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# vit-base-patch16-224-Trial006_007_008-YEL_STEM3

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: 0.0514
- 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: 60
- eval_batch_size: 60
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 240
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7488        | 0.8   | 3    | 0.7067          | 0.5253   |
| 0.6693        | 1.87  | 7    | 0.6195          | 0.6263   |
| 0.5816        | 2.93  | 11   | 0.5114          | 0.7273   |
| 0.4669        | 4.0   | 15   | 0.3586          | 0.8990   |
| 0.3998        | 4.8   | 18   | 0.2867          | 0.9192   |
| 0.3579        | 5.87  | 22   | 0.2326          | 0.9596   |
| 0.3087        | 6.93  | 26   | 0.1684          | 0.9596   |
| 0.2448        | 8.0   | 30   | 0.1525          | 0.9697   |
| 0.207         | 8.8   | 33   | 0.1863          | 0.9394   |
| 0.2283        | 9.87  | 37   | 0.1259          | 0.9697   |
| 0.2354        | 10.93 | 41   | 0.1090          | 0.9495   |
| 0.2452        | 12.0  | 45   | 0.0999          | 0.9596   |
| 0.2373        | 12.8  | 48   | 0.0702          | 0.9899   |
| 0.1516        | 13.87 | 52   | 0.0749          | 0.9697   |
| 0.1893        | 14.93 | 56   | 0.0808          | 0.9798   |
| 0.1761        | 16.0  | 60   | 0.0514          | 1.0      |
| 0.1971        | 16.8  | 63   | 0.0954          | 0.9798   |
| 0.1801        | 17.87 | 67   | 0.0536          | 0.9899   |
| 0.2004        | 18.93 | 71   | 0.0836          | 0.9596   |
| 0.1899        | 20.0  | 75   | 0.0500          | 0.9899   |
| 0.2222        | 20.8  | 78   | 0.0874          | 0.9798   |
| 0.1341        | 21.87 | 82   | 0.0371          | 1.0      |
| 0.2187        | 22.93 | 86   | 0.0545          | 1.0      |
| 0.1547        | 24.0  | 90   | 0.0445          | 1.0      |
| 0.1307        | 24.8  | 93   | 0.0404          | 1.0      |
| 0.1563        | 25.87 | 97   | 0.0377          | 1.0      |
| 0.1467        | 26.93 | 101  | 0.0405          | 1.0      |
| 0.1413        | 28.0  | 105  | 0.0379          | 1.0      |
| 0.1317        | 28.8  | 108  | 0.0367          | 1.0      |
| 0.1264        | 29.87 | 112  | 0.0359          | 1.0      |
| 0.128         | 30.93 | 116  | 0.0328          | 1.0      |
| 0.1292        | 32.0  | 120  | 0.0356          | 1.0      |
| 0.1549        | 32.8  | 123  | 0.0339          | 1.0      |
| 0.1272        | 33.87 | 127  | 0.0296          | 1.0      |
| 0.1376        | 34.93 | 131  | 0.0295          | 1.0      |
| 0.1884        | 36.0  | 135  | 0.0308          | 1.0      |
| 0.1536        | 36.8  | 138  | 0.0295          | 1.0      |
| 0.166         | 37.87 | 142  | 0.0299          | 1.0      |
| 0.0929        | 38.93 | 146  | 0.0307          | 1.0      |
| 0.1592        | 40.0  | 150  | 0.0307          | 1.0      |


### Framework versions

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

plant2 plant3