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# swin-tiny-patch4-window7-224-finetuned-wsdmhar

This model is a fine-tuned version of [nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat](https://huggingface.co/nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1444
- Accuracy: 0.9683

## 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0624        | 1.0   | 53   | 0.8879          | 0.6092   |
| 0.6893        | 2.0   | 106  | 0.6601          | 0.7090   |
| 0.6152        | 3.0   | 159  | 0.5114          | 0.7855   |
| 0.5456        | 4.0   | 212  | 0.3819          | 0.8423   |
| 0.4673        | 5.0   | 265  | 0.3267          | 0.8719   |
| 0.4166        | 6.0   | 318  | 0.2804          | 0.9039   |
| 0.3757        | 7.0   | 371  | 0.2881          | 0.8994   |
| 0.3798        | 8.0   | 424  | 0.2635          | 0.9032   |
| 0.3303        | 9.0   | 477  | 0.2703          | 0.9074   |
| 0.3346        | 10.0  | 530  | 0.2565          | 0.9005   |
| 0.2971        | 11.0  | 583  | 0.2182          | 0.9311   |
| 0.2992        | 12.0  | 636  | 0.2240          | 0.9256   |
| 0.2637        | 13.0  | 689  | 0.2131          | 0.9239   |
| 0.2653        | 14.0  | 742  | 0.1801          | 0.9397   |
| 0.2472        | 15.0  | 795  | 0.1807          | 0.9377   |
| 0.2263        | 16.0  | 848  | 0.1612          | 0.9466   |
| 0.1786        | 17.0  | 901  | 0.1735          | 0.9418   |
| 0.2103        | 18.0  | 954  | 0.1786          | 0.9463   |
| 0.1725        | 19.0  | 1007 | 0.1631          | 0.9473   |
| 0.1787        | 20.0  | 1060 | 0.1439          | 0.9532   |
| 0.1924        | 21.0  | 1113 | 0.1388          | 0.9504   |
| 0.1662        | 22.0  | 1166 | 0.1470          | 0.9508   |
| 0.1724        | 23.0  | 1219 | 0.1538          | 0.9497   |
| 0.1633        | 24.0  | 1272 | 0.1731          | 0.9384   |
| 0.174         | 25.0  | 1325 | 0.1555          | 0.9539   |
| 0.1657        | 26.0  | 1378 | 0.1542          | 0.9494   |
| 0.1513        | 27.0  | 1431 | 0.1526          | 0.9508   |
| 0.126         | 28.0  | 1484 | 0.1560          | 0.9511   |
| 0.1508        | 29.0  | 1537 | 0.1607          | 0.9480   |
| 0.1368        | 30.0  | 1590 | 0.1729          | 0.9435   |
| 0.1166        | 31.0  | 1643 | 0.1555          | 0.9532   |
| 0.1076        | 32.0  | 1696 | 0.1400          | 0.9580   |
| 0.1189        | 33.0  | 1749 | 0.1419          | 0.9590   |
| 0.1512        | 34.0  | 1802 | 0.1364          | 0.9580   |
| 0.1323        | 35.0  | 1855 | 0.1497          | 0.9539   |
| 0.1031        | 36.0  | 1908 | 0.1437          | 0.9580   |
| 0.1215        | 37.0  | 1961 | 0.1460          | 0.9559   |
| 0.1069        | 38.0  | 2014 | 0.1362          | 0.9601   |
| 0.129         | 39.0  | 2067 | 0.1490          | 0.9590   |
| 0.1202        | 40.0  | 2120 | 0.1616          | 0.9545   |
| 0.1011        | 41.0  | 2173 | 0.1518          | 0.9570   |
| 0.1092        | 42.0  | 2226 | 0.1308          | 0.9618   |
| 0.1163        | 43.0  | 2279 | 0.1458          | 0.9590   |
| 0.1074        | 44.0  | 2332 | 0.1414          | 0.9549   |
| 0.0814        | 45.0  | 2385 | 0.1509          | 0.9580   |
| 0.0985        | 46.0  | 2438 | 0.1287          | 0.9628   |
| 0.0863        | 47.0  | 2491 | 0.1277          | 0.9625   |
| 0.0932        | 48.0  | 2544 | 0.1453          | 0.9559   |
| 0.0863        | 49.0  | 2597 | 0.1520          | 0.9566   |
| 0.0887        | 50.0  | 2650 | 0.1279          | 0.9656   |
| 0.0744        | 51.0  | 2703 | 0.1552          | 0.9566   |
| 0.0928        | 52.0  | 2756 | 0.1465          | 0.9621   |
| 0.0776        | 53.0  | 2809 | 0.1575          | 0.9583   |
| 0.088         | 54.0  | 2862 | 0.1614          | 0.9563   |
| 0.0909        | 55.0  | 2915 | 0.1312          | 0.9638   |
| 0.089         | 56.0  | 2968 | 0.1357          | 0.9652   |
| 0.0587        | 57.0  | 3021 | 0.1510          | 0.9614   |
| 0.0931        | 58.0  | 3074 | 0.1466          | 0.9580   |
| 0.0878        | 59.0  | 3127 | 0.1499          | 0.9590   |
| 0.0725        | 60.0  | 3180 | 0.1524          | 0.9597   |
| 0.0543        | 61.0  | 3233 | 0.1543          | 0.9583   |
| 0.0773        | 62.0  | 3286 | 0.1513          | 0.9635   |
| 0.0626        | 63.0  | 3339 | 0.1511          | 0.9601   |
| 0.0649        | 64.0  | 3392 | 0.1467          | 0.9594   |
| 0.0705        | 65.0  | 3445 | 0.1443          | 0.9590   |
| 0.0737        | 66.0  | 3498 | 0.1361          | 0.9607   |
| 0.0518        | 67.0  | 3551 | 0.1441          | 0.9594   |
| 0.0502        | 68.0  | 3604 | 0.1535          | 0.9590   |
| 0.0701        | 69.0  | 3657 | 0.1362          | 0.9663   |
| 0.0826        | 70.0  | 3710 | 0.1492          | 0.9611   |
| 0.0715        | 71.0  | 3763 | 0.1615          | 0.9625   |
| 0.0635        | 72.0  | 3816 | 0.1488          | 0.9642   |
| 0.0522        | 73.0  | 3869 | 0.1456          | 0.9621   |
| 0.0485        | 74.0  | 3922 | 0.1386          | 0.9645   |
| 0.0629        | 75.0  | 3975 | 0.1463          | 0.9632   |
| 0.0568        | 76.0  | 4028 | 0.1472          | 0.9621   |
| 0.0556        | 77.0  | 4081 | 0.1440          | 0.9659   |
| 0.0547        | 78.0  | 4134 | 0.1421          | 0.9635   |
| 0.0527        | 79.0  | 4187 | 0.1444          | 0.9683   |
| 0.054         | 80.0  | 4240 | 0.1464          | 0.9628   |
| 0.0641        | 81.0  | 4293 | 0.1491          | 0.9635   |
| 0.0546        | 82.0  | 4346 | 0.1529          | 0.9611   |
| 0.059         | 83.0  | 4399 | 0.1462          | 0.9652   |
| 0.0485        | 84.0  | 4452 | 0.1567          | 0.9632   |
| 0.0388        | 85.0  | 4505 | 0.1548          | 0.9621   |
| 0.0421        | 86.0  | 4558 | 0.1484          | 0.9621   |
| 0.0375        | 87.0  | 4611 | 0.1681          | 0.9597   |
| 0.0376        | 88.0  | 4664 | 0.1513          | 0.9632   |
| 0.0514        | 89.0  | 4717 | 0.1485          | 0.9642   |
| 0.0598        | 90.0  | 4770 | 0.1541          | 0.9638   |
| 0.0431        | 91.0  | 4823 | 0.1474          | 0.9628   |
| 0.0432        | 92.0  | 4876 | 0.1498          | 0.9645   |
| 0.0391        | 93.0  | 4929 | 0.1506          | 0.9645   |
| 0.0408        | 94.0  | 4982 | 0.1462          | 0.9642   |
| 0.0335        | 95.0  | 5035 | 0.1509          | 0.9652   |
| 0.0447        | 96.0  | 5088 | 0.1508          | 0.9635   |
| 0.0477        | 97.0  | 5141 | 0.1510          | 0.9635   |
| 0.0504        | 98.0  | 5194 | 0.1510          | 0.9642   |
| 0.0406        | 99.0  | 5247 | 0.1479          | 0.9649   |
| 0.0343        | 100.0 | 5300 | 0.1480          | 0.9645   |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1

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