---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-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-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: 0.3168
- 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: 128
- 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5136        | 1.0   | 47   | 1.1031          | 0.8430   |
| 0.5547        | 2.0   | 94   | 0.5232          | 0.9269   |
| 0.4111        | 3.0   | 141  | 0.3988          | 0.9310   |
| 0.3438        | 4.0   | 188  | 0.3553          | 0.9337   |
| 0.298         | 5.0   | 235  | 0.3448          | 0.9296   |


### Framework versions

- Transformers 4.39.2
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.15.2