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# vit-base-patch16-224-Trial008-YEL_STEM4

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2608
- 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: 30
- eval_batch_size: 30
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 120
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7064        | 0.8   | 1    | 0.7803          | 0.4667   |
| 0.6891        | 1.6   | 2    | 0.6427          | 0.6      |
| 0.6152        | 2.4   | 3    | 0.5351          | 0.6667   |
| 0.6156        | 4.0   | 5    | 0.3241          | 0.8667   |
| 0.4845        | 4.8   | 6    | 0.2608          | 1.0      |
| 0.4413        | 5.6   | 7    | 0.1876          | 1.0      |
| 0.4024        | 6.4   | 8    | 0.1277          | 1.0      |
| 0.3111        | 8.0   | 10   | 0.1968          | 0.9333   |
| 0.3655        | 8.8   | 11   | 0.0837          | 1.0      |
| 0.3985        | 9.6   | 12   | 0.0476          | 1.0      |
| 0.2309        | 10.4  | 13   | 0.0446          | 1.0      |
| 0.2046        | 12.0  | 15   | 0.0307          | 1.0      |
| 0.2295        | 12.8  | 16   | 0.0220          | 1.0      |
| 0.1953        | 13.6  | 17   | 0.0161          | 1.0      |
| 0.1944        | 14.4  | 18   | 0.0135          | 1.0      |
| 0.256         | 16.0  | 20   | 0.0097          | 1.0      |
| 0.2346        | 16.8  | 21   | 0.0081          | 1.0      |
| 0.1702        | 17.6  | 22   | 0.0073          | 1.0      |
| 0.1831        | 18.4  | 23   | 0.0067          | 1.0      |
| 0.2229        | 20.0  | 25   | 0.0061          | 1.0      |
| 0.1109        | 20.8  | 26   | 0.0053          | 1.0      |
| 0.292         | 21.6  | 27   | 0.0046          | 1.0      |
| 0.1701        | 22.4  | 28   | 0.0040          | 1.0      |
| 0.1471        | 24.0  | 30   | 0.0031          | 1.0      |
| 0.1796        | 24.8  | 31   | 0.0028          | 1.0      |
| 0.1232        | 25.6  | 32   | 0.0026          | 1.0      |
| 0.1646        | 26.4  | 33   | 0.0024          | 1.0      |
| 0.1649        | 28.0  | 35   | 0.0021          | 1.0      |
| 0.1294        | 28.8  | 36   | 0.0021          | 1.0      |
| 0.1352        | 29.6  | 37   | 0.0020          | 1.0      |
| 0.0985        | 30.4  | 38   | 0.0020          | 1.0      |
| 0.1209        | 32.0  | 40   | 0.0020          | 1.0      |
| 0.1004        | 32.8  | 41   | 0.0020          | 1.0      |
| 0.0718        | 33.6  | 42   | 0.0019          | 1.0      |
| 0.1028        | 34.4  | 43   | 0.0019          | 1.0      |
| 0.0984        | 36.0  | 45   | 0.0018          | 1.0      |
| 0.0866        | 36.8  | 46   | 0.0018          | 1.0      |
| 0.136         | 37.6  | 47   | 0.0018          | 1.0      |
| 0.1433        | 38.4  | 48   | 0.0018          | 1.0      |
| 0.1121        | 40.0  | 50   | 0.0017          | 1.0      |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1

plant2 plant3