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# beit-base-patch16-224-pt22k-ft22k-finetuned-eurosat

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1650
- Accuracy: 0.7003

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 3.0558        | 1.0     | 43   | 3.0721          | 0.0519   |
| 2.9535        | 2.0     | 86   | 2.9192          | 0.1068   |
| 2.7928        | 3.0     | 129  | 2.6951          | 0.2315   |
| 2.5368        | 4.0     | 172  | 2.4673          | 0.3457   |
| 2.3928        | 5.0     | 215  | 2.2777          | 0.4303   |
| 2.1938        | 6.0     | 258  | 2.1163          | 0.4896   |
| 2.0454        | 7.0     | 301  | 1.9760          | 0.5208   |
| 1.9604        | 8.0     | 344  | 1.8527          | 0.5593   |
| 1.8495        | 9.0     | 387  | 1.7479          | 0.5890   |
| 1.6308        | 10.0    | 430  | 1.6564          | 0.6231   |
| 1.6896        | 11.0    | 473  | 1.5779          | 0.6424   |
| 1.6121        | 12.0    | 516  | 1.5088          | 0.6632   |
| 1.5673        | 13.0    | 559  | 1.4500          | 0.6751   |
| 1.4859        | 14.0    | 602  | 1.3988          | 0.6869   |
| 1.4813        | 15.0    | 645  | 1.3561          | 0.6958   |
| 1.4414        | 16.0    | 688  | 1.3177          | 0.7062   |
| 1.4252        | 17.0    | 731  | 1.2833          | 0.7136   |
| 1.3811        | 18.0    | 774  | 1.2549          | 0.7226   |
| 1.3464        | 19.0    | 817  | 1.2304          | 0.7240   |
| 1.2451        | 20.0    | 860  | 1.2093          | 0.7270   |
| 1.2871        | 21.0    | 903  | 1.1904          | 0.7315   |
| 1.2546        | 22.0    | 946  | 1.1746          | 0.7315   |
| 1.2464        | 23.0    | 989  | 1.1611          | 0.7329   |
| 1.3012        | 24.0    | 1032 | 1.1499          | 0.7374   |
| 1.2477        | 25.0    | 1075 | 1.1409          | 0.7404   |
| 1.2761        | 26.0    | 1118 | 1.1337          | 0.7404   |
| 1.2687        | 27.0    | 1161 | 1.1289          | 0.7404   |
| 1.2304        | 28.0    | 1204 | 1.1258          | 0.7433   |
| 1.2628        | 29.0    | 1247 | 1.1244          | 0.7433   |
| 1.2639        | 29.3077 | 1260 | 1.1243          | 0.7433   |


### Framework versions

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

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