swin-tiny-patch4-window7-224-ve-U13-b-80b
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6122
- Accuracy: 0.7826
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 | 1.3855 | 0.1304 |
| 1.3852 | 2.0 | 13 | 1.3762 | 0.2826 |
| 1.3852 | 2.92 | 19 | 1.3521 | 0.2826 |
| 1.3565 | 4.0 | 26 | 1.2510 | 0.3478 |
| 1.2024 | 4.92 | 32 | 1.1528 | 0.3478 |
| 1.2024 | 6.0 | 39 | 1.0294 | 0.5 |
| 1.0453 | 6.92 | 45 | 0.9608 | 0.5217 |
| 0.8827 | 8.0 | 52 | 0.8801 | 0.6087 |
| 0.8827 | 8.92 | 58 | 0.9884 | 0.5652 |
| 0.7887 | 10.0 | 65 | 0.7927 | 0.6522 |
| 0.6795 | 10.92 | 71 | 0.7237 | 0.6522 |
| 0.6795 | 12.0 | 78 | 0.7250 | 0.6739 |
| 0.5777 | 12.92 | 84 | 0.7140 | 0.6957 |
| 0.496 | 14.0 | 91 | 0.8014 | 0.6957 |
| 0.496 | 14.92 | 97 | 0.8701 | 0.6739 |
| 0.4224 | 16.0 | 104 | 0.9384 | 0.6522 |
| 0.3744 | 16.92 | 110 | 0.7594 | 0.7174 |
| 0.3744 | 18.0 | 117 | 0.6122 | 0.7826 |
| 0.3775 | 18.92 | 123 | 0.8143 | 0.7174 |
| 0.3275 | 20.0 | 130 | 0.9981 | 0.6522 |
| 0.3275 | 20.92 | 136 | 0.8603 | 0.7174 |
| 0.3202 | 22.0 | 143 | 0.8412 | 0.6957 |
| 0.3202 | 22.92 | 149 | 0.8654 | 0.7174 |
| 0.2849 | 24.0 | 156 | 0.9650 | 0.6957 |
| 0.2518 | 24.92 | 162 | 0.8102 | 0.7609 |
| 0.2518 | 26.0 | 169 | 0.7203 | 0.7826 |
| 0.2467 | 26.92 | 175 | 0.9435 | 0.7391 |
| 0.2218 | 28.0 | 182 | 0.8905 | 0.7391 |
| 0.2218 | 28.92 | 188 | 1.0828 | 0.6957 |
| 0.2075 | 30.0 | 195 | 0.8936 | 0.7174 |
| 0.1893 | 30.92 | 201 | 0.8836 | 0.7826 |
| 0.1893 | 32.0 | 208 | 0.9692 | 0.7174 |
| 0.194 | 32.92 | 214 | 1.0390 | 0.7609 |
| 0.1739 | 34.0 | 221 | 0.8695 | 0.7609 |
| 0.1739 | 34.92 | 227 | 1.1836 | 0.6739 |
| 0.1895 | 36.0 | 234 | 1.0131 | 0.7391 |
| 0.1428 | 36.92 | 240 | 0.9618 | 0.7609 |
| 0.1428 | 38.0 | 247 | 0.9950 | 0.7609 |
| 0.1443 | 38.92 | 253 | 0.9113 | 0.7826 |
| 0.1574 | 40.0 | 260 | 0.9213 | 0.7174 |
| 0.1574 | 40.92 | 266 | 0.9437 | 0.7391 |
| 0.1442 | 42.0 | 273 | 0.9226 | 0.7609 |
| 0.1442 | 42.92 | 279 | 0.9430 | 0.7391 |
| 0.1186 | 44.0 | 286 | 0.9759 | 0.7826 |
| 0.1135 | 44.92 | 292 | 0.9651 | 0.7391 |
| 0.1135 | 46.0 | 299 | 0.9536 | 0.7609 |
| 0.1299 | 46.92 | 305 | 0.9118 | 0.7609 |
| 0.134 | 48.0 | 312 | 0.9848 | 0.7826 |
| 0.134 | 48.92 | 318 | 0.8641 | 0.7609 |
| 0.1418 | 50.0 | 325 | 1.0553 | 0.7609 |
| 0.1074 | 50.92 | 331 | 1.2511 | 0.6957 |
| 0.1074 | 52.0 | 338 | 1.0186 | 0.7391 |
| 0.1144 | 52.92 | 344 | 1.0467 | 0.7174 |
| 0.0999 | 54.0 | 351 | 0.9898 | 0.7391 |
| 0.0999 | 54.92 | 357 | 1.1780 | 0.7391 |
| 0.1131 | 56.0 | 364 | 1.0015 | 0.7609 |
| 0.1152 | 56.92 | 370 | 1.0759 | 0.7609 |
| 0.1152 | 58.0 | 377 | 1.1294 | 0.7174 |
| 0.1012 | 58.92 | 383 | 1.0894 | 0.7391 |
| 0.0938 | 60.0 | 390 | 1.0764 | 0.7391 |
| 0.0938 | 60.92 | 396 | 1.1784 | 0.7174 |
| 0.0944 | 62.0 | 403 | 1.1581 | 0.7174 |
| 0.0944 | 62.92 | 409 | 1.0444 | 0.7391 |
| 0.1015 | 64.0 | 416 | 1.0996 | 0.7391 |
| 0.0762 | 64.92 | 422 | 1.1235 | 0.7609 |
| 0.0762 | 66.0 | 429 | 1.0999 | 0.7391 |
| 0.0775 | 66.92 | 435 | 1.0776 | 0.7391 |
| 0.0787 | 68.0 | 442 | 1.0879 | 0.7391 |
| 0.0787 | 68.92 | 448 | 1.0913 | 0.7391 |
| 0.081 | 70.0 | 455 | 1.0558 | 0.7391 |
| 0.0749 | 70.92 | 461 | 1.0401 | 0.7391 |
| 0.0749 | 72.0 | 468 | 1.0539 | 0.7391 |
| 0.0841 | 72.92 | 474 | 1.0663 | 0.7391 |
| 0.0928 | 73.85 | 480 | 1.0712 | 0.7391 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0