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

This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Accuracy: 1.0

## 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3804        | 1.0   | 65   | 0.1751          | 0.9653   |
| 0.1041        | 2.0   | 130  | 0.0557          | 0.9805   |
| 0.071         | 3.0   | 195  | 0.0241          | 0.9924   |
| 0.0687        | 4.0   | 260  | 0.0198          | 0.9967   |
| 0.0851        | 5.0   | 325  | 0.0278          | 0.9892   |
| 0.0383        | 6.0   | 390  | 0.0075          | 0.9978   |
| 0.0485        | 7.0   | 455  | 0.0115          | 0.9967   |
| 0.0781        | 8.0   | 520  | 0.0157          | 0.9924   |
| 0.0374        | 9.0   | 585  | 0.0075          | 0.9967   |
| 0.0435        | 10.0  | 650  | 0.0052          | 0.9989   |
| 0.0309        | 11.0  | 715  | 0.0105          | 0.9957   |
| 0.0464        | 12.0  | 780  | 0.0085          | 0.9967   |
| 0.0189        | 13.0  | 845  | 0.0032          | 1.0      |
| 0.0156        | 14.0  | 910  | 0.0065          | 0.9967   |
| 0.0207        | 15.0  | 975  | 0.0018          | 1.0      |
| 0.0337        | 16.0  | 1040 | 0.0065          | 0.9967   |
| 0.0313        | 17.0  | 1105 | 0.0009          | 1.0      |
| 0.027         | 18.0  | 1170 | 0.0032          | 1.0      |
| 0.0142        | 19.0  | 1235 | 0.0017          | 1.0      |
| 0.0113        | 20.0  | 1300 | 0.0020          | 1.0      |
| 0.0275        | 21.0  | 1365 | 0.0030          | 1.0      |
| 0.0102        | 22.0  | 1430 | 0.0007          | 1.0      |
| 0.0191        | 23.0  | 1495 | 0.0102          | 0.9978   |
| 0.0092        | 24.0  | 1560 | 0.0037          | 0.9989   |
| 0.0157        | 25.0  | 1625 | 0.0012          | 1.0      |
| 0.0117        | 26.0  | 1690 | 0.0014          | 1.0      |
| 0.0114        | 27.0  | 1755 | 0.0003          | 1.0      |
| 0.0152        | 28.0  | 1820 | 0.0009          | 1.0      |
| 0.0124        | 29.0  | 1885 | 0.0015          | 0.9989   |
| 0.0093        | 30.0  | 1950 | 0.0033          | 0.9989   |
| 0.0094        | 31.0  | 2015 | 0.0027          | 0.9989   |
| 0.0242        | 32.0  | 2080 | 0.0026          | 0.9989   |
| 0.0021        | 33.0  | 2145 | 0.0012          | 0.9989   |
| 0.0074        | 34.0  | 2210 | 0.0020          | 0.9989   |
| 0.0065        | 35.0  | 2275 | 0.0019          | 0.9989   |
| 0.0067        | 36.0  | 2340 | 0.0046          | 0.9989   |
| 0.0084        | 37.0  | 2405 | 0.0016          | 0.9989   |
| 0.0125        | 38.0  | 2470 | 0.0011          | 1.0      |
| 0.0093        | 39.0  | 2535 | 0.0021          | 0.9989   |
| 0.0037        | 40.0  | 2600 | 0.0004          | 1.0      |
| 0.0082        | 41.0  | 2665 | 0.0011          | 0.9989   |
| 0.0071        | 42.0  | 2730 | 0.0010          | 1.0      |
| 0.0183        | 43.0  | 2795 | 0.0037          | 0.9989   |
| 0.0071        | 44.0  | 2860 | 0.0004          | 1.0      |
| 0.0032        | 45.0  | 2925 | 0.0002          | 1.0      |
| 0.0052        | 46.0  | 2990 | 0.0001          | 1.0      |
| 0.007         | 47.0  | 3055 | 0.0002          | 1.0      |
| 0.0026        | 48.0  | 3120 | 0.0002          | 1.0      |
| 0.0089        | 49.0  | 3185 | 0.0003          | 1.0      |
| 0.0031        | 50.0  | 3250 | 0.0003          | 1.0      |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1

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