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# swin-tiny-patch4-window7-224-finetuned-eurosat_DATA7_20240411

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0669
- Accuracy: 0.9784

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9517        | 1.0   | 365   | 0.9502          | 0.5231   |
| 0.7427        | 2.0   | 731   | 0.7194          | 0.6734   |
| 0.6384        | 3.0   | 1096  | 0.6085          | 0.7290   |
| 0.6007        | 4.0   | 1462  | 0.5564          | 0.7512   |
| 0.606         | 5.0   | 1827  | 0.5444          | 0.7606   |
| 0.5432        | 6.0   | 2193  | 0.4737          | 0.7991   |
| 0.5239        | 7.0   | 2558  | 0.4484          | 0.8048   |
| 0.4525        | 8.0   | 2924  | 0.3606          | 0.8487   |
| 0.4318        | 9.0   | 3289  | 0.3628          | 0.8514   |
| 0.4213        | 10.0  | 3655  | 0.2967          | 0.8759   |
| 0.3846        | 11.0  | 4020  | 0.2828          | 0.8880   |
| 0.3833        | 12.0  | 4386  | 0.2494          | 0.9011   |
| 0.3671        | 13.0  | 4751  | 0.2430          | 0.9028   |
| 0.3458        | 14.0  | 5117  | 0.2222          | 0.9107   |
| 0.3049        | 15.0  | 5482  | 0.2041          | 0.9190   |
| 0.3198        | 16.0  | 5848  | 0.1761          | 0.9338   |
| 0.2648        | 17.0  | 6213  | 0.1772          | 0.9348   |
| 0.2517        | 18.0  | 6579  | 0.1846          | 0.9303   |
| 0.251         | 19.0  | 6944  | 0.1848          | 0.9299   |
| 0.2351        | 20.0  | 7310  | 0.1448          | 0.9448   |
| 0.2604        | 21.0  | 7675  | 0.1467          | 0.9425   |
| 0.245         | 22.0  | 8041  | 0.1292          | 0.9503   |
| 0.2095        | 23.0  | 8406  | 0.1381          | 0.9490   |
| 0.2022        | 24.0  | 8772  | 0.1542          | 0.9469   |
| 0.2041        | 25.0  | 9137  | 0.1229          | 0.9563   |
| 0.1899        | 26.0  | 9503  | 0.1225          | 0.9557   |
| 0.167         | 27.0  | 9868  | 0.1137          | 0.9588   |
| 0.2101        | 28.0  | 10234 | 0.0978          | 0.9646   |
| 0.2187        | 29.0  | 10599 | 0.1138          | 0.9588   |
| 0.1875        | 30.0  | 10965 | 0.1256          | 0.9517   |
| 0.1807        | 31.0  | 11330 | 0.1043          | 0.9625   |
| 0.1874        | 32.0  | 11696 | 0.1147          | 0.9613   |
| 0.1863        | 33.0  | 12061 | 0.1035          | 0.9627   |
| 0.1607        | 34.0  | 12427 | 0.0920          | 0.9652   |
| 0.1597        | 35.0  | 12792 | 0.1021          | 0.9617   |
| 0.1643        | 36.0  | 13158 | 0.1007          | 0.9638   |
| 0.1858        | 37.0  | 13523 | 0.0987          | 0.9667   |
| 0.152         | 38.0  | 13889 | 0.1038          | 0.9652   |
| 0.1622        | 39.0  | 14254 | 0.0964          | 0.9665   |
| 0.1598        | 40.0  | 14620 | 0.0976          | 0.9679   |
| 0.1438        | 41.0  | 14985 | 0.0918          | 0.9675   |
| 0.1355        | 42.0  | 15351 | 0.0884          | 0.9694   |
| 0.1563        | 43.0  | 15716 | 0.0905          | 0.9679   |
| 0.1319        | 44.0  | 16082 | 0.1046          | 0.9675   |
| 0.1613        | 45.0  | 16447 | 0.0792          | 0.9711   |
| 0.1454        | 46.0  | 16813 | 0.0872          | 0.9690   |
| 0.1166        | 47.0  | 17178 | 0.0857          | 0.9700   |
| 0.1297        | 48.0  | 17544 | 0.0779          | 0.9734   |
| 0.1328        | 49.0  | 17909 | 0.0792          | 0.9738   |
| 0.1212        | 50.0  | 18275 | 0.0736          | 0.9731   |
| 0.1275        | 51.0  | 18640 | 0.0900          | 0.9684   |
| 0.0988        | 52.0  | 19006 | 0.0739          | 0.9732   |
| 0.126         | 53.0  | 19371 | 0.0854          | 0.9721   |
| 0.138         | 54.0  | 19737 | 0.0858          | 0.9715   |
| 0.1169        | 55.0  | 20102 | 0.0770          | 0.9740   |
| 0.1328        | 56.0  | 20468 | 0.0780          | 0.9759   |
| 0.1231        | 57.0  | 20833 | 0.0770          | 0.9734   |
| 0.128         | 58.0  | 21199 | 0.0692          | 0.9738   |
| 0.1233        | 59.0  | 21564 | 0.0763          | 0.9721   |
| 0.1131        | 60.0  | 21930 | 0.0695          | 0.9754   |
| 0.1032        | 61.0  | 22295 | 0.0795          | 0.9731   |
| 0.146         | 62.0  | 22661 | 0.0702          | 0.9752   |
| 0.1117        | 63.0  | 23026 | 0.0725          | 0.9754   |
| 0.1137        | 64.0  | 23392 | 0.0710          | 0.9767   |
| 0.1083        | 65.0  | 23757 | 0.0764          | 0.9738   |
| 0.1053        | 66.0  | 24123 | 0.0712          | 0.9732   |
| 0.1193        | 67.0  | 24488 | 0.0813          | 0.9729   |
| 0.0901        | 68.0  | 24854 | 0.0758          | 0.9752   |
| 0.1142        | 69.0  | 25219 | 0.0672          | 0.9758   |
| 0.1166        | 70.0  | 25585 | 0.0803          | 0.9754   |
| 0.0835        | 71.0  | 25950 | 0.0791          | 0.9727   |
| 0.0989        | 72.0  | 26316 | 0.0801          | 0.9731   |
| 0.0952        | 73.0  | 26681 | 0.0717          | 0.9754   |
| 0.0945        | 74.0  | 27047 | 0.0666          | 0.9761   |
| 0.0944        | 75.0  | 27412 | 0.0703          | 0.9752   |
| 0.1092        | 76.0  | 27778 | 0.0624          | 0.9773   |
| 0.0976        | 77.0  | 28143 | 0.0637          | 0.9773   |
| 0.1008        | 78.0  | 28509 | 0.0744          | 0.9744   |
| 0.0755        | 79.0  | 28874 | 0.0694          | 0.9767   |
| 0.1023        | 80.0  | 29240 | 0.0800          | 0.9752   |
| 0.0987        | 81.0  | 29605 | 0.0773          | 0.9744   |
| 0.0806        | 82.0  | 29971 | 0.0689          | 0.9773   |
| 0.1019        | 83.0  | 30336 | 0.0749          | 0.9767   |
| 0.0994        | 84.0  | 30702 | 0.0696          | 0.9765   |
| 0.0897        | 85.0  | 31067 | 0.0698          | 0.9781   |
| 0.0636        | 86.0  | 31433 | 0.0672          | 0.9775   |
| 0.098         | 87.0  | 31798 | 0.0756          | 0.9767   |
| 0.0955        | 88.0  | 32164 | 0.0719          | 0.9771   |
| 0.0786        | 89.0  | 32529 | 0.0726          | 0.9771   |
| 0.0821        | 90.0  | 32895 | 0.0696          | 0.9769   |
| 0.0844        | 91.0  | 33260 | 0.0691          | 0.9771   |
| 0.0926        | 92.0  | 33626 | 0.0676          | 0.9767   |
| 0.0826        | 93.0  | 33991 | 0.0705          | 0.9769   |
| 0.0923        | 94.0  | 34357 | 0.0686          | 0.9771   |
| 0.0637        | 95.0  | 34722 | 0.0672          | 0.9781   |
| 0.0953        | 96.0  | 35088 | 0.0661          | 0.9786   |
| 0.0922        | 97.0  | 35453 | 0.0701          | 0.9777   |
| 0.0766        | 98.0  | 35819 | 0.0676          | 0.9779   |
| 0.0782        | 99.0  | 36184 | 0.0656          | 0.9783   |
| 0.0845        | 99.86 | 36500 | 0.0669          | 0.9784   |


### Framework versions

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

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