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# swin-tiny-patch4-window7-224-finetuned-eurosat_DATA7_20240410

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0817
- Accuracy: 0.9731

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.98          | 1.0   | 182   | 1.0130          | 0.4827   |
| 0.843         | 2.0   | 365   | 0.8658          | 0.5978   |
| 0.742         | 3.0   | 548   | 0.7068          | 0.6848   |
| 0.69          | 4.0   | 731   | 0.6297          | 0.7254   |
| 0.6413        | 5.0   | 913   | 0.5716          | 0.7571   |
| 0.611         | 6.0   | 1096  | 0.5445          | 0.7671   |
| 0.5844        | 7.0   | 1279  | 0.5181          | 0.7802   |
| 0.5616        | 8.0   | 1462  | 0.4757          | 0.7985   |
| 0.5634        | 9.0   | 1644  | 0.5245          | 0.7896   |
| 0.5223        | 10.0  | 1827  | 0.4991          | 0.7902   |
| 0.4723        | 11.0  | 2010  | 0.4363          | 0.8216   |
| 0.4443        | 12.0  | 2193  | 0.3813          | 0.8403   |
| 0.4538        | 13.0  | 2375  | 0.3500          | 0.8574   |
| 0.4273        | 14.0  | 2558  | 0.3326          | 0.8624   |
| 0.4247        | 15.0  | 2741  | 0.3224          | 0.8684   |
| 0.4063        | 16.0  | 2924  | 0.3096          | 0.8707   |
| 0.367         | 17.0  | 3106  | 0.2713          | 0.8945   |
| 0.3605        | 18.0  | 3289  | 0.3160          | 0.8755   |
| 0.3475        | 19.0  | 3472  | 0.2559          | 0.8995   |
| 0.3262        | 20.0  | 3655  | 0.2437          | 0.9030   |
| 0.3218        | 21.0  | 3837  | 0.2343          | 0.9090   |
| 0.3125        | 22.0  | 4020  | 0.2267          | 0.9113   |
| 0.336         | 23.0  | 4203  | 0.2170          | 0.9138   |
| 0.2813        | 24.0  | 4386  | 0.2062          | 0.9199   |
| 0.2802        | 25.0  | 4568  | 0.1956          | 0.9196   |
| 0.2996        | 26.0  | 4751  | 0.1923          | 0.9244   |
| 0.2699        | 27.0  | 4934  | 0.1934          | 0.9273   |
| 0.2642        | 28.0  | 5117  | 0.1973          | 0.9242   |
| 0.2491        | 29.0  | 5299  | 0.1686          | 0.9394   |
| 0.2611        | 30.0  | 5482  | 0.1793          | 0.9326   |
| 0.2383        | 31.0  | 5665  | 0.1744          | 0.9332   |
| 0.2338        | 32.0  | 5848  | 0.1537          | 0.9448   |
| 0.2225        | 33.0  | 6030  | 0.1569          | 0.9405   |
| 0.2383        | 34.0  | 6213  | 0.1422          | 0.9480   |
| 0.2253        | 35.0  | 6396  | 0.1413          | 0.9455   |
| 0.2257        | 36.0  | 6579  | 0.1535          | 0.9442   |
| 0.2308        | 37.0  | 6761  | 0.1655          | 0.9423   |
| 0.2241        | 38.0  | 6944  | 0.1272          | 0.9530   |
| 0.2253        | 39.0  | 7127  | 0.1464          | 0.9440   |
| 0.1996        | 40.0  | 7310  | 0.1332          | 0.9527   |
| 0.225         | 41.0  | 7492  | 0.1311          | 0.9530   |
| 0.1918        | 42.0  | 7675  | 0.1546          | 0.9459   |
| 0.1937        | 43.0  | 7858  | 0.1388          | 0.9515   |
| 0.2043        | 44.0  | 8041  | 0.1185          | 0.9596   |
| 0.1802        | 45.0  | 8223  | 0.1195          | 0.9557   |
| 0.1821        | 46.0  | 8406  | 0.1152          | 0.9604   |
| 0.1712        | 47.0  | 8589  | 0.1273          | 0.9575   |
| 0.1865        | 48.0  | 8772  | 0.1209          | 0.9565   |
| 0.1706        | 49.0  | 8954  | 0.1057          | 0.9611   |
| 0.1817        | 50.0  | 9137  | 0.1114          | 0.9602   |
| 0.1753        | 51.0  | 9320  | 0.1114          | 0.9621   |
| 0.1826        | 52.0  | 9503  | 0.1055          | 0.9634   |
| 0.178         | 53.0  | 9685  | 0.1069          | 0.9644   |
| 0.1522        | 54.0  | 9868  | 0.1036          | 0.9625   |
| 0.171         | 55.0  | 10051 | 0.1068          | 0.9619   |
| 0.1656        | 56.0  | 10234 | 0.0925          | 0.9644   |
| 0.163         | 57.0  | 10416 | 0.0939          | 0.9677   |
| 0.1631        | 58.0  | 10599 | 0.1130          | 0.9592   |
| 0.169         | 59.0  | 10782 | 0.0907          | 0.9671   |
| 0.1491        | 60.0  | 10965 | 0.1055          | 0.9632   |
| 0.1572        | 61.0  | 11147 | 0.0940          | 0.9638   |
| 0.1617        | 62.0  | 11330 | 0.1008          | 0.9636   |
| 0.1584        | 63.0  | 11513 | 0.0989          | 0.9673   |
| 0.1597        | 64.0  | 11696 | 0.1026          | 0.9648   |
| 0.1466        | 65.0  | 11878 | 0.1008          | 0.9665   |
| 0.1468        | 66.0  | 12061 | 0.0947          | 0.9644   |
| 0.1562        | 67.0  | 12244 | 0.0864          | 0.9707   |
| 0.1589        | 68.0  | 12427 | 0.0980          | 0.9656   |
| 0.1505        | 69.0  | 12609 | 0.0908          | 0.9681   |
| 0.1497        | 70.0  | 12792 | 0.0879          | 0.9690   |
| 0.1362        | 71.0  | 12975 | 0.0864          | 0.9700   |
| 0.1418        | 72.0  | 13158 | 0.0949          | 0.9684   |
| 0.1345        | 73.0  | 13340 | 0.0994          | 0.9681   |
| 0.1333        | 74.0  | 13523 | 0.0859          | 0.9700   |
| 0.1414        | 75.0  | 13706 | 0.0912          | 0.9692   |
| 0.137         | 76.0  | 13889 | 0.0863          | 0.9719   |
| 0.1326        | 77.0  | 14071 | 0.0811          | 0.9707   |
| 0.1429        | 78.0  | 14254 | 0.0875          | 0.9690   |
| 0.1363        | 79.0  | 14437 | 0.0909          | 0.9690   |
| 0.1344        | 80.0  | 14620 | 0.0913          | 0.9692   |
| 0.1221        | 81.0  | 14802 | 0.0908          | 0.9706   |
| 0.1192        | 82.0  | 14985 | 0.0835          | 0.9715   |
| 0.1252        | 83.0  | 15168 | 0.0865          | 0.9711   |
| 0.1404        | 84.0  | 15351 | 0.0922          | 0.9700   |
| 0.124         | 85.0  | 15533 | 0.0845          | 0.9700   |
| 0.1278        | 86.0  | 15716 | 0.0859          | 0.9721   |
| 0.1271        | 87.0  | 15899 | 0.0835          | 0.9725   |
| 0.1254        | 88.0  | 16082 | 0.0843          | 0.9721   |
| 0.1363        | 89.0  | 16264 | 0.0852          | 0.9707   |
| 0.1144        | 90.0  | 16447 | 0.0846          | 0.9729   |
| 0.1217        | 91.0  | 16630 | 0.0822          | 0.9729   |
| 0.1185        | 92.0  | 16813 | 0.0818          | 0.9731   |
| 0.1095        | 93.0  | 16995 | 0.0825          | 0.9725   |
| 0.1181        | 94.0  | 17178 | 0.0811          | 0.9729   |
| 0.1191        | 95.0  | 17361 | 0.0839          | 0.9736   |
| 0.1107        | 96.0  | 17544 | 0.0825          | 0.9729   |
| 0.1093        | 97.0  | 17726 | 0.0825          | 0.9734   |
| 0.1187        | 98.0  | 17909 | 0.0811          | 0.9731   |
| 0.1302        | 99.0  | 18092 | 0.0823          | 0.9727   |
| 0.1146        | 99.59 | 18200 | 0.0817          | 0.9731   |


### Framework versions

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

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