---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- pcuenq/oxford-pets
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
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# 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 pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2048
- Accuracy: 0.9432

## 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: 0.0003
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7025        | 1.0   | 185  | 0.3849          | 0.9242   |
| 0.2944        | 2.0   | 370  | 0.2704          | 0.9337   |
| 0.2129        | 3.0   | 555  | 0.2417          | 0.9378   |
| 0.1761        | 4.0   | 740  | 0.2305          | 0.9350   |
| 0.157         | 5.0   | 925  | 0.2281          | 0.9378   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1