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

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0603
- Accuracy: 0.9816

## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0105        | 0.99  | 78   | 0.9788          | 0.5251   |
| 0.8749        | 2.0   | 157  | 0.8167          | 0.6323   |
| 0.7352        | 2.99  | 235  | 0.6498          | 0.7179   |
| 0.647         | 4.0   | 314  | 0.5619          | 0.7630   |
| 0.5949        | 4.99  | 392  | 0.5068          | 0.7892   |
| 0.5773        | 6.0   | 471  | 0.4673          | 0.8058   |
| 0.5401        | 6.99  | 549  | 0.4277          | 0.8211   |
| 0.5116        | 8.0   | 628  | 0.4089          | 0.8314   |
| 0.4751        | 8.99  | 706  | 0.3891          | 0.8399   |
| 0.4659        | 10.0  | 785  | 0.3599          | 0.8574   |
| 0.4457        | 10.99 | 863  | 0.3493          | 0.8592   |
| 0.4021        | 12.0  | 942  | 0.3180          | 0.8751   |
| 0.3925        | 12.99 | 1020 | 0.2824          | 0.8863   |
| 0.3737        | 14.0  | 1099 | 0.2686          | 0.8960   |
| 0.3573        | 14.99 | 1177 | 0.2509          | 0.8978   |
| 0.3463        | 16.0  | 1256 | 0.2214          | 0.9101   |
| 0.3174        | 16.99 | 1334 | 0.2233          | 0.9143   |
| 0.3029        | 18.0  | 1413 | 0.2065          | 0.9222   |
| 0.27          | 18.99 | 1491 | 0.1900          | 0.9287   |
| 0.2796        | 20.0  | 1570 | 0.1843          | 0.9294   |
| 0.2649        | 20.99 | 1648 | 0.1811          | 0.9291   |
| 0.2561        | 22.0  | 1727 | 0.1679          | 0.9345   |
| 0.2384        | 22.99 | 1805 | 0.1579          | 0.9401   |
| 0.2415        | 24.0  | 1884 | 0.1414          | 0.9462   |
| 0.2282        | 24.99 | 1962 | 0.1478          | 0.9439   |
| 0.2345        | 26.0  | 2041 | 0.1523          | 0.9419   |
| 0.2196        | 26.99 | 2119 | 0.1240          | 0.9529   |
| 0.2118        | 28.0  | 2198 | 0.1228          | 0.9502   |
| 0.1977        | 28.99 | 2276 | 0.1192          | 0.9565   |
| 0.1994        | 30.0  | 2355 | 0.1208          | 0.9529   |
| 0.1959        | 30.99 | 2433 | 0.1189          | 0.9563   |
| 0.1946        | 32.0  | 2512 | 0.1106          | 0.9617   |
| 0.1777        | 32.99 | 2590 | 0.1048          | 0.9587   |
| 0.1895        | 34.0  | 2669 | 0.0965          | 0.9643   |
| 0.1779        | 34.99 | 2747 | 0.1081          | 0.9623   |
| 0.1717        | 36.0  | 2826 | 0.1155          | 0.9590   |
| 0.1715        | 36.99 | 2904 | 0.1006          | 0.9630   |
| 0.1748        | 38.0  | 2983 | 0.0894          | 0.9695   |
| 0.1677        | 38.99 | 3061 | 0.1037          | 0.9639   |
| 0.1723        | 40.0  | 3140 | 0.1009          | 0.9637   |
| 0.1552        | 40.99 | 3218 | 0.0867          | 0.9697   |
| 0.1658        | 42.0  | 3297 | 0.0860          | 0.9702   |
| 0.1531        | 42.99 | 3375 | 0.0921          | 0.9673   |
| 0.1593        | 44.0  | 3454 | 0.0941          | 0.9646   |
| 0.1568        | 44.99 | 3532 | 0.0879          | 0.9686   |
| 0.1621        | 46.0  | 3611 | 0.0929          | 0.9693   |
| 0.1602        | 46.99 | 3689 | 0.0777          | 0.9744   |
| 0.1397        | 48.0  | 3768 | 0.0821          | 0.9733   |
| 0.1475        | 48.99 | 3846 | 0.0810          | 0.9711   |
| 0.1452        | 50.0  | 3925 | 0.0943          | 0.9697   |
| 0.148         | 50.99 | 4003 | 0.0943          | 0.9704   |
| 0.1392        | 52.0  | 4082 | 0.0869          | 0.9695   |
| 0.1389        | 52.99 | 4160 | 0.0876          | 0.9738   |
| 0.1295        | 54.0  | 4239 | 0.0716          | 0.9749   |
| 0.1394        | 54.99 | 4317 | 0.0760          | 0.9751   |
| 0.1354        | 56.0  | 4396 | 0.0771          | 0.9726   |
| 0.1303        | 56.99 | 4474 | 0.0738          | 0.9738   |
| 0.1274        | 58.0  | 4553 | 0.0834          | 0.9724   |
| 0.1276        | 58.99 | 4631 | 0.0801          | 0.9747   |
| 0.1372        | 60.0  | 4710 | 0.0667          | 0.9762   |
| 0.1417        | 60.99 | 4788 | 0.0747          | 0.9738   |
| 0.1249        | 62.0  | 4867 | 0.0735          | 0.9744   |
| 0.1212        | 62.99 | 4945 | 0.0665          | 0.9780   |
| 0.1218        | 64.0  | 5024 | 0.0680          | 0.9771   |
| 0.1193        | 64.99 | 5102 | 0.0679          | 0.9760   |
| 0.1148        | 66.0  | 5181 | 0.0685          | 0.9774   |
| 0.1242        | 66.99 | 5259 | 0.0647          | 0.9800   |
| 0.1167        | 68.0  | 5338 | 0.0646          | 0.9783   |
| 0.117         | 68.99 | 5416 | 0.0763          | 0.9765   |
| 0.1153        | 70.0  | 5495 | 0.0720          | 0.9753   |
| 0.12          | 70.99 | 5573 | 0.0717          | 0.9771   |
| 0.1054        | 72.0  | 5652 | 0.0677          | 0.9767   |
| 0.1183        | 72.99 | 5730 | 0.0741          | 0.9756   |
| 0.1082        | 74.0  | 5809 | 0.0676          | 0.9787   |
| 0.1088        | 74.99 | 5887 | 0.0700          | 0.9751   |
| 0.1125        | 76.0  | 5966 | 0.0663          | 0.9785   |
| 0.1099        | 76.99 | 6044 | 0.0622          | 0.9789   |
| 0.1128        | 78.0  | 6123 | 0.0660          | 0.9794   |
| 0.1178        | 78.99 | 6201 | 0.0699          | 0.9780   |
| 0.1129        | 80.0  | 6280 | 0.0605          | 0.9794   |
| 0.1013        | 80.99 | 6358 | 0.0694          | 0.9778   |
| 0.1078        | 82.0  | 6437 | 0.0652          | 0.9783   |
| 0.0994        | 82.99 | 6515 | 0.0604          | 0.9812   |
| 0.1093        | 84.0  | 6594 | 0.0600          | 0.9805   |
| 0.1039        | 84.99 | 6672 | 0.0646          | 0.9787   |
| 0.0963        | 86.0  | 6751 | 0.0654          | 0.9789   |
| 0.1053        | 86.99 | 6829 | 0.0627          | 0.9803   |
| 0.0982        | 88.0  | 6908 | 0.0619          | 0.9800   |
| 0.0944        | 88.99 | 6986 | 0.0607          | 0.9796   |
| 0.0959        | 90.0  | 7065 | 0.0661          | 0.9800   |
| 0.101         | 90.99 | 7143 | 0.0642          | 0.9809   |
| 0.1095        | 92.0  | 7222 | 0.0607          | 0.9807   |
| 0.1079        | 92.99 | 7300 | 0.0610          | 0.9803   |
| 0.1153        | 94.0  | 7379 | 0.0632          | 0.9798   |
| 0.1022        | 94.99 | 7457 | 0.0618          | 0.9812   |
| 0.1079        | 96.0  | 7536 | 0.0606          | 0.9809   |
| 0.0942        | 96.99 | 7614 | 0.0612          | 0.9800   |
| 0.0927        | 98.0  | 7693 | 0.0598          | 0.9809   |
| 0.1032        | 98.99 | 7771 | 0.0604          | 0.9814   |
| 0.0925        | 99.36 | 7800 | 0.0603          | 0.9816   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2

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