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# swin-tiny-patch4-window7-224-finetuned-eurosat_DATA6_20240305

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0731
- Accuracy: 0.9747

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0432        | 0.99  | 78   | 1.0627          | 0.4531   |
| 0.9424        | 2.0   | 157  | 1.0076          | 0.5103   |
| 0.8681        | 2.99  | 235  | 0.9070          | 0.5778   |
| 0.7533        | 4.0   | 314  | 0.7472          | 0.6731   |
| 0.655         | 4.99  | 392  | 0.6276          | 0.7339   |
| 0.6233        | 6.0   | 471  | 0.5866          | 0.7572   |
| 0.592         | 6.99  | 549  | 0.5434          | 0.7711   |
| 0.5502        | 8.0   | 628  | 0.4688          | 0.8045   |
| 0.5192        | 8.99  | 706  | 0.4532          | 0.8139   |
| 0.4923        | 10.0  | 785  | 0.4253          | 0.8303   |
| 0.487         | 10.99 | 863  | 0.4004          | 0.8365   |
| 0.432         | 12.0  | 942  | 0.3639          | 0.8531   |
| 0.4194        | 12.99 | 1020 | 0.3321          | 0.8675   |
| 0.4031        | 14.0  | 1099 | 0.3658          | 0.8545   |
| 0.3803        | 14.99 | 1177 | 0.2912          | 0.8861   |
| 0.3742        | 16.0  | 1256 | 0.2719          | 0.8906   |
| 0.3767        | 16.99 | 1334 | 0.2803          | 0.8874   |
| 0.3281        | 18.0  | 1413 | 0.2732          | 0.8975   |
| 0.313         | 18.99 | 1491 | 0.2466          | 0.9065   |
| 0.3147        | 20.0  | 1570 | 0.2308          | 0.9096   |
| 0.3053        | 20.99 | 1648 | 0.2295          | 0.9170   |
| 0.2784        | 22.0  | 1727 | 0.2241          | 0.9170   |
| 0.2788        | 22.99 | 1805 | 0.2084          | 0.9240   |
| 0.2665        | 24.0  | 1884 | 0.2074          | 0.9197   |
| 0.2528        | 24.99 | 1962 | 0.1779          | 0.9339   |
| 0.2518        | 26.0  | 2041 | 0.1782          | 0.9359   |
| 0.2502        | 26.99 | 2119 | 0.1645          | 0.9415   |
| 0.2387        | 28.0  | 2198 | 0.1856          | 0.9312   |
| 0.2459        | 28.99 | 2276 | 0.1496          | 0.9430   |
| 0.2122        | 30.0  | 2355 | 0.1503          | 0.9469   |
| 0.2139        | 30.99 | 2433 | 0.1598          | 0.9424   |
| 0.2065        | 32.0  | 2512 | 0.1484          | 0.9448   |
| 0.2052        | 32.99 | 2590 | 0.1613          | 0.9417   |
| 0.2104        | 34.0  | 2669 | 0.1353          | 0.9561   |
| 0.2068        | 34.99 | 2747 | 0.1461          | 0.9464   |
| 0.2009        | 36.0  | 2826 | 0.1307          | 0.9529   |
| 0.1862        | 36.99 | 2904 | 0.1375          | 0.9509   |
| 0.1925        | 38.0  | 2983 | 0.1216          | 0.9556   |
| 0.192         | 38.99 | 3061 | 0.1215          | 0.9549   |
| 0.1974        | 40.0  | 3140 | 0.1301          | 0.9587   |
| 0.1858        | 40.99 | 3218 | 0.1156          | 0.9565   |
| 0.1802        | 42.0  | 3297 | 0.1105          | 0.9635   |
| 0.1828        | 42.99 | 3375 | 0.1394          | 0.9565   |
| 0.1767        | 44.0  | 3454 | 0.1142          | 0.9612   |
| 0.1852        | 44.99 | 3532 | 0.1097          | 0.9630   |
| 0.1717        | 46.0  | 3611 | 0.1147          | 0.9596   |
| 0.1799        | 46.99 | 3689 | 0.1222          | 0.9610   |
| 0.166         | 48.0  | 3768 | 0.1133          | 0.9608   |
| 0.1638        | 48.99 | 3846 | 0.1058          | 0.9646   |
| 0.1524        | 50.0  | 3925 | 0.1045          | 0.9626   |
| 0.1581        | 50.99 | 4003 | 0.1065          | 0.9632   |
| 0.1616        | 52.0  | 4082 | 0.0996          | 0.9691   |
| 0.158         | 52.99 | 4160 | 0.1048          | 0.9648   |
| 0.1466        | 54.0  | 4239 | 0.1047          | 0.9639   |
| 0.1617        | 54.99 | 4317 | 0.0982          | 0.9650   |
| 0.1624        | 56.0  | 4396 | 0.1010          | 0.9643   |
| 0.1465        | 56.99 | 4474 | 0.0999          | 0.9657   |
| 0.1368        | 58.0  | 4553 | 0.0977          | 0.9648   |
| 0.1445        | 58.99 | 4631 | 0.1106          | 0.9630   |
| 0.1368        | 60.0  | 4710 | 0.0985          | 0.9661   |
| 0.1401        | 60.99 | 4788 | 0.0913          | 0.9686   |
| 0.1447        | 62.0  | 4867 | 0.0888          | 0.9688   |
| 0.1356        | 62.99 | 4945 | 0.0894          | 0.9704   |
| 0.1358        | 64.0  | 5024 | 0.0851          | 0.9700   |
| 0.1345        | 64.99 | 5102 | 0.0920          | 0.9709   |
| 0.1297        | 66.0  | 5181 | 0.0858          | 0.9704   |
| 0.134         | 66.99 | 5259 | 0.0875          | 0.9709   |
| 0.1252        | 68.0  | 5338 | 0.0954          | 0.9688   |
| 0.139         | 68.99 | 5416 | 0.0869          | 0.9733   |
| 0.1341        | 70.0  | 5495 | 0.0816          | 0.9691   |
| 0.1263        | 70.99 | 5573 | 0.0835          | 0.9726   |
| 0.1247        | 72.0  | 5652 | 0.0858          | 0.9686   |
| 0.122         | 72.99 | 5730 | 0.0891          | 0.9688   |
| 0.118         | 74.0  | 5809 | 0.0829          | 0.9715   |
| 0.1236        | 74.99 | 5887 | 0.0743          | 0.9751   |
| 0.1305        | 76.0  | 5966 | 0.0882          | 0.9715   |
| 0.1219        | 76.99 | 6044 | 0.0826          | 0.9722   |
| 0.1184        | 78.0  | 6123 | 0.0838          | 0.9722   |
| 0.124         | 78.99 | 6201 | 0.0819          | 0.9731   |
| 0.118         | 80.0  | 6280 | 0.0859          | 0.9709   |
| 0.1082        | 80.99 | 6358 | 0.0816          | 0.9726   |
| 0.1195        | 82.0  | 6437 | 0.0783          | 0.9722   |
| 0.1101        | 82.99 | 6515 | 0.0792          | 0.9747   |
| 0.1233        | 84.0  | 6594 | 0.0759          | 0.9753   |
| 0.1181        | 84.99 | 6672 | 0.0783          | 0.9722   |
| 0.1153        | 86.0  | 6751 | 0.0817          | 0.9715   |
| 0.1116        | 86.99 | 6829 | 0.0856          | 0.9733   |
| 0.1066        | 88.0  | 6908 | 0.0788          | 0.9758   |
| 0.1047        | 88.99 | 6986 | 0.0771          | 0.9735   |
| 0.1109        | 90.0  | 7065 | 0.0733          | 0.9744   |
| 0.1125        | 90.99 | 7143 | 0.0748          | 0.9747   |
| 0.1166        | 92.0  | 7222 | 0.0757          | 0.9738   |
| 0.1021        | 92.99 | 7300 | 0.0778          | 0.9740   |
| 0.1089        | 94.0  | 7379 | 0.0741          | 0.9753   |
| 0.1061        | 94.99 | 7457 | 0.0718          | 0.9765   |
| 0.1144        | 96.0  | 7536 | 0.0728          | 0.9747   |
| 0.1146        | 96.99 | 7614 | 0.0737          | 0.9747   |
| 0.1097        | 98.0  | 7693 | 0.0727          | 0.9749   |
| 0.1139        | 98.99 | 7771 | 0.0731          | 0.9747   |
| 0.1025        | 99.36 | 7800 | 0.0731          | 0.9747   |


### Framework versions

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

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