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# ViT_Flower102

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0587
- Accuracy: 0.9853
- Precision: 0.9853
- Recall: 0.9853
- F1: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3962        | 0.22  | 100  | 1.7861          | 0.7373   | 0.7373    | 0.7373 | 0.7373 |
| 0.5196        | 0.45  | 200  | 0.7527          | 0.8951   | 0.8951    | 0.8951 | 0.8951 |
| 0.355         | 0.67  | 300  | 0.3937          | 0.9451   | 0.9451    | 0.9451 | 0.9451 |
| 0.1966        | 0.89  | 400  | 0.3312          | 0.9422   | 0.9422    | 0.9422 | 0.9422 |
| 0.1262        | 1.11  | 500  | 0.2145          | 0.9608   | 0.9608    | 0.9608 | 0.9608 |
| 0.1512        | 1.34  | 600  | 0.1652          | 0.9706   | 0.9706    | 0.9706 | 0.9706 |
| 0.1414        | 1.56  | 700  | 0.2562          | 0.9471   | 0.9471    | 0.9471 | 0.9471 |
| 0.1235        | 1.78  | 800  | 0.1742          | 0.9657   | 0.9657    | 0.9657 | 0.9657 |
| 0.0428        | 2.0   | 900  | 0.1809          | 0.9578   | 0.9578    | 0.9578 | 0.9578 |
| 0.0202        | 2.23  | 1000 | 0.1518          | 0.9637   | 0.9637    | 0.9637 | 0.9637 |
| 0.0451        | 2.45  | 1100 | 0.1214          | 0.9725   | 0.9725    | 0.9725 | 0.9725 |
| 0.0208        | 2.67  | 1200 | 0.1274          | 0.9725   | 0.9725    | 0.9725 | 0.9725 |
| 0.0673        | 2.9   | 1300 | 0.1904          | 0.9627   | 0.9627    | 0.9627 | 0.9627 |
| 0.0347        | 3.12  | 1400 | 0.1101          | 0.9765   | 0.9765    | 0.9765 | 0.9765 |
| 0.0035        | 3.34  | 1500 | 0.1274          | 0.9765   | 0.9765    | 0.9765 | 0.9765 |
| 0.0629        | 3.56  | 1600 | 0.0743          | 0.9833   | 0.9833    | 0.9833 | 0.9833 |
| 0.0368        | 3.79  | 1700 | 0.0801          | 0.9804   | 0.9804    | 0.9804 | 0.9804 |
| 0.0021        | 4.01  | 1800 | 0.0947          | 0.9794   | 0.9794    | 0.9794 | 0.9794 |
| 0.0037        | 4.23  | 1900 | 0.0990          | 0.9775   | 0.9775    | 0.9775 | 0.9775 |
| 0.0015        | 4.45  | 2000 | 0.0782          | 0.9824   | 0.9824    | 0.9824 | 0.9824 |
| 0.002         | 4.68  | 2100 | 0.0924          | 0.9775   | 0.9775    | 0.9775 | 0.9775 |
| 0.0013        | 4.9   | 2200 | 0.0648          | 0.9892   | 0.9892    | 0.9892 | 0.9892 |
| 0.0013        | 5.12  | 2300 | 0.0705          | 0.9843   | 0.9843    | 0.9843 | 0.9843 |
| 0.0012        | 5.35  | 2400 | 0.0667          | 0.9853   | 0.9853    | 0.9853 | 0.9853 |
| 0.0011        | 5.57  | 2500 | 0.0654          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.001         | 5.79  | 2600 | 0.0809          | 0.9804   | 0.9804    | 0.9804 | 0.9804 |
| 0.001         | 6.01  | 2700 | 0.0603          | 0.9853   | 0.9853    | 0.9853 | 0.9853 |
| 0.0009        | 6.24  | 2800 | 0.0587          | 0.9853   | 0.9853    | 0.9853 | 0.9853 |
| 0.0009        | 6.46  | 2900 | 0.0613          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0354        | 6.68  | 3000 | 0.0625          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0009        | 6.9   | 3100 | 0.0640          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0009        | 7.13  | 3200 | 0.0637          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0008        | 7.35  | 3300 | 0.0640          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0007        | 7.57  | 3400 | 0.0638          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0008        | 7.8   | 3500 | 0.0632          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0006        | 8.02  | 3600 | 0.0628          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0006        | 8.24  | 3700 | 0.0628          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0007        | 8.46  | 3800 | 0.0629          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0006        | 8.69  | 3900 | 0.0628          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0007        | 8.91  | 4000 | 0.0629          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0006        | 9.13  | 4100 | 0.0629          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0005        | 9.35  | 4200 | 0.0629          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0006        | 9.58  | 4300 | 0.0629          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |
| 0.0006        | 9.8   | 4400 | 0.0629          | 0.9863   | 0.9863    | 0.9863 | 0.9863 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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