vit-base-patch16-224-ve-U12-b-80
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: 0.8139
- Accuracy: 0.8478
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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 80
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.92 | 6 | 1.3850 | 0.3478 |
| 1.3848 | 2.0 | 13 | 1.3701 | 0.4783 |
| 1.3848 | 2.92 | 19 | 1.3196 | 0.5 |
| 1.3508 | 4.0 | 26 | 1.2287 | 0.4130 |
| 1.2282 | 4.92 | 32 | 1.1280 | 0.3913 |
| 1.2282 | 6.0 | 39 | 1.0625 | 0.3913 |
| 1.0677 | 6.92 | 45 | 0.9840 | 0.5 |
| 0.9278 | 8.0 | 52 | 0.8970 | 0.6957 |
| 0.9278 | 8.92 | 58 | 0.8530 | 0.7391 |
| 0.8003 | 10.0 | 65 | 0.7872 | 0.8043 |
| 0.6486 | 10.92 | 71 | 0.6974 | 0.8043 |
| 0.6486 | 12.0 | 78 | 0.6409 | 0.8043 |
| 0.514 | 12.92 | 84 | 0.6050 | 0.8261 |
| 0.3945 | 14.0 | 91 | 0.6589 | 0.7609 |
| 0.3945 | 14.92 | 97 | 0.6343 | 0.7609 |
| 0.337 | 16.0 | 104 | 0.7340 | 0.7174 |
| 0.2779 | 16.92 | 110 | 0.5629 | 0.8261 |
| 0.2779 | 18.0 | 117 | 0.5934 | 0.8261 |
| 0.2374 | 18.92 | 123 | 0.7080 | 0.7609 |
| 0.2201 | 20.0 | 130 | 0.7100 | 0.7391 |
| 0.2201 | 20.92 | 136 | 0.7673 | 0.7609 |
| 0.1889 | 22.0 | 143 | 0.7889 | 0.7391 |
| 0.1889 | 22.92 | 149 | 0.7971 | 0.7391 |
| 0.1463 | 24.0 | 156 | 0.6888 | 0.7826 |
| 0.1261 | 24.92 | 162 | 0.8399 | 0.7609 |
| 0.1261 | 26.0 | 169 | 0.7244 | 0.7826 |
| 0.1489 | 26.92 | 175 | 0.8311 | 0.7391 |
| 0.1132 | 28.0 | 182 | 0.7987 | 0.7609 |
| 0.1132 | 28.92 | 188 | 0.7380 | 0.8043 |
| 0.1279 | 30.0 | 195 | 0.8103 | 0.8043 |
| 0.0925 | 30.92 | 201 | 0.8462 | 0.7609 |
| 0.0925 | 32.0 | 208 | 0.8233 | 0.8043 |
| 0.0893 | 32.92 | 214 | 0.8241 | 0.7826 |
| 0.083 | 34.0 | 221 | 0.8443 | 0.7826 |
| 0.083 | 34.92 | 227 | 0.8429 | 0.7826 |
| 0.1044 | 36.0 | 234 | 0.9362 | 0.7609 |
| 0.0739 | 36.92 | 240 | 1.1173 | 0.7391 |
| 0.0739 | 38.0 | 247 | 0.7812 | 0.8261 |
| 0.0962 | 38.92 | 253 | 0.7595 | 0.8043 |
| 0.0869 | 40.0 | 260 | 0.8031 | 0.8261 |
| 0.0869 | 40.92 | 266 | 0.8359 | 0.8261 |
| 0.0837 | 42.0 | 273 | 0.8151 | 0.8261 |
| 0.0837 | 42.92 | 279 | 0.8295 | 0.8261 |
| 0.0535 | 44.0 | 286 | 0.8096 | 0.8261 |
| 0.0694 | 44.92 | 292 | 0.8352 | 0.8261 |
| 0.0694 | 46.0 | 299 | 0.8216 | 0.8261 |
| 0.0736 | 46.92 | 305 | 0.8683 | 0.8043 |
| 0.0705 | 48.0 | 312 | 0.8554 | 0.8261 |
| 0.0705 | 48.92 | 318 | 0.8139 | 0.8478 |
| 0.0559 | 50.0 | 325 | 0.9030 | 0.7826 |
| 0.0474 | 50.92 | 331 | 0.9053 | 0.7609 |
| 0.0474 | 52.0 | 338 | 0.8810 | 0.8261 |
| 0.0477 | 52.92 | 344 | 0.8912 | 0.8043 |
| 0.0529 | 54.0 | 351 | 0.9078 | 0.8043 |
| 0.0529 | 54.92 | 357 | 0.8804 | 0.8043 |
| 0.038 | 56.0 | 364 | 0.9498 | 0.7826 |
| 0.0407 | 56.92 | 370 | 0.9134 | 0.8043 |
| 0.0407 | 58.0 | 377 | 0.8452 | 0.8478 |
| 0.0353 | 58.92 | 383 | 0.8735 | 0.8261 |
| 0.0349 | 60.0 | 390 | 0.9153 | 0.8043 |
| 0.0349 | 60.92 | 396 | 0.9209 | 0.8043 |
| 0.0322 | 62.0 | 403 | 0.9091 | 0.8261 |
| 0.0322 | 62.92 | 409 | 0.9137 | 0.8261 |
| 0.0392 | 64.0 | 416 | 0.8896 | 0.8261 |
| 0.0419 | 64.92 | 422 | 0.8613 | 0.8478 |
| 0.0419 | 66.0 | 429 | 0.8844 | 0.8261 |
| 0.0518 | 66.92 | 435 | 0.9093 | 0.8043 |
| 0.0349 | 68.0 | 442 | 0.9082 | 0.8043 |
| 0.0349 | 68.92 | 448 | 0.8879 | 0.8261 |
| 0.0359 | 70.0 | 455 | 0.8809 | 0.8261 |
| 0.0377 | 70.92 | 461 | 0.8777 | 0.8261 |
| 0.0377 | 72.0 | 468 | 0.8845 | 0.8261 |
| 0.0324 | 72.92 | 474 | 0.8845 | 0.8261 |
| 0.0365 | 73.85 | 480 | 0.8850 | 0.8261 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0