---
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: []
datasets:
- pcuenq/oxford-pets
---



# 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: 0.2139
- Accuracy: 0.9350

## Model description

The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. 
Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.

## Intended uses & limitations

### Intended Uses
This model is intended for image classification tasks, particularly those aligned with the ImageNet dataset's domain. 
It can also serve as a feature extractor for transfer learning on smaller, domain-specific datasets. 

### Limitations
This model may not generalize well to datasets that differ significantly from ImageNet. 
It is computationally intensive and may be unsuitable for use cases requiring low-latency predictions.

## Training and evaluation data
### Training Data
Pretraining Data: ImageNet-21k (14M images, 21k classes).
Fine-tuning Data: ImageNet ILSVRC2012 (1M images, 1k classes).

### Evaluation Data
Dataset: ImageNet ILSVRC2012 validation set.
Size: 50,000 images across 1,000 classes.
Metrics: Loss (0.2031), Accuracy (94.59%).


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3953        | 1.0   | 370  | 0.2863          | 0.9310   |
| 0.1918        | 2.0   | 740  | 0.2139          | 0.9391   |
| 0.165         | 3.0   | 1110 | 0.2008          | 0.9418   |
| 0.1476        | 4.0   | 1480 | 0.1912          | 0.9432   |
| 0.1359        | 5.0   | 1850 | 0.1872          | 0.9445   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0