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# vit-base-patch16-224-Trial007-YEL_STEM

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: 0.0373
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7081        | 0.89  | 2    | 0.6818          | 0.5556   |
| 0.6584        | 1.78  | 4    | 0.5915          | 0.7037   |
| 0.5552        | 2.67  | 6    | 0.5366          | 0.7407   |
| 0.3763        | 4.0   | 9    | 0.3560          | 0.8519   |
| 0.397         | 4.89  | 11   | 0.2999          | 0.8519   |
| 0.3313        | 5.78  | 13   | 0.2307          | 0.9074   |
| 0.2957        | 6.67  | 15   | 0.1746          | 0.9259   |
| 0.2383        | 8.0   | 18   | 0.1432          | 0.9444   |
| 0.2664        | 8.89  | 20   | 0.3320          | 0.9074   |
| 0.2242        | 9.78  | 22   | 0.1120          | 0.9630   |
| 0.2072        | 10.67 | 24   | 0.0718          | 0.9630   |
| 0.1399        | 12.0  | 27   | 0.0494          | 0.9815   |
| 0.1846        | 12.89 | 29   | 0.0373          | 1.0      |
| 0.1816        | 13.78 | 31   | 0.0354          | 1.0      |
| 0.1453        | 14.67 | 33   | 0.0461          | 0.9815   |
| 0.1406        | 16.0  | 36   | 0.0333          | 1.0      |
| 0.1749        | 16.89 | 38   | 0.0275          | 1.0      |
| 0.1383        | 17.78 | 40   | 0.0203          | 1.0      |
| 0.1659        | 18.67 | 42   | 0.0186          | 1.0      |
| 0.153         | 20.0  | 45   | 0.0184          | 1.0      |
| 0.1497        | 20.89 | 47   | 0.0215          | 1.0      |
| 0.1088        | 21.78 | 49   | 0.0408          | 0.9815   |
| 0.1796        | 22.67 | 51   | 0.0377          | 0.9815   |
| 0.1041        | 24.0  | 54   | 0.0631          | 0.9815   |
| 0.1193        | 24.89 | 56   | 0.0637          | 0.9815   |
| 0.1653        | 25.78 | 58   | 0.0730          | 0.9815   |
| 0.1296        | 26.67 | 60   | 0.0779          | 0.9815   |
| 0.1036        | 28.0  | 63   | 0.0312          | 0.9815   |
| 0.1287        | 28.89 | 65   | 0.0116          | 1.0      |
| 0.1307        | 29.78 | 67   | 0.0129          | 1.0      |
| 0.1337        | 30.67 | 69   | 0.0141          | 1.0      |
| 0.1274        | 32.0  | 72   | 0.0161          | 1.0      |
| 0.1612        | 32.89 | 74   | 0.0177          | 1.0      |
| 0.1504        | 33.78 | 76   | 0.0181          | 1.0      |
| 0.1307        | 34.67 | 78   | 0.0175          | 1.0      |
| 0.125         | 36.0  | 81   | 0.0170          | 1.0      |
| 0.1357        | 36.89 | 83   | 0.0165          | 1.0      |
| 0.1033        | 37.78 | 85   | 0.0162          | 1.0      |
| 0.1749        | 38.67 | 87   | 0.0164          | 1.0      |
| 0.0906        | 40.0  | 90   | 0.0153          | 1.0      |
| 0.1349        | 40.89 | 92   | 0.0152          | 1.0      |
| 0.1056        | 41.78 | 94   | 0.0150          | 1.0      |
| 0.1328        | 42.67 | 96   | 0.0148          | 1.0      |
| 0.0742        | 44.0  | 99   | 0.0148          | 1.0      |
| 0.0875        | 44.44 | 100  | 0.0148          | 1.0      |


### Framework versions

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

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