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# vit-large-patch16-224-finetuned-eurosat

This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4947
- Accuracy: 0.875
- Precision: 0.8796
- Recall: 0.8760
- F1: 0.8715

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.0089        | 1.0   | 10   | 1.4560          | 0.6188   | 0.6505    | 0.6232 | 0.6086 |
| 0.8976        | 2.0   | 20   | 0.4947          | 0.875    | 0.8796    | 0.8760 | 0.8715 |
| 0.3555        | 3.0   | 30   | 0.4390          | 0.8313   | 0.8272    | 0.8339 | 0.8274 |
| 0.1709        | 4.0   | 40   | 0.4177          | 0.85     | 0.8506    | 0.8559 | 0.8484 |
| 0.0882        | 5.0   | 50   | 0.4561          | 0.8625   | 0.8630    | 0.8610 | 0.8592 |
| 0.1136        | 6.0   | 60   | 0.5954          | 0.8375   | 0.8494    | 0.8346 | 0.8231 |
| 0.0863        | 7.0   | 70   | 0.5323          | 0.8375   | 0.8353    | 0.8373 | 0.8349 |
| 0.0637        | 8.0   | 80   | 0.4966          | 0.8438   | 0.8579    | 0.8470 | 0.8476 |
| 0.0536        | 9.0   | 90   | 0.6577          | 0.8375   | 0.8598    | 0.8423 | 0.8383 |
| 0.1069        | 10.0  | 100  | 0.5586          | 0.8438   | 0.8417    | 0.8429 | 0.8387 |
| 0.0446        | 11.0  | 110  | 0.5196          | 0.85     | 0.8703    | 0.8520 | 0.8520 |
| 0.0401        | 12.0  | 120  | 0.4480          | 0.8625   | 0.8651    | 0.8653 | 0.8632 |
| 0.0333        | 13.0  | 130  | 0.4955          | 0.8375   | 0.8441    | 0.8422 | 0.8393 |
| 0.036         | 14.0  | 140  | 0.5074          | 0.8438   | 0.8515    | 0.8477 | 0.8469 |
| 0.0288        | 15.0  | 150  | 0.5466          | 0.8438   | 0.8465    | 0.8441 | 0.8426 |
| 0.0141        | 16.0  | 160  | 0.6208          | 0.8313   | 0.8400    | 0.8324 | 0.8299 |
| 0.0145        | 17.0  | 170  | 0.5696          | 0.8438   | 0.8547    | 0.8458 | 0.8449 |
| 0.0194        | 18.0  | 180  | 0.5469          | 0.8562   | 0.8606    | 0.8575 | 0.8566 |
| 0.0139        | 19.0  | 190  | 0.5600          | 0.8625   | 0.8658    | 0.8670 | 0.8634 |
| 0.0184        | 20.0  | 200  | 0.5611          | 0.8625   | 0.8658    | 0.8675 | 0.8638 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1

aechmea_fasciata agave_americana agave_attenuata agave_tequilana aglaonema_commutatum albuca_spiralis allium_cepa allium_sativum