Upload weights - GeoFractalDavid-Basin-k50 - Run 20251016_011725 - Acc 70.99%

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

@@ -23,7 +23,7 @@ model-index:
       type: imagenet-1k
     metrics:
     - type: accuracy
-      value: 70.22
+      value: 70.99
       name: Validation Accuracy
 ---
 
@@ -34,17 +34,17 @@ Features must "fit" geometric signatures: k-simplex shapes, Cantor positions, an
 
 ## 🎯 Performance
 
-- **Best Validation Accuracy**: 70.22%
-- **Epoch**: 6/10
-- **Training Time**: 12m
+- **Best Validation Accuracy**: 70.99%
+- **Epoch**: 8/10
+- **Training Time**: 17m
 
 ### Per-Scale Performance
-- **Scale 448D**: 58.89%
-- **Scale 512D**: 58.12%
-- **Scale 576D**: 67.32%
-- **Scale 640D**: 56.80%
-- **Scale 704D**: 62.28%
-- **Scale 768D**: 52.94%
+- **Scale 448D**: 55.36%
+- **Scale 512D**: 53.71%
+- **Scale 576D**: 68.07%
+- **Scale 640D**: 51.45%
+- **Scale 704D**: 62.92%
+- **Scale 768D**: 47.41%
 
 
 ## 🏗️ Architecture
@@ -72,8 +72,8 @@ Each scale learns to weight these components differently.
 The alpha parameter controls middle-interval weighting in the Cantor staircase.
 
 - **Initial**: 0.3301
-- **Final**: 0.0000
-- **Change**: -0.3300
+- **Final**: -0.0509
+- **Change**: -0.3810
 - **Converged to 0.5**: False
 
 The Cantor staircase uses soft triadic decomposition with learnable alpha to map
@@ -85,34 +85,34 @@ Each class has a learned scalar Cantor prototype. The model pulls features towar
 their class's Cantor position.
 
 **Scale 448D**:
-- Mean: 0.0428
-- Std: 0.0793
-- Range: [-0.1109, 0.2148]
+- Mean: 0.0278
+- Std: 0.0780
+- Range: [-0.1270, 0.1944]
 
 **Scale 512D**:
-- Mean: 0.0428
-- Std: 0.0793
-- Range: [-0.1107, 0.2148]
+- Mean: 0.0277
+- Std: 0.0780
+- Range: [-0.1269, 0.1945]
 
 **Scale 576D**:
-- Mean: 0.0434
-- Std: 0.0794
-- Range: [-0.1107, 0.2185]
+- Mean: 0.0279
+- Std: 0.0781
+- Range: [-0.1267, 0.1947]
 
 **Scale 640D**:
-- Mean: 0.0427
-- Std: 0.0793
-- Range: [-0.1109, 0.2145]
+- Mean: 0.0277
+- Std: 0.0780
+- Range: [-0.1273, 0.1942]
 
 **Scale 704D**:
-- Mean: 0.0433
-- Std: 0.0794
-- Range: [-0.1107, 0.2177]
+- Mean: 0.0279
+- Std: 0.0781
+- Range: [-0.1266, 0.1946]
 
 **Scale 768D**:
-- Mean: 0.0429
-- Std: 0.0793
-- Range: [-0.1106, 0.2144]
+- Mean: 0.0277
+- Std: 0.0780
+- Range: [-0.1271, 0.1944]
 
 
 Most classes cluster around 0.5 (middle Cantor region), with smooth spread across [0,1].
@@ -122,12 +122,12 @@ This creates a continuous manifold rather than discrete bins.
 
 Each scale learns optimal weights for combining geometric components:
 
-**Scale 448D**: Feature=0.667, Cantor=0.032, Crystal=0.301
-**Scale 512D**: Feature=0.565, Cantor=0.034, Crystal=0.401
-**Scale 576D**: Feature=0.983, Cantor=0.002, Crystal=0.015
-**Scale 640D**: Feature=0.501, Cantor=0.034, Crystal=0.466
-**Scale 704D**: Feature=0.889, Cantor=0.003, Crystal=0.109
-**Scale 768D**: Feature=0.547, Cantor=0.019, Crystal=0.434
+**Scale 448D**: Feature=0.644, Cantor=0.025, Crystal=0.331
+**Scale 512D**: Feature=0.518, Cantor=0.026, Crystal=0.456
+**Scale 576D**: Feature=0.990, Cantor=0.001, Crystal=0.009
+**Scale 640D**: Feature=0.444, Cantor=0.026, Crystal=0.530
+**Scale 704D**: Feature=0.883, Cantor=0.002, Crystal=0.115
+**Scale 768D**: Feature=0.459, Cantor=0.014, Crystal=0.527
 
 
 **Pattern**: Lower scales rely on feature similarity, higher scales use crystal geometry.

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

@@ -1,3 +1,3 @@
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+oid sha256:57e5ed5333623cc1ffa3470d01b0ef80b5a7442e8af5f42800422e04ba98e9a0
 size 777585564

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

@@ -1,20 +1,20 @@
 {
-  "epoch": 5,
+  "epoch": 7,
   "metrics": {
-    "val_acc": 70.216,
-    "train_acc": 72.25490509824246,
+    "val_acc": 70.986,
+    "train_acc": 73.44819215605771,
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-      "512": 58.122,
-      "576": 67.318,
-      "640": 56.802,
-      "704": 62.28,
-      "768": 52.938
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+      "512": 53.714,
+      "576": 68.07,
+      "640": 51.448,
+      "704": 62.922,
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-    "best_val_acc": 70.216,
-    "best_epoch": 5,
-    "final_train_acc": 72.25490509824246,
-    "training_time": "12m"
+    "best_val_acc": 70.986,
+    "best_epoch": 7,
+    "final_train_acc": 73.44819215605771,
+    "training_time": "17m"
   },
   "config": {
     "name": "geofractal_david_basin",
@@ -70,91 +70,91 @@
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     "geo_weights": {
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@@ -164,7 +164,9 @@
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@@ -188,7 +194,9 @@
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weights/GeoFractalDavid-Basin-k50/20251016_011725/training_history.json

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 }