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# swinv2-tiny-patch4-window8-256-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6760
- Accuracy: 0.8170

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2519        | 0.9955  | 167  | 0.7302          | 0.8017   |
| 0.2407        | 1.9970  | 335  | 0.7095          | 0.7836   |
| 0.3423        | 2.9985  | 503  | 0.7016          | 0.7884   |
| 0.4687        | 4.0     | 671  | 0.6480          | 0.7969   |
| 0.4789        | 4.9955  | 838  | 0.5132          | 0.8160   |
| 0.4417        | 5.9970  | 1006 | 0.5321          | 0.8065   |
| 0.435         | 6.9985  | 1174 | 0.5770          | 0.8093   |
| 0.4106        | 8.0     | 1342 | 0.5650          | 0.8189   |
| 0.4216        | 8.9955  | 1509 | 0.5535          | 0.8132   |
| 0.3786        | 9.9970  | 1677 | 0.5745          | 0.8179   |
| 0.3536        | 10.9985 | 1845 | 0.6322          | 0.8046   |
| 0.4842        | 12.0    | 2013 | 0.7200          | 0.8103   |
| 0.3095        | 12.9955 | 2180 | 0.6996          | 0.8112   |
| 0.2603        | 13.9970 | 2348 | 0.7004          | 0.8065   |
| 0.2838        | 14.9985 | 2516 | 0.6331          | 0.8227   |
| 0.3449        | 16.0    | 2684 | 0.6788          | 0.8122   |
| 0.253         | 16.9955 | 2851 | 0.6940          | 0.8103   |
| 0.2647        | 17.9970 | 3019 | 0.6770          | 0.8132   |
| 0.2991        | 18.9985 | 3187 | 0.6647          | 0.8189   |
| 0.26          | 19.9106 | 3340 | 0.6760          | 0.8170   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.2
- Datasets 2.19.1
- Tokenizers 0.19.1

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