Upload weights - GeoFractalDavid-Basin-k50 - Run 20251015_222757 - Acc 80.36%

weights/GeoFractalDavid-Basin-k50/20251015_222757/README.md

@@ -23,7 +23,7 @@ model-index:
       type: imagenet-1k
     metrics:
     - type: accuracy
-      value: 79.01
+      value: 80.36
       name: Validation Accuracy
 ---
 
@@ -34,18 +34,18 @@ Features must "fit" geometric signatures: k-simplex shapes, Cantor positions, an
 
 ## 🎯 Performance
 
-- **Best Validation Accuracy**: 79.01%
-- **Epoch**: 2/10
-- **Training Time**: 5m
+- **Best Validation Accuracy**: 80.36%
+- **Epoch**: 4/10
+- **Training Time**: 11m
 
 ### Per-Scale Performance
-- **Scale 576D**: 78.05%
-- **Scale 640D**: 77.73%
-- **Scale 704D**: 78.18%
-- **Scale 768D**: 77.52%
-- **Scale 832D**: 77.78%
-- **Scale 896D**: 77.79%
-- **Scale 960D**: 77.53%
+- **Scale 576D**: 76.15%
+- **Scale 640D**: 75.49%
+- **Scale 704D**: 77.49%
+- **Scale 768D**: 74.66%
+- **Scale 832D**: 75.15%
+- **Scale 896D**: 76.66%
+- **Scale 960D**: 71.48%
 
 
 ## 🏗️ 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**: 0.4746
-- **Final**: 0.3957
-- **Change**: -0.0789
+- **Final**: 0.2590
+- **Change**: -0.2156
 - **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: 0.4079
-- Std: 0.1335
-- Range: [0.0001, 0.5663]
+- Mean: 0.2858
+- Std: 0.1704
+- Range: [-0.0008, 0.5007]
 
 **Scale 640D**:
-- Mean: 0.4078
-- Std: 0.1341
-- Range: [-0.0002, 0.5663]
+- Mean: 0.2854
+- Std: 0.1706
+- Range: [-0.0008, 0.5012]
 
 **Scale 704D**:
-- Mean: 0.4076
-- Std: 0.1266
-- Range: [0.0002, 0.5618]
+- Mean: 0.2919
+- Std: 0.1680
+- Range: [-0.0009, 0.5009]
 
 **Scale 768D**:
-- Mean: 0.4047
-- Std: 0.1384
-- Range: [-0.0000, 0.5645]
+- Mean: 0.2816
+- Std: 0.1721
+- Range: [-0.0012, 0.5009]
 
 **Scale 832D**:
-- Mean: 0.4062
-- Std: 0.1359
-- Range: [-0.0002, 0.5651]
+- Mean: 0.2837
+- Std: 0.1713
+- Range: [-0.0013, 0.5009]
 
 **Scale 896D**:
-- Mean: 0.4052
-- Std: 0.1302
-- Range: [-0.0001, 0.5604]
+- Mean: 0.2890
+- Std: 0.1691
+- Range: [-0.0008, 0.5007]
 
 **Scale 960D**:
-- Mean: 0.4056
-- Std: 0.1329
-- Range: [-0.0002, 0.5615]
+- Mean: 0.2867
+- Std: 0.1701
+- Range: [-0.0011, 0.5006]
 
 
 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=0.642, Cantor=0.058, Crystal=0.301
-**Scale 640D**: Feature=0.621, Cantor=0.058, Crystal=0.321
-**Scale 704D**: Feature=0.836, Cantor=0.027, Crystal=0.137
-**Scale 768D**: Feature=0.545, Cantor=0.071, Crystal=0.383
-**Scale 832D**: Feature=0.606, Cantor=0.057, Crystal=0.337
-**Scale 896D**: Feature=0.808, Cantor=0.028, Crystal=0.163
-**Scale 960D**: Feature=0.713, Cantor=0.039, Crystal=0.248
+**Scale 576D**: Feature=0.664, Cantor=0.037, Crystal=0.300
+**Scale 640D**: Feature=0.629, Cantor=0.038, Crystal=0.333
+**Scale 704D**: Feature=0.936, Cantor=0.006, Crystal=0.058
+**Scale 768D**: Feature=0.534, Cantor=0.047, Crystal=0.419
+**Scale 832D**: Feature=0.609, Cantor=0.037, Crystal=0.354
+**Scale 896D**: Feature=0.892, Cantor=0.006, Crystal=0.102
+**Scale 960D**: Feature=0.737, Cantor=0.019, Crystal=0.244
 
 
 **Pattern**: Lower scales rely on feature similarity, higher scales use crystal geometry.

weights/GeoFractalDavid-Basin-k50/20251015_222757/model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:a06afd2edf5ceec6759fa0cf2474a79d36e150010506952ff37522860eb25537
 size 1163870188

weights/GeoFractalDavid-Basin-k50/20251015_222757/model_metadata.json

@@ -1,21 +1,21 @@
 {
-  "epoch": 1,
+  "epoch": 3,
   "metrics": {
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-    "train_acc": 80.07714841234593,
+    "val_acc": 80.356,
+    "train_acc": 81.860054153752,
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-      "704": 78.18,
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+      "704": 77.492,
+      "768": 74.662,
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-    "best_val_acc": 79.014,
-    "best_epoch": 1,
-    "final_train_acc": 80.07714841234593,
-    "training_time": "5m"
+    "best_val_acc": 80.356,
+    "best_epoch": 3,
+    "final_train_acc": 81.860054153752,
+    "training_time": "11m"
   },
   "config": {
     "name": "geofractal_david_basin",
@@ -72,126 +72,136 @@
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weights/GeoFractalDavid-Basin-k50/20251015_222757/training_history.json

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 }