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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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0846
- F1: 0.5965
- Roc Auc: 0.7500
- Accuracy: 0.3659

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 0.91  | 8    | 0.6685          | 0.0992 | 0.5341  | 0.0      |
| 0.7041        | 1.94  | 17   | 0.4597          | 0.1245 | 0.5440  | 0.0      |
| 0.5413        | 2.97  | 26   | 0.1970          | 0.0287 | 0.5067  | 0.0081   |
| 0.2531        | 4.0   | 35   | 0.1487          | 0.0099 | 0.5022  | 0.0      |
| 0.15          | 4.91  | 43   | 0.1465          | 0.0566 | 0.5142  | 0.0407   |
| 0.145         | 5.94  | 52   | 0.1433          | 0.1166 | 0.5312  | 0.0569   |
| 0.138         | 6.97  | 61   | 0.1412          | 0.2140 | 0.5629  | 0.0976   |
| 0.1374        | 8.0   | 70   | 0.1377          | 0.2698 | 0.5827  | 0.1138   |
| 0.1374        | 8.91  | 78   | 0.1319          | 0.2410 | 0.5726  | 0.1057   |
| 0.1309        | 9.94  | 87   | 0.1284          | 0.3100 | 0.6014  | 0.1382   |
| 0.1256        | 10.97 | 96   | 0.1228          | 0.2667 | 0.5824  | 0.1220   |
| 0.1196        | 12.0  | 105  | 0.1201          | 0.3500 | 0.6186  | 0.1463   |
| 0.116         | 12.91 | 113  | 0.1169          | 0.3732 | 0.6286  | 0.1707   |
| 0.1102        | 13.94 | 122  | 0.1137          | 0.3650 | 0.6220  | 0.1951   |
| 0.1062        | 14.97 | 131  | 0.1082          | 0.3843 | 0.6316  | 0.2195   |
| 0.1019        | 16.0  | 140  | 0.1048          | 0.4630 | 0.6751  | 0.2602   |
| 0.1019        | 16.91 | 148  | 0.1033          | 0.4475 | 0.6614  | 0.2602   |
| 0.0965        | 17.94 | 157  | 0.1046          | 0.4890 | 0.6899  | 0.2846   |
| 0.0935        | 18.97 | 166  | 0.1014          | 0.4651 | 0.6711  | 0.2358   |
| 0.0928        | 20.0  | 175  | 0.0998          | 0.4877 | 0.6918  | 0.2520   |
| 0.0897        | 20.91 | 183  | 0.0959          | 0.5145 | 0.6961  | 0.2683   |
| 0.0843        | 21.94 | 192  | 0.0933          | 0.5296 | 0.7080  | 0.2927   |
| 0.0829        | 22.97 | 201  | 0.0919          | 0.5610 | 0.7255  | 0.3171   |
| 0.0804        | 24.0  | 210  | 0.0917          | 0.5644 | 0.7257  | 0.3496   |
| 0.0804        | 24.91 | 218  | 0.0898          | 0.6036 | 0.7505  | 0.3577   |
| 0.0797        | 25.94 | 227  | 0.0886          | 0.5758 | 0.7331  | 0.3333   |
| 0.0762        | 26.97 | 236  | 0.0865          | 0.5740 | 0.7330  | 0.3415   |
| 0.0757        | 28.0  | 245  | 0.0879          | 0.5893 | 0.7429  | 0.3577   |
| 0.0736        | 28.91 | 253  | 0.0866          | 0.5875 | 0.7427  | 0.3415   |
| 0.0716        | 29.94 | 262  | 0.0855          | 0.5910 | 0.7430  | 0.3659   |
| 0.0722        | 30.97 | 271  | 0.0857          | 0.5917 | 0.7452  | 0.3577   |
| 0.0716        | 32.0  | 280  | 0.0864          | 0.5868 | 0.7405  | 0.3415   |
| 0.0716        | 32.91 | 288  | 0.0850          | 0.5917 | 0.7452  | 0.3577   |
| 0.0701        | 33.94 | 297  | 0.0849          | 0.5965 | 0.7500  | 0.3577   |
| 0.0701        | 34.97 | 306  | 0.0844          | 0.5875 | 0.7427  | 0.3496   |
| 0.0704        | 36.0  | 315  | 0.0846          | 0.5982 | 0.7501  | 0.3659   |
| 0.0695        | 36.57 | 320  | 0.0846          | 0.5965 | 0.7500  | 0.3659   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

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