swinv2-tiny-patch4-window8-256-ve-U13-b-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: 0.7882
- Accuracy: 0.7391
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 | 1.3858 | 0.1304 |
| 1.3856 | 2.0 | 13 | 1.3777 | 0.3696 |
| 1.3856 | 2.92 | 19 | 1.3488 | 0.2391 |
| 1.361 | 4.0 | 26 | 1.2503 | 0.2826 |
| 1.2088 | 4.92 | 32 | 1.1317 | 0.4130 |
| 1.2088 | 6.0 | 39 | 1.0244 | 0.4565 |
| 1.0729 | 6.92 | 45 | 1.0413 | 0.4565 |
| 0.9554 | 8.0 | 52 | 0.9286 | 0.5652 |
| 0.9554 | 8.92 | 58 | 0.9103 | 0.5652 |
| 0.8221 | 10.0 | 65 | 0.8519 | 0.6522 |
| 0.732 | 10.92 | 71 | 0.8300 | 0.5870 |
| 0.732 | 12.0 | 78 | 0.8103 | 0.6304 |
| 0.6491 | 12.92 | 84 | 0.9533 | 0.5870 |
| 0.5724 | 14.0 | 91 | 0.7882 | 0.7391 |
| 0.5724 | 14.92 | 97 | 0.8072 | 0.6957 |
| 0.5305 | 16.0 | 104 | 0.7651 | 0.7391 |
| 0.4879 | 16.92 | 110 | 0.7379 | 0.7174 |
| 0.4879 | 18.0 | 117 | 0.7590 | 0.6739 |
| 0.4346 | 18.92 | 123 | 0.9283 | 0.6739 |
| 0.3671 | 20.0 | 130 | 1.0188 | 0.6304 |
| 0.3671 | 20.92 | 136 | 0.8959 | 0.7391 |
| 0.3725 | 22.0 | 143 | 0.9502 | 0.6957 |
| 0.3725 | 22.92 | 149 | 0.9627 | 0.6522 |
| 0.3321 | 24.0 | 156 | 0.9619 | 0.6957 |
| 0.3376 | 24.92 | 162 | 1.0459 | 0.6739 |
| 0.3376 | 26.0 | 169 | 1.0167 | 0.6522 |
| 0.3699 | 26.92 | 175 | 0.9949 | 0.6304 |
| 0.3098 | 28.0 | 182 | 0.9944 | 0.6739 |
| 0.3098 | 28.92 | 188 | 1.0860 | 0.6304 |
| 0.253 | 30.0 | 195 | 1.1721 | 0.6522 |
| 0.2615 | 30.92 | 201 | 1.1626 | 0.6739 |
| 0.2615 | 32.0 | 208 | 1.2464 | 0.6304 |
| 0.242 | 32.92 | 214 | 1.2179 | 0.6522 |
| 0.2173 | 34.0 | 221 | 1.2407 | 0.6304 |
| 0.2173 | 34.92 | 227 | 1.1585 | 0.6739 |
| 0.2305 | 36.0 | 234 | 1.3048 | 0.6522 |
| 0.2114 | 36.92 | 240 | 1.1776 | 0.6522 |
| 0.2114 | 38.0 | 247 | 1.1460 | 0.6522 |
| 0.2243 | 38.92 | 253 | 1.2424 | 0.6957 |
| 0.1822 | 40.0 | 260 | 1.2804 | 0.6739 |
| 0.1822 | 40.92 | 266 | 1.3472 | 0.6739 |
| 0.2065 | 42.0 | 273 | 1.3632 | 0.6739 |
| 0.2065 | 42.92 | 279 | 1.2832 | 0.6739 |
| 0.1942 | 44.0 | 286 | 1.3500 | 0.6739 |
| 0.1699 | 44.92 | 292 | 1.3242 | 0.6739 |
| 0.1699 | 46.0 | 299 | 1.3189 | 0.6957 |
| 0.1764 | 46.92 | 305 | 1.2840 | 0.6739 |
| 0.1771 | 48.0 | 312 | 1.3069 | 0.6957 |
| 0.1771 | 48.92 | 318 | 1.1585 | 0.6957 |
| 0.2095 | 50.0 | 325 | 1.3702 | 0.6957 |
| 0.1404 | 50.92 | 331 | 1.3539 | 0.6957 |
| 0.1404 | 52.0 | 338 | 1.3723 | 0.6957 |
| 0.1449 | 52.92 | 344 | 1.3877 | 0.6957 |
| 0.1348 | 54.0 | 351 | 1.3381 | 0.6739 |
| 0.1348 | 54.92 | 357 | 1.3700 | 0.6739 |
| 0.1683 | 56.0 | 364 | 1.2871 | 0.6957 |
| 0.1577 | 56.92 | 370 | 1.3214 | 0.6957 |
| 0.1577 | 58.0 | 377 | 1.3992 | 0.6522 |
| 0.1474 | 58.92 | 383 | 1.3800 | 0.6522 |
| 0.1267 | 60.0 | 390 | 1.2535 | 0.6739 |
| 0.1267 | 60.92 | 396 | 1.3200 | 0.6739 |
| 0.1171 | 62.0 | 403 | 1.3730 | 0.6739 |
| 0.1171 | 62.92 | 409 | 1.3678 | 0.6739 |
| 0.1461 | 64.0 | 416 | 1.3788 | 0.6739 |
| 0.1124 | 64.92 | 422 | 1.3944 | 0.6739 |
| 0.1124 | 66.0 | 429 | 1.3724 | 0.6739 |
| 0.1168 | 66.92 | 435 | 1.3553 | 0.6522 |
| 0.1243 | 68.0 | 442 | 1.3829 | 0.6739 |
| 0.1243 | 68.92 | 448 | 1.4040 | 0.6739 |
| 0.1375 | 70.0 | 455 | 1.4127 | 0.6522 |
| 0.1017 | 70.92 | 461 | 1.4070 | 0.6522 |
| 0.1017 | 72.0 | 468 | 1.3989 | 0.6739 |
| 0.1346 | 72.92 | 474 | 1.3995 | 0.6739 |
| 0.1382 | 73.85 | 480 | 1.3988 | 0.6739 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0