---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-oxford-iiit-pets

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the cepha-cutoutCLAHE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6194
- Accuracy: 0.7639

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0901        | 1.0   | 32   | 0.9278          | 0.4931   |
| 0.5383        | 2.0   | 64   | 0.6985          | 0.6319   |
| 0.2707        | 3.0   | 96   | 0.6691          | 0.7222   |
| 0.0366        | 4.0   | 128  | 0.9557          | 0.6806   |
| 0.0066        | 5.0   | 160  | 0.8927          | 0.7083   |
| 0.0075        | 6.0   | 192  | 1.2046          | 0.7014   |
| 0.0013        | 7.0   | 224  | 1.2583          | 0.7083   |
| 0.0006        | 8.0   | 256  | 1.3180          | 0.6944   |
| 0.0004        | 9.0   | 288  | 1.3468          | 0.7014   |
| 0.0002        | 10.0  | 320  | 1.3582          | 0.6875   |
| 0.0002        | 11.0  | 352  | 1.3868          | 0.6875   |
| 0.0002        | 12.0  | 384  | 1.4094          | 0.6806   |
| 0.0002        | 13.0  | 416  | 1.4392          | 0.6806   |
| 0.0002        | 14.0  | 448  | 1.4536          | 0.6875   |
| 0.0001        | 15.0  | 480  | 1.4695          | 0.6875   |
| 0.0001        | 16.0  | 512  | 1.4850          | 0.6875   |
| 0.0001        | 17.0  | 544  | 1.5004          | 0.6875   |
| 0.0001        | 18.0  | 576  | 1.5110          | 0.6875   |
| 0.0001        | 19.0  | 608  | 1.5219          | 0.6875   |
| 0.0001        | 20.0  | 640  | 1.5340          | 0.6875   |
| 0.0001        | 21.0  | 672  | 1.5461          | 0.6875   |
| 0.0001        | 22.0  | 704  | 1.5541          | 0.6875   |
| 0.0001        | 23.0  | 736  | 1.5624          | 0.6875   |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0