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

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1792
- Accuracy: 0.4627
- F1: 0.1004
- Precision: 0.2132
- Recall: 0.0852
- Auc Roc: 0.7403

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auc Roc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.2828        | 1.0     | 82   | 0.2357          | 0.3645   | 0.0412 | 0.0410    | 0.0414 | 0.5445  |
| 0.2048        | 2.0     | 164  | 0.1922          | 0.5713   | 0.0492 | 0.0396    | 0.0648 | 0.5762  |
| 0.2067        | 3.0     | 246  | 0.1899          | 0.4824   | 0.0493 | 0.1107    | 0.0550 | 0.6240  |
| 0.1974        | 4.0     | 328  | 0.1870          | 0.4079   | 0.0474 | 0.0485    | 0.0463 | 0.6619  |
| 0.2021        | 5.0     | 410  | 0.2048          | 0.1612   | 0.0358 | 0.1222    | 0.0227 | 0.6841  |
| 0.1843        | 6.0     | 492  | 0.1838          | 0.3709   | 0.0625 | 0.0967    | 0.0526 | 0.6965  |
| 0.187         | 7.0     | 574  | 0.1800          | 0.4009   | 0.0610 | 0.0963    | 0.0535 | 0.7127  |
| 0.1992        | 8.0     | 656  | 0.1775          | 0.4547   | 0.0563 | 0.1095    | 0.0548 | 0.7172  |
| 0.179         | 9.0     | 738  | 0.1863          | 0.3316   | 0.0667 | 0.1044    | 0.0521 | 0.7192  |
| 0.1683        | 10.0    | 820  | 0.1786          | 0.4165   | 0.0573 | 0.0951    | 0.0521 | 0.7194  |
| 0.1742        | 11.0    | 902  | 0.1764          | 0.4575   | 0.0714 | 0.1289    | 0.0640 | 0.7240  |
| 0.1871        | 12.0    | 984  | 0.1767          | 0.5130   | 0.0626 | 0.1030    | 0.0642 | 0.7272  |
| 0.1802        | 13.0    | 1066 | 0.1750          | 0.4575   | 0.0604 | 0.1088    | 0.0573 | 0.7305  |
| 0.1666        | 14.0    | 1148 | 0.1755          | 0.4414   | 0.0847 | 0.1338    | 0.0732 | 0.7282  |
| 0.1674        | 15.0    | 1230 | 0.1767          | 0.4263   | 0.0733 | 0.0990    | 0.0651 | 0.7296  |
| 0.1846        | 16.0    | 1312 | 0.1771          | 0.4460   | 0.0866 | 0.1363    | 0.0784 | 0.7349  |
| 0.1819        | 17.0    | 1394 | 0.1762          | 0.4356   | 0.0686 | 0.1063    | 0.0604 | 0.7282  |
| 0.1594        | 18.0    | 1476 | 0.1757          | 0.4298   | 0.0804 | 0.1289    | 0.0699 | 0.7345  |
| 0.1805        | 19.0    | 1558 | 0.1783          | 0.4206   | 0.0884 | 0.1358    | 0.0803 | 0.7323  |
| 0.164         | 20.0    | 1640 | 0.1768          | 0.4858   | 0.0896 | 0.1683    | 0.0816 | 0.7377  |
| 0.1673        | 21.0    | 1722 | 0.1779          | 0.4292   | 0.0888 | 0.1500    | 0.0764 | 0.7281  |
| 0.1719        | 22.0    | 1804 | 0.1780          | 0.5142   | 0.0783 | 0.1409    | 0.0746 | 0.7358  |
| 0.1598        | 23.0    | 1886 | 0.1762          | 0.4801   | 0.0876 | 0.2268    | 0.0778 | 0.7387  |
| 0.1682        | 24.0    | 1968 | 0.1788          | 0.4570   | 0.0954 | 0.1420    | 0.0849 | 0.7406  |
| 0.1558        | 25.0    | 2050 | 0.1795          | 0.4391   | 0.1023 | 0.2186    | 0.0912 | 0.7396  |
| 0.1695        | 26.0    | 2132 | 0.1768          | 0.4830   | 0.0881 | 0.2050    | 0.0789 | 0.7368  |
| 0.1514        | 27.0    | 2214 | 0.1771          | 0.4402   | 0.0958 | 0.2160    | 0.0807 | 0.7385  |
| 0.1669        | 28.0    | 2296 | 0.1773          | 0.4876   | 0.0892 | 0.2195    | 0.0791 | 0.7428  |
| 0.156         | 29.0    | 2378 | 0.1802          | 0.4107   | 0.1000 | 0.2148    | 0.0820 | 0.7331  |
| 0.1555        | 30.0    | 2460 | 0.1795          | 0.4258   | 0.0975 | 0.2033    | 0.0832 | 0.7336  |
| 0.1527        | 31.0    | 2542 | 0.1773          | 0.4853   | 0.0955 | 0.2149    | 0.0835 | 0.7407  |
| 0.1592        | 32.0    | 2624 | 0.1786          | 0.4830   | 0.0920 | 0.2138    | 0.0810 | 0.7402  |
| 0.1567        | 33.0    | 2706 | 0.1788          | 0.4916   | 0.0908 | 0.2125    | 0.0795 | 0.7415  |
| 0.1595        | 34.0    | 2788 | 0.1788          | 0.4558   | 0.0993 | 0.2175    | 0.0835 | 0.7385  |
| 0.1431        | 35.0    | 2870 | 0.1791          | 0.4529   | 0.1043 | 0.2087    | 0.0867 | 0.7364  |
| 0.1709        | 36.0    | 2952 | 0.1792          | 0.4541   | 0.1009 | 0.2145    | 0.0861 | 0.7389  |
| 0.1525        | 37.0    | 3034 | 0.1789          | 0.4714   | 0.0957 | 0.2123    | 0.0832 | 0.7405  |
| 0.1467        | 38.0    | 3116 | 0.1791          | 0.4639   | 0.0981 | 0.2110    | 0.0840 | 0.7397  |
| 0.1531        | 39.0    | 3198 | 0.1791          | 0.4633   | 0.1000 | 0.2123    | 0.0850 | 0.7404  |
| 0.1507        | 39.5169 | 3240 | 0.1792          | 0.4627   | 0.1004 | 0.2132    | 0.0852 | 0.7403  |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

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