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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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1631
- Accuracy: 0.9546

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4463        | 1.0   | 51   | 0.3232          | 0.9166   |
| 0.2061        | 2.0   | 102  | 0.1659          | 0.9398   |
| 0.1795        | 3.0   | 153  | 0.2043          | 0.9192   |
| 0.1356        | 4.0   | 204  | 0.1204          | 0.9542   |
| 0.1193        | 5.0   | 255  | 0.1357          | 0.9454   |
| 0.1054        | 6.0   | 306  | 0.1197          | 0.9516   |
| 0.0974        | 7.0   | 357  | 0.1081          | 0.9586   |
| 0.092         | 8.0   | 408  | 0.1220          | 0.9532   |
| 0.0601        | 9.0   | 459  | 0.1587          | 0.9466   |
| 0.0639        | 10.0  | 510  | 0.1676          | 0.9440   |
| 0.072         | 11.0  | 561  | 0.1058          | 0.9618   |
| 0.0606        | 12.0  | 612  | 0.1061          | 0.9634   |
| 0.0572        | 13.0  | 663  | 0.1375          | 0.9552   |
| 0.0563        | 14.0  | 714  | 0.1377          | 0.9548   |
| 0.0413        | 15.0  | 765  | 0.1823          | 0.9470   |
| 0.0361        | 16.0  | 816  | 0.0992          | 0.9674   |
| 0.0471        | 17.0  | 867  | 0.1508          | 0.9550   |
| 0.04          | 18.0  | 918  | 0.1700          | 0.9506   |
| 0.0417        | 19.0  | 969  | 0.1760          | 0.9454   |
| 0.0238        | 20.0  | 1020 | 0.1311          | 0.9600   |
| 0.0319        | 21.0  | 1071 | 0.1502          | 0.9562   |
| 0.0328        | 22.0  | 1122 | 0.1843          | 0.9484   |
| 0.0363        | 23.0  | 1173 | 0.1473          | 0.9558   |
| 0.0385        | 24.0  | 1224 | 0.1625          | 0.9516   |
| 0.0198        | 25.0  | 1275 | 0.1749          | 0.9490   |
| 0.0349        | 26.0  | 1326 | 0.1586          | 0.9528   |
| 0.0337        | 27.0  | 1377 | 0.1343          | 0.9614   |
| 0.0261        | 28.0  | 1428 | 0.1624          | 0.9542   |
| 0.0253        | 29.0  | 1479 | 0.1727          | 0.9532   |
| 0.0271        | 30.0  | 1530 | 0.1631          | 0.9546   |


### Framework versions

- Transformers 4.37.2
- Pytorch 1.13.1
- Datasets 2.16.1
- Tokenizers 0.15.1

meme not_meme