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# vit-base-patch16-224-Trial007-YEL_STEM1

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: 0.0269
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8443        | 0.89  | 2    | 0.7813          | 0.3148   |
| 0.7501        | 1.78  | 4    | 0.7087          | 0.5556   |
| 0.6312        | 2.67  | 6    | 0.5306          | 0.9074   |
| 0.4329        | 4.0   | 9    | 0.3618          | 0.9074   |
| 0.4438        | 4.89  | 11   | 0.2699          | 0.9444   |
| 0.3858        | 5.78  | 13   | 0.3650          | 0.7963   |
| 0.339         | 6.67  | 15   | 0.1911          | 0.9630   |
| 0.2852        | 8.0   | 18   | 0.1611          | 0.9630   |
| 0.1866        | 8.89  | 20   | 0.1516          | 0.9444   |
| 0.1748        | 9.78  | 22   | 0.1333          | 0.9630   |
| 0.1996        | 10.67 | 24   | 0.1188          | 0.9630   |
| 0.1604        | 12.0  | 27   | 0.1169          | 0.9444   |
| 0.1319        | 12.89 | 29   | 0.0835          | 0.9815   |
| 0.141         | 13.78 | 31   | 0.0704          | 0.9815   |
| 0.123         | 14.67 | 33   | 0.0574          | 0.9815   |
| 0.0678        | 16.0  | 36   | 0.0604          | 0.9815   |
| 0.1208        | 16.89 | 38   | 0.0385          | 0.9815   |
| 0.0942        | 17.78 | 40   | 0.0269          | 1.0      |
| 0.0822        | 18.67 | 42   | 0.0169          | 1.0      |
| 0.0578        | 20.0  | 45   | 0.0175          | 1.0      |
| 0.0611        | 20.89 | 47   | 0.0220          | 1.0      |
| 0.1053        | 21.78 | 49   | 0.0098          | 1.0      |
| 0.1713        | 22.67 | 51   | 0.0156          | 1.0      |
| 0.0515        | 24.0  | 54   | 0.0111          | 1.0      |
| 0.1227        | 24.89 | 56   | 0.0166          | 1.0      |
| 0.0891        | 25.78 | 58   | 0.0093          | 1.0      |
| 0.0768        | 26.67 | 60   | 0.0090          | 1.0      |
| 0.0755        | 28.0  | 63   | 0.0108          | 1.0      |
| 0.0798        | 28.89 | 65   | 0.0201          | 1.0      |
| 0.1005        | 29.78 | 67   | 0.0118          | 1.0      |
| 0.1113        | 30.67 | 69   | 0.0131          | 1.0      |
| 0.1034        | 32.0  | 72   | 0.0171          | 1.0      |
| 0.0857        | 32.89 | 74   | 0.0158          | 1.0      |
| 0.0864        | 33.78 | 76   | 0.0141          | 1.0      |
| 0.1241        | 34.67 | 78   | 0.0127          | 1.0      |
| 0.0868        | 36.0  | 81   | 0.0118          | 1.0      |
| 0.0704        | 36.89 | 83   | 0.0113          | 1.0      |
| 0.0938        | 37.78 | 85   | 0.0109          | 1.0      |
| 0.1181        | 38.67 | 87   | 0.0120          | 1.0      |
| 0.0509        | 40.0  | 90   | 0.0149          | 1.0      |
| 0.0684        | 40.89 | 92   | 0.0155          | 1.0      |
| 0.0625        | 41.78 | 94   | 0.0151          | 1.0      |
| 0.0746        | 42.67 | 96   | 0.0143          | 1.0      |
| 0.1062        | 44.0  | 99   | 0.0133          | 1.0      |
| 0.0579        | 44.44 | 100  | 0.0132          | 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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