---
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
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# 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: 0.2955
- Accuracy: 0.9241

## 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: 3e-05
- 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  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.4469        | 1.0   | 5313  | 0.8716   | 1.1871          |
| 0.7861        | 2.0   | 10626 | 0.9056   | 0.4685          |
| 0.732         | 3.0   | 15939 | 0.9139   | 0.3551          |
| 0.3327        | 4.0   | 21252 | 0.9199   | 0.3090          |
| 0.4886        | 5.0   | 26565 | 0.9241   | 0.2955          |


### Framework versions

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