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vit-base-patch16-224-Trial006-007-008-YEL_STEM3
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1618
- Accuracy: 0.9241
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7035 | 1.0 | 6 | 0.6557 | 0.6203 |
| 0.6168 | 2.0 | 12 | 0.5788 | 0.7215 |
| 0.5412 | 3.0 | 18 | 0.5005 | 0.7785 |
| 0.496 | 4.0 | 24 | 0.4946 | 0.7722 |
| 0.4024 | 5.0 | 30 | 0.4057 | 0.8165 |
| 0.4098 | 6.0 | 36 | 0.3076 | 0.8544 |
| 0.3645 | 7.0 | 42 | 0.3250 | 0.8418 |
| 0.276 | 8.0 | 48 | 0.2206 | 0.8924 |
| 0.3358 | 9.0 | 54 | 0.2100 | 0.8987 |
| 0.3386 | 10.0 | 60 | 0.1618 | 0.9241 |
| 0.2778 | 11.0 | 66 | 0.1609 | 0.9177 |
| 0.25 | 12.0 | 72 | 0.1581 | 0.9114 |
| 0.2914 | 13.0 | 78 | 0.1663 | 0.9114 |
| 0.2273 | 14.0 | 84 | 0.1525 | 0.9177 |
| 0.2694 | 15.0 | 90 | 0.1708 | 0.9051 |
| 0.2745 | 16.0 | 96 | 0.2364 | 0.8734 |
| 0.2809 | 17.0 | 102 | 0.1976 | 0.8608 |
| 0.2368 | 18.0 | 108 | 0.1517 | 0.9114 |
| 0.328 | 19.0 | 114 | 0.2454 | 0.8671 |
| 0.2571 | 20.0 | 120 | 0.1482 | 0.9114 |
| 0.2996 | 21.0 | 126 | 0.1629 | 0.8987 |
| 0.266 | 22.0 | 132 | 0.1360 | 0.9114 |
| 0.2323 | 23.0 | 138 | 0.1427 | 0.9114 |
| 0.2285 | 24.0 | 144 | 0.1683 | 0.9051 |
| 0.2566 | 25.0 | 150 | 0.1442 | 0.9114 |
| 0.2509 | 26.0 | 156 | 0.1595 | 0.9114 |
| 0.2337 | 27.0 | 162 | 0.1291 | 0.9177 |
| 0.2203 | 28.0 | 168 | 0.1302 | 0.8987 |
| 0.2409 | 29.0 | 174 | 0.1274 | 0.9114 |
| 0.2256 | 30.0 | 180 | 0.1272 | 0.8987 |
| 0.2157 | 31.0 | 186 | 0.1289 | 0.9177 |
| 0.2168 | 32.0 | 192 | 0.1267 | 0.9114 |
| 0.2426 | 33.0 | 198 | 0.1438 | 0.8987 |
| 0.2404 | 34.0 | 204 | 0.1388 | 0.8987 |
| 0.2218 | 35.0 | 210 | 0.1243 | 0.9241 |
| 0.3068 | 36.0 | 216 | 0.1268 | 0.9241 |
| 0.1721 | 37.0 | 222 | 0.1477 | 0.8987 |
| 0.2201 | 38.0 | 228 | 0.1545 | 0.8987 |
| 0.2581 | 39.0 | 234 | 0.1700 | 0.8987 |
| 0.213 | 40.0 | 240 | 0.1254 | 0.9114 |
| 0.2953 | 41.0 | 246 | 0.1237 | 0.9114 |
| 0.2564 | 42.0 | 252 | 0.1472 | 0.9051 |
| 0.249 | 43.0 | 258 | 0.1409 | 0.9051 |
| 0.2372 | 44.0 | 264 | 0.1495 | 0.9114 |
| 0.2541 | 45.0 | 270 | 0.1412 | 0.9051 |
| 0.1997 | 46.0 | 276 | 0.1308 | 0.9114 |
| 0.2381 | 47.0 | 282 | 0.1253 | 0.9177 |
| 0.2623 | 48.0 | 288 | 0.1267 | 0.9051 |
| 0.1855 | 49.0 | 294 | 0.1285 | 0.9051 |
| 0.1877 | 50.0 | 300 | 0.1289 | 0.9051 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1
