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# vit-base-flowers102

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the nelorth/oxford-flowers dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0770
- Accuracy: 0.9853

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5779        | 0.22  | 100  | 2.8895          | 0.7775   |
| 1.2226        | 0.45  | 200  | 1.5942          | 0.9255   |
| 0.606         | 0.67  | 300  | 0.8012          | 0.9529   |
| 0.3413        | 0.89  | 400  | 0.4845          | 0.9706   |
| 0.1571        | 1.11  | 500  | 0.2611          | 0.9814   |
| 0.1237        | 1.34  | 600  | 0.1691          | 0.9784   |
| 0.049         | 1.56  | 700  | 0.1146          | 0.9892   |
| 0.0763        | 1.78  | 800  | 0.1209          | 0.9863   |
| 0.0864        | 2.0   | 900  | 0.1223          | 0.9804   |
| 0.0786        | 2.23  | 1000 | 0.1075          | 0.9833   |
| 0.0269        | 2.45  | 1100 | 0.0919          | 0.9843   |
| 0.0178        | 2.67  | 1200 | 0.0795          | 0.9873   |
| 0.0165        | 2.9   | 1300 | 0.0727          | 0.9873   |
| 0.0144        | 3.12  | 1400 | 0.0784          | 0.9853   |
| 0.0138        | 3.34  | 1500 | 0.0759          | 0.9853   |
| 0.0135        | 3.56  | 1600 | 0.0737          | 0.9863   |
| 0.0123        | 3.79  | 1700 | 0.0770          | 0.9853   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

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