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# bachh

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: 0.5198
- Accuracy: 0.8654

## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8279        | 0.11  | 100  | 0.9659          | 0.6697   |
| 0.7801        | 0.22  | 200  | 0.8758          | 0.6725   |
| 0.7136        | 0.34  | 300  | 0.7788          | 0.7178   |
| 0.7302        | 0.45  | 400  | 0.7041          | 0.7438   |
| 0.6793        | 0.56  | 500  | 0.6882          | 0.7431   |
| 0.5872        | 0.67  | 600  | 0.6480          | 0.7683   |
| 0.5711        | 0.78  | 700  | 0.6490          | 0.7588   |
| 0.6003        | 0.9   | 800  | 0.6116          | 0.7727   |
| 0.4382        | 1.01  | 900  | 0.6016          | 0.7883   |
| 0.4666        | 1.12  | 1000 | 0.5848          | 0.7803   |
| 0.3944        | 1.23  | 1100 | 0.6039          | 0.7745   |
| 0.3805        | 1.35  | 1200 | 0.4929          | 0.8221   |
| 0.3795        | 1.46  | 1300 | 0.5391          | 0.8048   |
| 0.3727        | 1.57  | 1400 | 0.5303          | 0.8149   |
| 0.3658        | 1.68  | 1500 | 0.5471          | 0.8160   |
| 0.3289        | 1.79  | 1600 | 0.5170          | 0.8184   |
| 0.2832        | 1.91  | 1700 | 0.4795          | 0.8334   |
| 0.2048        | 2.02  | 1800 | 0.4942          | 0.8300   |
| 0.2085        | 2.13  | 1900 | 0.4743          | 0.8394   |
| 0.1449        | 2.24  | 2000 | 0.4642          | 0.8469   |
| 0.1662        | 2.35  | 2100 | 0.4669          | 0.8426   |
| 0.1603        | 2.47  | 2200 | 0.4721          | 0.8452   |
| 0.1079        | 2.58  | 2300 | 0.5236          | 0.8342   |
| 0.1952        | 2.69  | 2400 | 0.4448          | 0.8493   |
| 0.2091        | 2.8   | 2500 | 0.4959          | 0.8518   |
| 0.166         | 2.91  | 2600 | 0.5036          | 0.8356   |
| 0.055         | 3.03  | 2700 | 0.4854          | 0.8497   |
| 0.0639        | 3.14  | 2800 | 0.4987          | 0.8605   |
| 0.0421        | 3.25  | 2900 | 0.5258          | 0.8543   |
| 0.0292        | 3.36  | 3000 | 0.5287          | 0.8601   |
| 0.0136        | 3.48  | 3100 | 0.5250          | 0.8593   |
| 0.0421        | 3.59  | 3200 | 0.5192          | 0.8647   |
| 0.0307        | 3.7   | 3300 | 0.5251          | 0.8569   |
| 0.0536        | 3.81  | 3400 | 0.5300          | 0.8611   |
| 0.0269        | 3.92  | 3500 | 0.5198          | 0.8654   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2

ak bcc bkl df mel nv scc vasc