swinv2-tiny-patch4-window8-256-ve-U13-b-80b
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: 0.7057
- 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: 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: 0.1
- num_epochs: 80
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.92 | 6 | 1.3861 | 0.1304 |
| 1.386 | 2.0 | 13 | 1.3837 | 0.4348 |
| 1.386 | 2.92 | 19 | 1.3776 | 0.3043 |
| 1.3807 | 4.0 | 26 | 1.3570 | 0.2391 |
| 1.3386 | 4.92 | 32 | 1.3224 | 0.2174 |
| 1.3386 | 6.0 | 39 | 1.2085 | 0.3478 |
| 1.209 | 6.92 | 45 | 1.1056 | 0.4565 |
| 1.0561 | 8.0 | 52 | 1.0507 | 0.4783 |
| 1.0561 | 8.92 | 58 | 1.0161 | 0.4565 |
| 0.9157 | 10.0 | 65 | 0.8613 | 0.6304 |
| 0.8002 | 10.92 | 71 | 0.9073 | 0.5652 |
| 0.8002 | 12.0 | 78 | 0.8300 | 0.6304 |
| 0.7181 | 12.92 | 84 | 0.8958 | 0.5870 |
| 0.6405 | 14.0 | 91 | 0.8075 | 0.7174 |
| 0.6405 | 14.92 | 97 | 0.7478 | 0.6957 |
| 0.6064 | 16.0 | 104 | 0.7370 | 0.7174 |
| 0.5556 | 16.92 | 110 | 0.7057 | 0.7391 |
| 0.5556 | 18.0 | 117 | 0.7395 | 0.6522 |
| 0.4822 | 18.92 | 123 | 0.8734 | 0.6957 |
| 0.4241 | 20.0 | 130 | 0.9991 | 0.6739 |
| 0.4241 | 20.92 | 136 | 0.8416 | 0.7174 |
| 0.4307 | 22.0 | 143 | 0.9195 | 0.6957 |
| 0.4307 | 22.92 | 149 | 0.9211 | 0.6522 |
| 0.381 | 24.0 | 156 | 0.9683 | 0.6087 |
| 0.3707 | 24.92 | 162 | 1.0067 | 0.6739 |
| 0.3707 | 26.0 | 169 | 0.9793 | 0.6522 |
| 0.3918 | 26.92 | 175 | 0.9758 | 0.6739 |
| 0.3513 | 28.0 | 182 | 0.9761 | 0.6739 |
| 0.3513 | 28.92 | 188 | 1.0745 | 0.6304 |
| 0.2739 | 30.0 | 195 | 1.0775 | 0.6739 |
| 0.2882 | 30.92 | 201 | 1.1521 | 0.6739 |
| 0.2882 | 32.0 | 208 | 1.2072 | 0.6522 |
| 0.2588 | 32.92 | 214 | 1.1374 | 0.6739 |
| 0.2498 | 34.0 | 221 | 1.2131 | 0.6522 |
| 0.2498 | 34.92 | 227 | 1.1309 | 0.7391 |
| 0.2584 | 36.0 | 234 | 1.2828 | 0.6957 |
| 0.2228 | 36.92 | 240 | 1.1381 | 0.6739 |
| 0.2228 | 38.0 | 247 | 1.2116 | 0.6522 |
| 0.2408 | 38.92 | 253 | 1.1962 | 0.6739 |
| 0.2042 | 40.0 | 260 | 1.2557 | 0.6739 |
| 0.2042 | 40.92 | 266 | 1.3511 | 0.6739 |
| 0.2141 | 42.0 | 273 | 1.3636 | 0.6304 |
| 0.2141 | 42.92 | 279 | 1.3084 | 0.6304 |
| 0.2135 | 44.0 | 286 | 1.3847 | 0.6087 |
| 0.191 | 44.92 | 292 | 1.2408 | 0.6957 |
| 0.191 | 46.0 | 299 | 1.1750 | 0.7174 |
| 0.1833 | 46.92 | 305 | 1.1804 | 0.6957 |
| 0.189 | 48.0 | 312 | 1.1867 | 0.7174 |
| 0.189 | 48.92 | 318 | 1.0623 | 0.7391 |
| 0.2196 | 50.0 | 325 | 1.2626 | 0.6957 |
| 0.1505 | 50.92 | 331 | 1.2745 | 0.6957 |
| 0.1505 | 52.0 | 338 | 1.3473 | 0.6957 |
| 0.1604 | 52.92 | 344 | 1.3535 | 0.6522 |
| 0.1377 | 54.0 | 351 | 1.3873 | 0.6522 |
| 0.1377 | 54.92 | 357 | 1.4287 | 0.6522 |
| 0.1752 | 56.0 | 364 | 1.3014 | 0.6957 |
| 0.1684 | 56.92 | 370 | 1.3564 | 0.6739 |
| 0.1684 | 58.0 | 377 | 1.4165 | 0.6957 |
| 0.1597 | 58.92 | 383 | 1.3624 | 0.6739 |
| 0.1393 | 60.0 | 390 | 1.3018 | 0.6957 |
| 0.1393 | 60.92 | 396 | 1.3197 | 0.6739 |
| 0.1347 | 62.0 | 403 | 1.3542 | 0.6739 |
| 0.1347 | 62.92 | 409 | 1.3460 | 0.6739 |
| 0.155 | 64.0 | 416 | 1.3998 | 0.6739 |
| 0.1198 | 64.92 | 422 | 1.3982 | 0.6739 |
| 0.1198 | 66.0 | 429 | 1.3989 | 0.6522 |
| 0.1318 | 66.92 | 435 | 1.4035 | 0.6522 |
| 0.1382 | 68.0 | 442 | 1.3626 | 0.6522 |
| 0.1382 | 68.92 | 448 | 1.3714 | 0.6522 |
| 0.1451 | 70.0 | 455 | 1.4174 | 0.6739 |
| 0.1203 | 70.92 | 461 | 1.4343 | 0.6739 |
| 0.1203 | 72.0 | 468 | 1.4045 | 0.6522 |
| 0.141 | 72.92 | 474 | 1.3904 | 0.6522 |
| 0.1516 | 73.85 | 480 | 1.3849 | 0.6522 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0