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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: 0.1436
- Accuracy: 0.9526

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3316        | 0.03  | 10   | 2.2478          | 0.1604   |
| 2.1681        | 0.06  | 20   | 1.9824          | 0.4184   |
| 1.8612        | 0.09  | 30   | 1.5035          | 0.604    |
| 1.3889        | 0.11  | 40   | 0.9938          | 0.7216   |
| 1.2006        | 0.14  | 50   | 0.7234          | 0.8074   |
| 0.9586        | 0.17  | 60   | 0.5678          | 0.8392   |
| 0.8623        | 0.2   | 70   | 0.4286          | 0.8826   |
| 0.7509        | 0.23  | 80   | 0.3739          | 0.8848   |
| 0.6981        | 0.26  | 90   | 0.3286          | 0.904    |
| 0.7039        | 0.28  | 100  | 0.3099          | 0.9048   |
| 0.634         | 0.31  | 110  | 0.2724          | 0.9162   |
| 0.6496        | 0.34  | 120  | 0.2538          | 0.9196   |
| 0.5795        | 0.37  | 130  | 0.2426          | 0.9214   |
| 0.6245        | 0.4   | 140  | 0.2195          | 0.9294   |
| 0.5503        | 0.43  | 150  | 0.2034          | 0.9374   |
| 0.5557        | 0.45  | 160  | 0.1889          | 0.94     |
| 0.5474        | 0.48  | 170  | 0.1849          | 0.9408   |
| 0.5562        | 0.51  | 180  | 0.1912          | 0.9346   |
| 0.5442        | 0.54  | 190  | 0.1761          | 0.9422   |
| 0.5367        | 0.57  | 200  | 0.1866          | 0.9374   |
| 0.5252        | 0.6   | 210  | 0.1636          | 0.9482   |
| 0.5394        | 0.63  | 220  | 0.1656          | 0.9458   |
| 0.5582        | 0.65  | 230  | 0.1670          | 0.944    |
| 0.5642        | 0.68  | 240  | 0.1633          | 0.9486   |
| 0.5           | 0.71  | 250  | 0.1602          | 0.9468   |
| 0.504         | 0.74  | 260  | 0.1624          | 0.9448   |
| 0.4887        | 0.77  | 270  | 0.1556          | 0.9482   |
| 0.4755        | 0.8   | 280  | 0.1493          | 0.9522   |
| 0.4605        | 0.82  | 290  | 0.1444          | 0.9516   |
| 0.5271        | 0.85  | 300  | 0.1471          | 0.9526   |
| 0.514         | 0.88  | 310  | 0.1453          | 0.9506   |
| 0.5155        | 0.91  | 320  | 0.1448          | 0.9516   |
| 0.4785        | 0.94  | 330  | 0.1434          | 0.9522   |
| 0.4647        | 0.97  | 340  | 0.1436          | 0.9528   |
| 0.4657        | 1.0   | 350  | 0.1436          | 0.9526   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1

airplane automobile bird cat deer dog frog horse ship truck