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# 378A1_results_384_4cate_1

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4707
- Accuracy: 0.8997

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8756        | 1.0   | 37   | 0.5714          | 0.7908   |
| 0.4508        | 2.0   | 74   | 0.3688          | 0.8418   |
| 0.2344        | 3.0   | 111  | 0.3064          | 0.8741   |
| 0.1445        | 4.0   | 148  | 0.2948          | 0.8946   |
| 0.0774        | 5.0   | 185  | 0.3461          | 0.8793   |
| 0.0393        | 6.0   | 222  | 0.3229          | 0.8997   |
| 0.0164        | 7.0   | 259  | 0.3441          | 0.9048   |
| 0.0222        | 8.0   | 296  | 0.4192          | 0.9099   |
| 0.0125        | 9.0   | 333  | 0.4443          | 0.8810   |
| 0.0029        | 10.0  | 370  | 0.4007          | 0.9116   |
| 0.0014        | 11.0  | 407  | 0.4277          | 0.9150   |
| 0.0003        | 12.0  | 444  | 0.4445          | 0.9014   |
| 0.0002        | 13.0  | 481  | 0.4437          | 0.9031   |
| 0.0002        | 14.0  | 518  | 0.4481          | 0.9048   |
| 0.0002        | 15.0  | 555  | 0.4512          | 0.9031   |
| 0.0002        | 16.0  | 592  | 0.4537          | 0.9014   |
| 0.0002        | 17.0  | 629  | 0.4562          | 0.9014   |
| 0.0002        | 18.0  | 666  | 0.4583          | 0.9014   |
| 0.0001        | 19.0  | 703  | 0.4594          | 0.9014   |
| 0.0001        | 20.0  | 740  | 0.4615          | 0.9031   |
| 0.0001        | 21.0  | 777  | 0.4635          | 0.9031   |
| 0.0001        | 22.0  | 814  | 0.4652          | 0.9031   |
| 0.0001        | 23.0  | 851  | 0.4659          | 0.9031   |
| 0.0001        | 24.0  | 888  | 0.4679          | 0.8997   |
| 0.0001        | 25.0  | 925  | 0.4681          | 0.9014   |
| 0.0001        | 26.0  | 962  | 0.4688          | 0.8997   |
| 0.0001        | 27.0  | 999  | 0.4695          | 0.8997   |
| 0.0001        | 28.0  | 1036 | 0.4701          | 0.8997   |
| 0.0001        | 29.0  | 1073 | 0.4706          | 0.8997   |
| 0.0001        | 30.0  | 1110 | 0.4707          | 0.8997   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3

label_0 label_1 label_2 label_3