Swin-DMAE-H-DA-REVAL-80
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0766
- Accuracy: 0.75
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.608 | 0.97 | 22 | 1.6091 | 0.25 |
| 1.5899 | 1.98 | 45 | 1.5960 | 0.1923 |
| 1.4759 | 2.99 | 68 | 1.4430 | 0.3462 |
| 1.1012 | 4.0 | 91 | 1.3213 | 0.5192 |
| 0.8965 | 4.97 | 113 | 1.1938 | 0.4231 |
| 0.7214 | 5.98 | 136 | 1.1870 | 0.4615 |
| 0.6757 | 6.99 | 159 | 1.2117 | 0.5 |
| 0.5529 | 8.0 | 182 | 1.1976 | 0.4615 |
| 0.5279 | 8.97 | 204 | 1.1250 | 0.5192 |
| 0.4701 | 9.98 | 227 | 1.0999 | 0.5577 |
| 0.3721 | 10.99 | 250 | 0.7842 | 0.6538 |
| 0.3631 | 12.0 | 273 | 1.1728 | 0.6154 |
| 0.3384 | 12.97 | 295 | 1.2413 | 0.5769 |
| 0.2531 | 13.98 | 318 | 0.9144 | 0.6346 |
| 0.2753 | 14.99 | 341 | 0.8959 | 0.6923 |
| 0.2611 | 16.0 | 364 | 1.1399 | 0.6538 |
| 0.2072 | 16.97 | 386 | 1.0732 | 0.7115 |
| 0.2532 | 17.98 | 409 | 1.1922 | 0.7115 |
| 0.1633 | 18.99 | 432 | 1.0600 | 0.6731 |
| 0.1946 | 20.0 | 455 | 1.2289 | 0.6538 |
| 0.2214 | 20.97 | 477 | 1.3591 | 0.6731 |
| 0.1666 | 21.98 | 500 | 1.0736 | 0.7115 |
| 0.141 | 22.99 | 523 | 1.0315 | 0.6923 |
| 0.1275 | 24.0 | 546 | 1.0766 | 0.75 |
| 0.136 | 24.97 | 568 | 1.1796 | 0.7115 |
| 0.1402 | 25.98 | 591 | 1.0339 | 0.7115 |
| 0.1336 | 26.99 | 614 | 1.3446 | 0.6154 |
| 0.1218 | 28.0 | 637 | 1.2967 | 0.7115 |
| 0.1034 | 28.97 | 659 | 1.5955 | 0.6538 |
| 0.1196 | 29.98 | 682 | 1.5721 | 0.5769 |
| 0.1368 | 30.99 | 705 | 1.8208 | 0.6346 |
| 0.1477 | 32.0 | 728 | 1.4237 | 0.6923 |
| 0.1299 | 32.97 | 750 | 1.4061 | 0.7115 |
| 0.1111 | 33.98 | 773 | 1.6664 | 0.6346 |
| 0.068 | 34.99 | 796 | 1.7432 | 0.6538 |
| 0.1142 | 36.0 | 819 | 1.4518 | 0.6923 |
| 0.1258 | 36.97 | 841 | 1.7217 | 0.6346 |
| 0.1055 | 37.98 | 864 | 1.6348 | 0.6154 |
| 0.1049 | 38.99 | 887 | 1.8378 | 0.6346 |
| 0.0822 | 40.0 | 910 | 1.6760 | 0.6731 |
| 0.1114 | 40.97 | 932 | 1.7310 | 0.6346 |
| 0.0704 | 41.98 | 955 | 1.7105 | 0.6538 |
| 0.0983 | 42.99 | 978 | 1.8320 | 0.5962 |
| 0.0909 | 44.0 | 1001 | 1.5632 | 0.6346 |
| 0.0991 | 44.97 | 1023 | 1.7606 | 0.6731 |
| 0.0658 | 45.98 | 1046 | 1.5927 | 0.6538 |
| 0.0412 | 46.99 | 1069 | 1.4660 | 0.6538 |
| 0.0919 | 48.0 | 1092 | 1.3294 | 0.6731 |
| 0.0726 | 48.97 | 1114 | 1.5551 | 0.6346 |
| 0.0554 | 49.98 | 1137 | 1.7157 | 0.6154 |
| 0.0585 | 50.99 | 1160 | 1.8280 | 0.5962 |
| 0.0607 | 52.0 | 1183 | 1.6142 | 0.6538 |
| 0.0719 | 52.97 | 1205 | 1.9924 | 0.5962 |
| 0.0877 | 53.98 | 1228 | 1.7806 | 0.6346 |
| 0.0743 | 54.99 | 1251 | 1.9820 | 0.6538 |
| 0.0464 | 56.0 | 1274 | 1.9449 | 0.6346 |
| 0.077 | 56.97 | 1296 | 1.6826 | 0.6923 |
| 0.073 | 57.98 | 1319 | 1.7594 | 0.6538 |
| 0.0623 | 58.99 | 1342 | 1.8303 | 0.6346 |
| 0.0383 | 60.0 | 1365 | 1.8124 | 0.6154 |
| 0.0526 | 60.97 | 1387 | 1.8164 | 0.6923 |
| 0.0679 | 61.98 | 1410 | 1.8586 | 0.6731 |
| 0.0625 | 62.99 | 1433 | 1.9150 | 0.6346 |
| 0.0482 | 64.0 | 1456 | 1.9622 | 0.6346 |
| 0.0646 | 64.97 | 1478 | 1.9476 | 0.6154 |
| 0.0594 | 65.98 | 1501 | 1.5958 | 0.6923 |
| 0.0568 | 66.99 | 1524 | 1.8275 | 0.6731 |
| 0.0662 | 68.0 | 1547 | 1.7576 | 0.6731 |
| 0.0428 | 68.97 | 1569 | 1.9325 | 0.6538 |
| 0.0433 | 69.98 | 1592 | 1.8206 | 0.6731 |
| 0.0511 | 70.99 | 1615 | 1.9029 | 0.6538 |
| 0.0502 | 72.0 | 1638 | 1.8821 | 0.6538 |
| 0.0544 | 72.97 | 1660 | 1.9535 | 0.6538 |
| 0.0399 | 73.98 | 1683 | 1.8455 | 0.6538 |
| 0.0561 | 74.99 | 1706 | 1.8290 | 0.6538 |
| 0.041 | 76.0 | 1729 | 1.8427 | 0.6538 |
| 0.0582 | 76.97 | 1751 | 1.8591 | 0.6538 |
| 0.0315 | 77.36 | 1760 | 1.8612 | 0.6538 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0