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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: 0.3937
- Accuracy: 0.8583

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6866        | 0.99  | 37   | 0.6202          | 0.6742   |
| 0.5892        | 2.0   | 75   | 0.5467          | 0.72     |
| 0.5195        | 2.99  | 112  | 0.4933          | 0.7483   |
| 0.4322        | 4.0   | 150  | 0.4787          | 0.765    |
| 0.3712        | 4.99  | 187  | 0.3829          | 0.8208   |
| 0.3162        | 6.0   | 225  | 0.3960          | 0.8133   |
| 0.3082        | 6.99  | 262  | 0.3591          | 0.8392   |
| 0.3038        | 8.0   | 300  | 0.3274          | 0.8467   |
| 0.2794        | 8.99  | 337  | 0.3533          | 0.8433   |
| 0.2596        | 10.0  | 375  | 0.3766          | 0.8258   |
| 0.2369        | 10.99 | 412  | 0.3392          | 0.8575   |
| 0.2503        | 12.0  | 450  | 0.3198          | 0.8625   |
| 0.2009        | 12.99 | 487  | 0.3438          | 0.8625   |
| 0.2195        | 14.0  | 525  | 0.3234          | 0.8617   |
| 0.2025        | 14.99 | 562  | 0.3758          | 0.855    |
| 0.1879        | 16.0  | 600  | 0.3909          | 0.8408   |
| 0.18          | 16.99 | 637  | 0.3642          | 0.8617   |
| 0.1545        | 18.0  | 675  | 0.3948          | 0.8567   |
| 0.171         | 18.99 | 712  | 0.3889          | 0.8525   |
| 0.1667        | 20.0  | 750  | 0.3883          | 0.8625   |
| 0.163         | 20.99 | 787  | 0.3743          | 0.8575   |
| 0.1682        | 22.0  | 825  | 0.3739          | 0.8592   |
| 0.1611        | 22.99 | 862  | 0.3623          | 0.8742   |
| 0.1348        | 24.0  | 900  | 0.3806          | 0.8592   |
| 0.1366        | 24.99 | 937  | 0.3849          | 0.865    |
| 0.1418        | 26.0  | 975  | 0.4049          | 0.8558   |
| 0.1096        | 26.99 | 1012 | 0.3849          | 0.8608   |
| 0.1347        | 28.0  | 1050 | 0.3926          | 0.8592   |
| 0.137         | 28.99 | 1087 | 0.3938          | 0.8592   |
| 0.1312        | 29.6  | 1110 | 0.3937          | 0.8583   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "validation", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8583333333333333, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us