Upload weights - GeoFractalDavid-Basin-k100 - Run 20251015_215228 - Acc 81.26%

weights/GeoFractalDavid-Basin-k100/20251015_215228/README.md

@@ -23,7 +23,7 @@ model-index:
       type: imagenet-1k
     metrics:
     - type: accuracy
-      value: 80.21
+      value: 81.26
       name: Validation Accuracy
 ---
 
@@ -34,16 +34,16 @@ Features must "fit" geometric signatures: k-simplex shapes, Cantor positions, an
 
 ## 🎯 Performance
 
-- **Best Validation Accuracy**: 80.21%
-- **Epoch**: 4/10
-- **Training Time**: 11m
+- **Best Validation Accuracy**: 81.26%
+- **Epoch**: 6/10
+- **Training Time**: 18m
 
 ### Per-Scale Performance
-- **Scale 384D**: 76.22%
-- **Scale 512D**: 75.70%
-- **Scale 768D**: 79.05%
-- **Scale 1024D**: 73.03%
-- **Scale 1280D**: 71.51%
+- **Scale 384D**: 73.76%
+- **Scale 512D**: 72.30%
+- **Scale 768D**: 80.01%
+- **Scale 1024D**: 68.60%
+- **Scale 1280D**: 68.76%
 
 
 ## 🏗️ Architecture
@@ -71,8 +71,8 @@ Each scale learns to weight these components differently.
 The alpha parameter controls middle-interval weighting in the Cantor staircase.
 
 - **Initial**: 0.4765
-- **Final**: 0.2674
-- **Change**: -0.2091
+- **Final**: 0.0860
+- **Change**: -0.3905
 - **Converged to 0.5**: False
 
 The Cantor staircase uses soft triadic decomposition with learnable alpha to map
@@ -84,29 +84,29 @@ Each class has a learned scalar Cantor prototype. The model pulls features towar
 their class's Cantor position.
 
 **Scale 384D**:
-- Mean: 0.2938
-- Std: 0.1685
-- Range: [-0.0009, 0.5011]
+- Mean: 0.1262
+- Std: 0.1473
+- Range: [-0.0379, 0.4845]
 
 **Scale 512D**:
-- Mean: 0.2935
-- Std: 0.1688
-- Range: [-0.0010, 0.5011]
+- Mean: 0.1260
+- Std: 0.1472
+- Range: [-0.0382, 0.4846]
 
 **Scale 768D**:
-- Mean: 0.2972
-- Std: 0.1672
-- Range: [-0.0007, 0.5011]
+- Mean: 0.1310
+- Std: 0.1496
+- Range: [-0.0335, 0.4861]
 
 **Scale 1024D**:
-- Mean: 0.2955
-- Std: 0.1680
-- Range: [-0.0008, 0.5010]
+- Mean: 0.1279
+- Std: 0.1481
+- Range: [-0.0352, 0.4848]
 
 **Scale 1280D**:
-- Mean: 0.2948
-- Std: 0.1681
-- Range: [-0.0007, 0.5008]
+- Mean: 0.1280
+- Std: 0.1482
+- Range: [-0.0357, 0.4851]
 
 
 Most classes cluster around 0.5 (middle Cantor region), with smooth spread across [0,1].
@@ -116,11 +116,11 @@ This creates a continuous manifold rather than discrete bins.
 
 Each scale learns optimal weights for combining geometric components:
 
-**Scale 384D**: Feature=0.750, Cantor=0.038, Crystal=0.213
-**Scale 512D**: Feature=0.675, Cantor=0.038, Crystal=0.287
-**Scale 768D**: Feature=0.934, Cantor=0.006, Crystal=0.060
-**Scale 1024D**: Feature=0.830, Cantor=0.013, Crystal=0.157
-**Scale 1280D**: Feature=0.806, Cantor=0.011, Crystal=0.183
+**Scale 384D**: Feature=0.790, Cantor=0.029, Crystal=0.181
+**Scale 512D**: Feature=0.692, Cantor=0.030, Crystal=0.278
+**Scale 768D**: Feature=0.956, Cantor=0.002, Crystal=0.041
+**Scale 1024D**: Feature=0.850, Cantor=0.007, Crystal=0.143
+**Scale 1280D**: Feature=0.777, Cantor=0.005, Crystal=0.218
 
 
 **Pattern**: Lower scales rely on feature similarity, higher scales use crystal geometry.

weights/GeoFractalDavid-Basin-k100/20251015_215228/model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6744687b6006939004487b8a0f3b8671221f674699654e183816871dbac4579b
+oid sha256:618ba0a9b92a9606190b9350e43c0b8cb32538fad85e3433e4ebeb7d397e138e
 size 1657081812

weights/GeoFractalDavid-Basin-k100/20251015_215228/model_metadata.json

@@ -1,19 +1,19 @@
 {
-  "epoch": 3,
+  "epoch": 5,
   "metrics": {
-    "val_acc": 80.212,
-    "train_acc": 81.6552408858486,
+    "val_acc": 81.264,
+    "train_acc": 83.23192839028792,
     "scale_accuracies": {
-      "384": 76.222,
-      "512": 75.7,
-      "768": 79.054,
-      "1024": 73.032,
-      "1280": 71.506
+      "384": 73.76,
+      "512": 72.304,
+      "768": 80.006,
+      "1024": 68.598,
+      "1280": 68.756
     },
-    "best_val_acc": 80.212,
-    "best_epoch": 3,
-    "final_train_acc": 81.6552408858486,
-    "training_time": "11m"
+    "best_val_acc": 81.264,
+    "best_epoch": 5,
+    "final_train_acc": 83.23192839028792,
+    "training_time": "18m"
   },
   "config": {
     "name": "geofractal_david_basin",
@@ -68,78 +68,78 @@
     "alpha_summary": {
       "global": {
         "initial": 0.47651901841163635,
-        "final": 0.2674439251422882,
-        "change": -0.20907509326934814,
+        "final": 0.08600518852472305,
+        "change": -0.3905138298869133,
         "converged_to_0.5": false
       }
     },
     "cantor_prototypes": {
       "384": {
-        "final_mean": 0.2938278615474701,
-        "final_std": 0.16854523122310638,
+        "final_mean": 0.126153364777565,
+        "final_std": 0.14734618365764618,
         "final_range": [
-          -0.0008550718193873763,
-          0.5011345148086548
+          -0.037852317094802856,
+          0.4844595193862915
         ]
       },
       "512": {
-        "final_mean": 0.2935275435447693,
-        "final_std": 0.16875462234020233,
+        "final_mean": 0.12597854435443878,
+        "final_std": 0.14719624817371368,
         "final_range": [
-          -0.0010234399233013391,
-          0.5011292099952698
+          -0.038224589079618454,
+          0.4846232533454895
         ]
       },
       "768": {
-        "final_mean": 0.2972216308116913,
-        "final_std": 0.16723190248012543,
+        "final_mean": 0.13096983730793,
+        "final_std": 0.14958152174949646,
         "final_range": [
-          -0.0006906716153025627,
-          0.501091480255127
+          -0.03346291929483414,
+          0.4860755205154419
         ]
       },
       "1024": {
-        "final_mean": 0.29551267623901367,
-        "final_std": 0.16796204447746277,
+        "final_mean": 0.12790125608444214,
+        "final_std": 0.148138165473938,
         "final_range": [
-          -0.0007872090209275484,
-          0.5009996294975281
+          -0.0352325513958931,
+          0.4847520887851715
         ]
       },
       "1280": {
-        "final_mean": 0.29475849866867065,
-        "final_std": 0.16814614832401276,
+        "final_mean": 0.12803278863430023,
+        "final_std": 0.14818762242794037,
         "final_range": [
-          -0.0007241899147629738,
-          0.5007756352424622
+          -0.03566541522741318,
+          0.4850762188434601
         ]
       }
     },
     "geo_weights": {
       "384": {
-        "feature": 0.749521791934967,
-        "cantor": 0.03791790083050728,
-        "crystal": 0.21256029605865479
+        "feature": 0.7898806929588318,
+        "cantor": 0.02874314785003662,
+        "crystal": 0.181376114487648
       },
       "512": {
-        "feature": 0.6745965480804443,
-        "cantor": 0.03832589089870453,
-        "crystal": 0.28707757592201233
+        "feature": 0.6919162273406982,
+        "cantor": 0.029592182487249374,
+        "crystal": 0.2784915864467621
       },
       "768": {
-        "feature": 0.9337414503097534,
-        "cantor": 0.005896884016692638,
-        "crystal": 0.06036167964339256
+        "feature": 0.9564967751502991,
+        "cantor": 0.0023245441261678934,
+        "crystal": 0.04117864742875099
       },
       "1024": {
-        "feature": 0.829938530921936,
-        "cantor": 0.013140466064214706,
-        "crystal": 0.15692101418972015
+        "feature": 0.8504803776741028,
+        "cantor": 0.006762458011507988,
+        "crystal": 0.1427571028470993
       },
       "1280": {
-        "feature": 0.8058218359947205,
-        "cantor": 0.01125203538686037,
-        "crystal": 0.18292604386806488
+        "feature": 0.7771109342575073,
+        "cantor": 0.0049291495233774185,
+        "crystal": 0.2179599106311798
       }
     },
     "training_history": {
@@ -147,31 +147,41 @@
         1,
         2,
         3,
-        4
+        4,
+        5,
+        6
       ],
       "train_loss": [
         1.9505906203113044,
         1.4809168164912885,
         1.3559537357606066,
-        1.2701277191075273
+        1.2701277191075273,
+        1.2047958634912777,
+        1.1590601703800714
       ],
       "train_acc": [
         73.81504518926884,
         80.02266683422224,
         80.93675531761276,
-        81.6552408858486
+        81.6552408858486,
+        82.46067842833916,
+        83.23192839028792
       ],
       "val_acc": [
         77.886,
         78.794,
         79.654,
-        80.212
+        80.212,
+        81.044,
+        81.264
       ],
       "lr": [
         0.001,
         0.0009755527298894294,
         0.0009046039886902864,
-        0.0007940987335200905
+        0.0007940987335200905,
+        0.0006548539886902864,
+        0.0005005000000000001
       ]
     }
   }

weights/GeoFractalDavid-Basin-k100/20251015_215228/training_history.json

@@ -4,31 +4,41 @@
       1,
       2,
       3,
-      4
+      4,
+      5,
+      6
     ],
     "train_loss": [
       1.9505906203113044,
       1.4809168164912885,
       1.3559537357606066,
-      1.2701277191075273
+      1.2701277191075273,
+      1.2047958634912777,
+      1.1590601703800714
     ],
     "train_acc": [
       73.81504518926884,
       80.02266683422224,
       80.93675531761276,
-      81.6552408858486
+      81.6552408858486,
+      82.46067842833916,
+      83.23192839028792
     ],
     "val_acc": [
       77.886,
       78.794,
       79.654,
-      80.212
+      80.212,
+      81.044,
+      81.264
     ],
     "lr": [
       0.001,
       0.0009755527298894294,
       0.0009046039886902864,
-      0.0007940987335200905
+      0.0007940987335200905,
+      0.0006548539886902864,
+      0.0005005000000000001
     ]
   },
   "loss_components": {
@@ -36,49 +46,65 @@
       1.8378438452562205,
       1.3904109782875536,
       1.2747827293202518,
-      1.1947141252577114
+      1.1947141252577114,
+      1.1406564257396297,
+      1.1068522563567176
     ],
     "correct": [
       0.6709056639918884,
       0.5508666726204153,
       0.46212543968480235,
-      0.4276195404866633
+      0.4276195404866633,
+      0.4136494039870299,
+      0.40744239060928267
     ],
     "incorrect": [
       0.44236757144474753,
       0.49260074028763146,
       0.5714762466498458,
-      0.6018640140470225
+      0.6018640140470225,
+      0.6114907695081668,
+      0.6145131488482412
     ],
     "contrast": [
       1.8915087977727763,
       1.186487875140894,
       1.0538383346681777,
-      0.9323251536383796
+      0.9323251536383796,
+      0.8425232719023007,
+      0.7843065797425688
     ],
     "coherence": [
       0.17278494205290135,
       0.13691392284684084,
       0.1167653447225358,
-      0.10019378941446648
+      0.10019378941446648,
+      0.07898570257729992,
+      0.05631235228440827
     ],
     "separation": [
       0.018797464182409962,
       0.03361053357805359,
       0.030615262179590867,
-      0.02386628114722505
+      0.02386628114722505,
+      0.02089437840107722,
+      0.02504361955681071
     ],
     "discretization": [
       0.10357529324607352,
       0.05982858222156481,
       0.06801877618907172,
-      0.09078408056578502
+      0.09078408056578502,
+      0.09189134842861951,
+      0.0826932579647905
     ],
     "total": [
       1.9505906203113044,
       1.4809168164912885,
       1.3559537357606066,
-      1.2701277191075273
+      1.2701277191075273,
+      1.2047958634912777,
+      1.1590601703800714
     ]
   },
   "scale_accuracies": {
@@ -86,37 +112,49 @@
       76.992,
       77.646,
       77.19,
-      76.222
+      76.222,
+      75.218,
+      73.76
     ],
     "512": [
       77.258,
       77.704,
       76.964,
-      75.7
+      75.7,
+      74.152,
+      72.304
     ],
     "768": [
       77.356,
       78.354,
       78.732,
-      79.054
+      79.054,
+      79.648,
+      80.006
     ],
     "1024": [
       77.152,
       77.75,
       76.162,
-      73.032
+      73.032,
+      70.208,
+      68.598
     ],
     "1280": [
       76.942,
       77.49,
       74.446,
-      71.506
+      71.506,
+      69.368,
+      68.756
     ]
   },
   "alpha_history": [
     0.47651901841163635,
     0.3986094892024994,
     0.34020790457725525,
-    0.2674439251422882
+    0.2674439251422882,
+    0.16413071751594543,
+    0.08600518852472305
   ]
 }