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---
library_name: transformers
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window16-256
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: swin-transformer-class
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-transformer-class
This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2549
- Accuracy: 0.4953
- F1: 0.4547
## 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: 0.0005
- 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:--------:|:----:|:---------------:|:--------:|:------:|
| 2.1381 | 0.9748 | 29 | 2.1103 | 0.2594 | 0.1420 |
| 1.9462 | 1.9832 | 59 | 1.8963 | 0.2783 | 0.1481 |
| 1.7299 | 2.9916 | 89 | 1.6978 | 0.3066 | 0.2504 |
| 1.6406 | 4.0 | 119 | 1.5954 | 0.3585 | 0.3221 |
| 1.5067 | 4.9748 | 148 | 1.5339 | 0.3915 | 0.3527 |
| 1.4566 | 5.9832 | 178 | 1.4972 | 0.4151 | 0.3769 |
| 1.4487 | 6.9916 | 208 | 1.4635 | 0.4387 | 0.3369 |
| 1.4335 | 8.0 | 238 | 1.4377 | 0.4481 | 0.3958 |
| 1.3974 | 8.9748 | 267 | 1.4213 | 0.4623 | 0.4066 |
| 1.3542 | 9.9832 | 297 | 1.4004 | 0.4575 | 0.4090 |
| 1.2964 | 10.9916 | 327 | 1.3880 | 0.4434 | 0.3832 |
| 1.3073 | 12.0 | 357 | 1.3716 | 0.4906 | 0.4449 |
| 1.3256 | 12.9748 | 386 | 1.3664 | 0.4528 | 0.4175 |
| 1.2867 | 13.9832 | 416 | 1.3622 | 0.4434 | 0.4033 |
| 1.3096 | 14.9916 | 446 | 1.3418 | 0.4764 | 0.4281 |
| 1.3012 | 16.0 | 476 | 1.3321 | 0.4528 | 0.4161 |
| 1.3086 | 16.9748 | 505 | 1.3248 | 0.4481 | 0.3578 |
| 1.2646 | 17.9832 | 535 | 1.3164 | 0.4717 | 0.4269 |
| 1.2647 | 18.9916 | 565 | 1.3140 | 0.4811 | 0.4394 |
| 1.2673 | 20.0 | 595 | 1.3073 | 0.4670 | 0.4311 |
| 1.2649 | 20.9748 | 624 | 1.2999 | 0.4906 | 0.4319 |
| 1.2721 | 21.9832 | 654 | 1.3007 | 0.4764 | 0.4236 |
| 1.317 | 22.9916 | 684 | 1.2982 | 0.4670 | 0.4167 |
| 1.2397 | 24.0 | 714 | 1.3031 | 0.4623 | 0.4115 |
| 1.209 | 24.9748 | 743 | 1.3075 | 0.4811 | 0.4379 |
| 1.1994 | 25.9832 | 773 | 1.3091 | 0.4245 | 0.3765 |
| 1.2695 | 26.9916 | 803 | 1.3017 | 0.4717 | 0.4362 |
| 1.2167 | 28.0 | 833 | 1.2986 | 0.4575 | 0.4153 |
| 1.234 | 28.9748 | 862 | 1.3082 | 0.4292 | 0.3773 |
| 1.2726 | 29.9832 | 892 | 1.3003 | 0.4670 | 0.4238 |
| 1.207 | 30.9916 | 922 | 1.2964 | 0.4670 | 0.4260 |
| 1.1534 | 32.0 | 952 | 1.3059 | 0.4292 | 0.3727 |
| 1.2477 | 32.9748 | 981 | 1.2924 | 0.4858 | 0.4397 |
| 1.2202 | 33.9832 | 1011 | 1.2924 | 0.4623 | 0.3850 |
| 1.2248 | 34.9916 | 1041 | 1.2969 | 0.4434 | 0.3680 |
| 1.1775 | 36.0 | 1071 | 1.2848 | 0.4953 | 0.4485 |
| 1.2401 | 36.9748 | 1100 | 1.2887 | 0.4575 | 0.4214 |
| 1.2311 | 37.9832 | 1130 | 1.2838 | 0.4858 | 0.4420 |
| 1.2143 | 38.9916 | 1160 | 1.2846 | 0.4906 | 0.4354 |
| 1.1548 | 40.0 | 1190 | 1.2828 | 0.4481 | 0.4057 |
| 1.1405 | 40.9748 | 1219 | 1.2878 | 0.4717 | 0.4356 |
| 1.1957 | 41.9832 | 1249 | 1.2839 | 0.4528 | 0.4063 |
| 1.211 | 42.9916 | 1279 | 1.2853 | 0.4670 | 0.4097 |
| 1.1849 | 44.0 | 1309 | 1.2779 | 0.4811 | 0.4360 |
| 1.1466 | 44.9748 | 1338 | 1.2765 | 0.4764 | 0.4341 |
| 1.1386 | 45.9832 | 1368 | 1.2836 | 0.4623 | 0.4184 |
| 1.2258 | 46.9916 | 1398 | 1.2718 | 0.4717 | 0.4293 |
| 1.2139 | 48.0 | 1428 | 1.2695 | 0.4906 | 0.4409 |
| 1.1938 | 48.9748 | 1457 | 1.2737 | 0.4764 | 0.4385 |
| 1.2171 | 49.9832 | 1487 | 1.2709 | 0.4670 | 0.4189 |
| 1.1804 | 50.9916 | 1517 | 1.2657 | 0.4764 | 0.4327 |
| 1.143 | 52.0 | 1547 | 1.2701 | 0.4764 | 0.4345 |
| 1.1723 | 52.9748 | 1576 | 1.2783 | 0.4717 | 0.4152 |
| 1.1454 | 53.9832 | 1606 | 1.2670 | 0.5047 | 0.4496 |
| 1.1957 | 54.9916 | 1636 | 1.2709 | 0.4670 | 0.4211 |
| 1.2383 | 56.0 | 1666 | 1.2752 | 0.4670 | 0.4136 |
| 1.1935 | 56.9748 | 1695 | 1.2670 | 0.4623 | 0.4201 |
| 1.159 | 57.9832 | 1725 | 1.2696 | 0.4717 | 0.4199 |
| 1.2267 | 58.9916 | 1755 | 1.2676 | 0.4858 | 0.4404 |
| 1.2047 | 60.0 | 1785 | 1.2659 | 0.4764 | 0.4336 |
| 1.1168 | 60.9748 | 1814 | 1.2680 | 0.4953 | 0.4466 |
| 1.2396 | 61.9832 | 1844 | 1.2741 | 0.4481 | 0.4045 |
| 1.1193 | 62.9916 | 1874 | 1.2791 | 0.4623 | 0.4184 |
| 1.1587 | 64.0 | 1904 | 1.2657 | 0.4858 | 0.4369 |
| 1.1492 | 64.9748 | 1933 | 1.2736 | 0.4717 | 0.4367 |
| 1.1303 | 65.9832 | 1963 | 1.2683 | 0.4811 | 0.4300 |
| 1.1672 | 66.9916 | 1993 | 1.2683 | 0.4953 | 0.4494 |
| 1.2035 | 68.0 | 2023 | 1.2667 | 0.4811 | 0.4447 |
| 1.1494 | 68.9748 | 2052 | 1.2645 | 0.4858 | 0.4476 |
| 1.1537 | 69.9832 | 2082 | 1.2714 | 0.4811 | 0.4434 |
| 1.18 | 70.9916 | 2112 | 1.2701 | 0.4811 | 0.4344 |
| 1.1386 | 72.0 | 2142 | 1.2688 | 0.4858 | 0.4440 |
| 1.1757 | 72.9748 | 2171 | 1.2694 | 0.4906 | 0.4514 |
| 1.1335 | 73.9832 | 2201 | 1.2712 | 0.4858 | 0.4419 |
| 1.1669 | 74.9916 | 2231 | 1.2701 | 0.5094 | 0.4651 |
| 1.1862 | 76.0 | 2261 | 1.2684 | 0.4764 | 0.4316 |
| 1.1695 | 76.9748 | 2290 | 1.2642 | 0.4906 | 0.4509 |
| 1.1317 | 77.9832 | 2320 | 1.2687 | 0.4811 | 0.4391 |
| 1.2023 | 78.9916 | 2350 | 1.2647 | 0.5 | 0.4579 |
| 1.1603 | 80.0 | 2380 | 1.2650 | 0.5 | 0.4596 |
| 1.1461 | 80.9748 | 2409 | 1.2623 | 0.4811 | 0.4396 |
| 1.1356 | 81.9832 | 2439 | 1.2621 | 0.4953 | 0.4449 |
| 1.1646 | 82.9916 | 2469 | 1.2713 | 0.4953 | 0.4526 |
| 1.152 | 84.0 | 2499 | 1.2661 | 0.5047 | 0.4632 |
| 1.0999 | 84.9748 | 2528 | 1.2685 | 0.5047 | 0.4576 |
| 1.1749 | 85.9832 | 2558 | 1.2716 | 0.4858 | 0.4459 |
| 1.1823 | 86.9916 | 2588 | 1.2624 | 0.4906 | 0.4441 |
| 1.1736 | 88.0 | 2618 | 1.2650 | 0.4811 | 0.4377 |
| 1.1565 | 88.9748 | 2647 | 1.2667 | 0.4670 | 0.4226 |
| 1.1565 | 89.9832 | 2677 | 1.2667 | 0.4953 | 0.4453 |
| 1.192 | 90.9916 | 2707 | 1.2634 | 0.5047 | 0.4635 |
| 1.1271 | 92.0 | 2737 | 1.2639 | 0.4764 | 0.4303 |
| 1.19 | 92.9748 | 2766 | 1.2631 | 0.4858 | 0.4412 |
| 1.1866 | 93.9832 | 2796 | 1.2616 | 0.4953 | 0.4555 |
| 1.0829 | 94.9916 | 2826 | 1.2586 | 0.4953 | 0.4522 |
| 1.1692 | 96.0 | 2856 | 1.2608 | 0.4906 | 0.4497 |
| 1.1503 | 96.9748 | 2885 | 1.2607 | 0.4953 | 0.4551 |
| 1.1263 | 97.9832 | 2915 | 1.2577 | 0.4953 | 0.4543 |
| 1.2199 | 98.9916 | 2945 | 1.2570 | 0.5047 | 0.4601 |
| 1.1347 | 100.0 | 2975 | 1.2555 | 0.4953 | 0.4503 |
| 1.1583 | 100.9748 | 3004 | 1.2557 | 0.5 | 0.4592 |
| 1.1697 | 101.9832 | 3034 | 1.2578 | 0.4858 | 0.4467 |
| 1.1918 | 102.9916 | 3064 | 1.2572 | 0.5047 | 0.4598 |
| 1.1959 | 104.0 | 3094 | 1.2563 | 0.5094 | 0.4649 |
| 1.2032 | 104.9748 | 3123 | 1.2551 | 0.4906 | 0.4480 |
| 1.2031 | 105.9832 | 3153 | 1.2552 | 0.4906 | 0.4491 |
| 1.1565 | 106.9916 | 3183 | 1.2544 | 0.5142 | 0.4668 |
| 1.1703 | 108.0 | 3213 | 1.2570 | 0.5 | 0.4598 |
| 1.2085 | 108.9748 | 3242 | 1.2550 | 0.5094 | 0.4639 |
| 1.1641 | 109.9832 | 3272 | 1.2578 | 0.4953 | 0.4551 |
| 1.1846 | 110.9916 | 3302 | 1.2579 | 0.4906 | 0.4510 |
| 1.1989 | 112.0 | 3332 | 1.2560 | 0.5 | 0.4579 |
| 1.111 | 112.9748 | 3361 | 1.2561 | 0.4953 | 0.4545 |
| 1.1703 | 113.9832 | 3391 | 1.2561 | 0.5047 | 0.4567 |
| 1.165 | 114.9916 | 3421 | 1.2567 | 0.5 | 0.4480 |
| 1.1295 | 116.0 | 3451 | 1.2582 | 0.4953 | 0.4475 |
| 1.1084 | 116.9748 | 3480 | 1.2574 | 0.5 | 0.4571 |
| 1.1577 | 117.9832 | 3510 | 1.2573 | 0.5047 | 0.4617 |
| 1.156 | 118.9916 | 3540 | 1.2565 | 0.4953 | 0.4559 |
| 1.1491 | 120.0 | 3570 | 1.2564 | 0.5 | 0.4573 |
| 1.1396 | 120.9748 | 3599 | 1.2572 | 0.5 | 0.4534 |
| 1.1545 | 121.9832 | 3629 | 1.2565 | 0.5 | 0.4604 |
| 1.1796 | 122.9916 | 3659 | 1.2563 | 0.5 | 0.4593 |
| 1.2012 | 124.0 | 3689 | 1.2559 | 0.4858 | 0.4454 |
| 1.1396 | 124.9748 | 3718 | 1.2567 | 0.4953 | 0.4555 |
| 1.1999 | 125.9832 | 3748 | 1.2558 | 0.4858 | 0.4450 |
| 1.1524 | 126.9916 | 3778 | 1.2569 | 0.4953 | 0.4554 |
| 1.2299 | 128.0 | 3808 | 1.2560 | 0.4953 | 0.4525 |
| 1.1548 | 128.9748 | 3837 | 1.2553 | 0.4764 | 0.4375 |
| 1.1869 | 129.9832 | 3867 | 1.2554 | 0.4811 | 0.4426 |
| 1.1891 | 130.9916 | 3897 | 1.2555 | 0.4811 | 0.4423 |
| 1.1353 | 132.0 | 3927 | 1.2565 | 0.4953 | 0.4554 |
| 1.1717 | 132.9748 | 3956 | 1.2569 | 0.5047 | 0.4643 |
| 1.1536 | 133.9832 | 3986 | 1.2556 | 0.5 | 0.4574 |
| 1.1667 | 134.9916 | 4016 | 1.2555 | 0.5 | 0.4594 |
| 1.1633 | 136.0 | 4046 | 1.2550 | 0.4953 | 0.4551 |
| 1.1646 | 136.9748 | 4075 | 1.2539 | 0.4858 | 0.4457 |
| 1.1618 | 137.9832 | 4105 | 1.2540 | 0.5047 | 0.4594 |
| 1.1581 | 138.9916 | 4135 | 1.2545 | 0.4858 | 0.4460 |
| 1.117 | 140.0 | 4165 | 1.2549 | 0.4858 | 0.4457 |
| 1.184 | 140.9748 | 4194 | 1.2552 | 0.4906 | 0.4504 |
| 1.1323 | 141.9832 | 4224 | 1.2553 | 0.4906 | 0.4504 |
| 1.1219 | 142.9916 | 4254 | 1.2550 | 0.4953 | 0.4547 |
| 1.1478 | 144.0 | 4284 | 1.2550 | 0.4953 | 0.4547 |
| 1.1177 | 144.9748 | 4313 | 1.2550 | 0.4953 | 0.4547 |
| 1.1326 | 145.9832 | 4343 | 1.2549 | 0.4953 | 0.4547 |
| 1.1392 | 146.2185 | 4350 | 1.2549 | 0.4953 | 0.4547 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
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