Epoch 200: 65.83%
Browse files
README.md
CHANGED
|
@@ -5,7 +5,7 @@ tags:
|
|
| 5 |
- cifar100
|
| 6 |
- geometric-learning
|
| 7 |
- fractal-encoding
|
| 8 |
-
-
|
| 9 |
- no-attention
|
| 10 |
- no-cross-entropy
|
| 11 |
datasets:
|
|
@@ -34,9 +34,9 @@ model-index:
|
|
| 34 |
|
| 35 |
**Geometric Basin Classification for CIFAR-100**
|
| 36 |
|
| 37 |
-
|
| 38 |
|
| 39 |
-
|
| 40 |
|
| 41 |
---
|
| 42 |
|
|
@@ -46,23 +46,23 @@ Current Status: Epoch 150/200
|
|
| 46 |
|--------|-------|
|
| 47 |
| **Best Test Accuracy** | **69.08%** |
|
| 48 |
| **Best Epoch** | 190 |
|
| 49 |
-
| **Current Train Accuracy** |
|
| 50 |
-
| **Current Test Accuracy** |
|
| 51 |
-
| **Current α (Cantor param)** | 0.
|
| 52 |
| **Total Parameters** | 198,392,937 |
|
| 53 |
-
| **Training Time** | 1:
|
| 54 |
|
| 55 |
### All Training Runs
|
| 56 |
|
| 57 |
| Timestamp | Status | Best Epoch | Test Acc | Train Acc | α |
|
| 58 |
|-----------|--------|------------|----------|-----------|---|
|
| 59 |
-
| `20251010_100047` |
|
| 60 |
|
| 61 |
### Comparison to State-of-the-Art
|
| 62 |
|
| 63 |
| Model | Accuracy | Status |
|
| 64 |
|-------|----------|--------|
|
| 65 |
-
| **geo-beatrix (this model)** | **69.08%** |
|
| 66 |
| vit-beatrix-dualstream | 66.0% | Vision Transformer + Cross-Entropy |
|
| 67 |
|
| 68 |
✅ **geo-beatrix has surpassed all baselines!**
|
|
@@ -187,18 +187,18 @@ epoch_checkpoint = hf_hub_download(
|
|
| 187 |
|
| 188 |
### Best Checkpoint
|
| 189 |
- Epoch: 190
|
| 190 |
-
- Train Acc:
|
| 191 |
-
- Test Acc:
|
| 192 |
-
- Alpha: 0.
|
| 193 |
-
- Loss: 0.
|
| 194 |
|
| 195 |
### Latest 5 Epochs
|
| 196 |
|
| 197 |
-
- **Epoch
|
| 198 |
-
- **Epoch
|
| 199 |
-
- **Epoch
|
| 200 |
-
- **Epoch
|
| 201 |
-
- **Epoch
|
| 202 |
|
| 203 |
### Training Milestones
|
| 204 |
- 🎯 **50% Accuracy** reached at epoch 35
|
|
|
|
| 5 |
- cifar100
|
| 6 |
- geometric-learning
|
| 7 |
- fractal-encoding
|
| 8 |
+
- trained
|
| 9 |
- no-attention
|
| 10 |
- no-cross-entropy
|
| 11 |
datasets:
|
|
|
|
| 34 |
|
| 35 |
**Geometric Basin Classification for CIFAR-100**
|
| 36 |
|
| 37 |
+
🎉 **Training Complete** 🎉
|
| 38 |
|
| 39 |
+
Final Status: Epoch 200/200
|
| 40 |
|
| 41 |
---
|
| 42 |
|
|
|
|
| 46 |
|--------|-------|
|
| 47 |
| **Best Test Accuracy** | **69.08%** |
|
| 48 |
| **Best Epoch** | 190 |
|
| 49 |
+
| **Current Train Accuracy** | 62.61% |
|
| 50 |
+
| **Current Test Accuracy** | 65.83% |
|
| 51 |
+
| **Current α (Cantor param)** | 0.4462 |
|
| 52 |
| **Total Parameters** | 198,392,937 |
|
| 53 |
+
| **Training Time** | 1:47:30 |
|
| 54 |
|
| 55 |
### All Training Runs
|
| 56 |
|
| 57 |
| Timestamp | Status | Best Epoch | Test Acc | Train Acc | α |
|
| 58 |
|-----------|--------|------------|----------|-----------|---|
|
| 59 |
+
| `20251010_100047` | ✅ | 190 | **69.08%** | 62.61% | 0.4462 |
|
| 60 |
|
| 61 |
### Comparison to State-of-the-Art
|
| 62 |
|
| 63 |
| Model | Accuracy | Status |
|
| 64 |
|-------|----------|--------|
|
| 65 |
+
| **geo-beatrix (this model)** | **69.08%** | ✅ Complete |
|
| 66 |
| vit-beatrix-dualstream | 66.0% | Vision Transformer + Cross-Entropy |
|
| 67 |
|
| 68 |
✅ **geo-beatrix has surpassed all baselines!**
|
|
|
|
| 187 |
|
| 188 |
### Best Checkpoint
|
| 189 |
- Epoch: 190
|
| 190 |
+
- Train Acc: 67.58%
|
| 191 |
+
- Test Acc: 67.91%
|
| 192 |
+
- Alpha: 0.4461
|
| 193 |
+
- Loss: 0.4957
|
| 194 |
|
| 195 |
### Latest 5 Epochs
|
| 196 |
|
| 197 |
+
- **Epoch 196**: Train 67.80%, Test 0.00%, α=0.4462, Loss=0.4937
|
| 198 |
+
- **Epoch 197**: Train 64.55%, Test 0.00%, α=0.4462, Loss=0.4892
|
| 199 |
+
- **Epoch 198**: Train 69.19%, Test 0.00%, α=0.4462, Loss=0.4939
|
| 200 |
+
- **Epoch 199**: Train 63.82%, Test 0.00%, α=0.4462, Loss=0.4870
|
| 201 |
+
- **Epoch 200**: Train 62.61%, Test 65.83%, α=0.4462, Loss=0.4909
|
| 202 |
|
| 203 |
### Training Milestones
|
| 204 |
- 🎯 **50% Accuracy** reached at epoch 35
|