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# 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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