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# swinv2-base-patch4-window12to16-192to256-22kto1k-ft-finetuned-eurosat

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-base-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4454
- Accuracy: 0.9036

## 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: 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: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.5802        | 1.0     | 48   | 1.6596          | 0.5445   |
| 0.9821        | 2.0     | 96   | 0.6121          | 0.8012   |
| 0.767         | 3.0     | 144  | 0.5143          | 0.8323   |
| 0.6452        | 4.0     | 192  | 0.4496          | 0.8516   |
| 0.4403        | 5.0     | 240  | 0.5212          | 0.8472   |
| 0.4859        | 6.0     | 288  | 0.4766          | 0.8828   |
| 0.4177        | 7.0     | 336  | 0.4704          | 0.8516   |
| 0.3815        | 8.0     | 384  | 0.4597          | 0.8694   |
| 0.4111        | 9.0     | 432  | 0.4289          | 0.8828   |
| 0.3375        | 10.0    | 480  | 0.4603          | 0.8709   |
| 0.3267        | 11.0    | 528  | 0.5173          | 0.8739   |
| 0.2948        | 12.0    | 576  | 0.4379          | 0.8932   |
| 0.2322        | 13.0    | 624  | 0.4454          | 0.9036   |
| 0.2612        | 14.0    | 672  | 0.5133          | 0.8739   |
| 0.2259        | 15.0    | 720  | 0.4377          | 0.8947   |
| 0.2534        | 16.0    | 768  | 0.5072          | 0.8724   |
| 0.1852        | 17.0    | 816  | 0.4951          | 0.8843   |
| 0.1976        | 18.0    | 864  | 0.5063          | 0.8902   |
| 0.2377        | 19.0    | 912  | 0.4767          | 0.8843   |
| 0.189         | 20.0    | 960  | 0.4763          | 0.8917   |
| 0.1744        | 21.0    | 1008 | 0.5027          | 0.8813   |
| 0.1546        | 22.0    | 1056 | 0.5021          | 0.8961   |
| 0.1451        | 23.0    | 1104 | 0.4772          | 0.9006   |
| 0.1681        | 24.0    | 1152 | 0.4767          | 0.8976   |
| 0.1539        | 25.0    | 1200 | 0.5087          | 0.8902   |
| 0.1054        | 26.0    | 1248 | 0.5186          | 0.8902   |
| 0.1111        | 27.0    | 1296 | 0.5066          | 0.9006   |
| 0.1057        | 28.0    | 1344 | 0.5019          | 0.8947   |
| 0.1498        | 29.0    | 1392 | 0.5147          | 0.9006   |
| 0.1255        | 29.3789 | 1410 | 0.5148          | 0.8991   |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

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