
+    "train_acc": 73.43039588125514,
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-      "384": 63.494,
-      "448": 63.142,
-      "512": 66.344,
-      "576": 62.382,
-      "640": 55.392,
-      "704": 48.112
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+      "384": 61.654,
+      "448": 60.794,
+      "512": 67.11,
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+      "640": 53.92,
+      "704": 49.1
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-    "best_val_acc": 70.224,
-    "best_epoch": 5,
-    "final_train_acc": 72.30673284591315,
-    "training_time": "11m"
+    "best_val_acc": 71.136,
+    "best_epoch": 7,
+    "final_train_acc": 73.43039588125514,
+    "training_time": "15m"
   },
   "config": {
     "name": "geofractal_david_basin",
@@ -72,104 +72,104 @@
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@@ -179,7 +179,9 @@
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weights/GeoFractalDavid-Basin-k7/20251016_001155/training_history.json

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 }