
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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: 0.3768
- Accuracy: 0.8989

## 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: Use 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log        | 0.9231  | 3    | 1.8889          | 0.2472   |
| No log        | 1.8462  | 6    | 1.7625          | 0.3820   |
| No log        | 2.7692  | 9    | 1.5603          | 0.4494   |
| 1.7854        | 4.0     | 13   | 1.3005          | 0.5281   |
| 1.7854        | 4.9231  | 16   | 1.0408          | 0.6292   |
| 1.7854        | 5.8462  | 19   | 0.8925          | 0.6854   |
| 1.1431        | 6.7692  | 22   | 0.7614          | 0.7303   |
| 1.1431        | 8.0     | 26   | 0.6343          | 0.7753   |
| 1.1431        | 8.9231  | 29   | 0.5810          | 0.7978   |
| 0.7715        | 9.8462  | 32   | 0.5551          | 0.8427   |
| 0.7715        | 10.7692 | 35   | 0.5209          | 0.8539   |
| 0.7715        | 12.0    | 39   | 0.5690          | 0.8202   |
| 0.5645        | 12.9231 | 42   | 0.4431          | 0.8876   |
| 0.5645        | 13.8462 | 45   | 0.4922          | 0.8202   |
| 0.5645        | 14.7692 | 48   | 0.4914          | 0.8315   |
| 0.4999        | 16.0    | 52   | 0.3768          | 0.8989   |
| 0.4999        | 16.9231 | 55   | 0.4292          | 0.8539   |
| 0.4999        | 17.8462 | 58   | 0.3846          | 0.8652   |
| 0.4555        | 18.7692 | 61   | 0.3498          | 0.8876   |
| 0.4555        | 20.0    | 65   | 0.3523          | 0.8652   |
| 0.4555        | 20.9231 | 68   | 0.3541          | 0.8876   |
| 0.3941        | 21.8462 | 71   | 0.3240          | 0.8989   |
| 0.3941        | 22.7692 | 74   | 0.3169          | 0.8989   |
| 0.3941        | 24.0    | 78   | 0.3317          | 0.8764   |
| 0.361         | 24.9231 | 81   | 0.3251          | 0.8876   |
| 0.361         | 25.8462 | 84   | 0.3198          | 0.8764   |
| 0.361         | 26.7692 | 87   | 0.3117          | 0.8764   |
| 0.3485        | 27.6923 | 90   | 0.3101          | 0.8764   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3

speckled centromere dfs nucleolar nuclear envelope nuclear dots homogeneous