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# 378A1_results_2

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: 0.5007
- Accuracy: 0.8861

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2051        | 1.0   | 37   | 1.0438          | 0.6429   |
| 0.6643        | 2.0   | 74   | 0.6103          | 0.7925   |
| 0.4615        | 3.0   | 111  | 0.4720          | 0.8435   |
| 0.3136        | 4.0   | 148  | 0.3668          | 0.8776   |
| 0.278         | 5.0   | 185  | 0.3650          | 0.8622   |
| 0.1875        | 6.0   | 222  | 0.3705          | 0.8690   |
| 0.1215        | 7.0   | 259  | 0.4093          | 0.8741   |
| 0.0885        | 8.0   | 296  | 0.3428          | 0.9014   |
| 0.0497        | 9.0   | 333  | 0.3854          | 0.8759   |
| 0.0348        | 10.0  | 370  | 0.4291          | 0.8707   |
| 0.0301        | 11.0  | 407  | 0.4464          | 0.8895   |
| 0.0246        | 12.0  | 444  | 0.4208          | 0.8929   |
| 0.0218        | 13.0  | 481  | 0.4256          | 0.8912   |
| 0.0198        | 14.0  | 518  | 0.4300          | 0.8878   |
| 0.0179        | 15.0  | 555  | 0.4403          | 0.8861   |
| 0.0165        | 16.0  | 592  | 0.4481          | 0.8861   |
| 0.0155        | 17.0  | 629  | 0.4554          | 0.8878   |
| 0.0146        | 18.0  | 666  | 0.4632          | 0.8878   |
| 0.0137        | 19.0  | 703  | 0.4691          | 0.8844   |
| 0.0129        | 20.0  | 740  | 0.4747          | 0.8861   |
| 0.0125        | 21.0  | 777  | 0.4792          | 0.8844   |
| 0.0119        | 22.0  | 814  | 0.4840          | 0.8844   |
| 0.0113        | 23.0  | 851  | 0.4875          | 0.8861   |
| 0.0111        | 24.0  | 888  | 0.4924          | 0.8844   |
| 0.0108        | 25.0  | 925  | 0.4947          | 0.8844   |
| 0.0105        | 26.0  | 962  | 0.4966          | 0.8844   |
| 0.0104        | 27.0  | 999  | 0.4988          | 0.8861   |
| 0.0102        | 28.0  | 1036 | 0.4997          | 0.8861   |
| 0.0101        | 29.0  | 1073 | 0.5005          | 0.8861   |
| 0.01          | 30.0  | 1110 | 0.5007          | 0.8861   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3

label_0 label_1 label_2 label_3