---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-cifar100-cifar100
  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-cifar100-cifar100

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co./google/vit-base-patch16-224-in21k) on the cifar100 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1612
- Accuracy: 0.2223

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 3.7207        | 1.0   | 5313  | 3.8632          | 0.0985   |
| 3.5093        | 2.0   | 10626 | 3.5664          | 0.1472   |
| 3.3675        | 3.0   | 15939 | 3.4389          | 0.166    |
| 2.9505        | 4.0   | 21252 | 3.2326          | 0.2093   |
| 3.1158        | 5.0   | 26565 | 3.1612          | 0.2223   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.0.1+cu117
- Datasets 3.0.0
- Tokenizers 0.19.1