---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit-base-cifar10
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cifar10
      type: cifar10
      config: plain_text
      split: train
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.106
---

<!-- 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-cifar10

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 cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3302
- Accuracy: 0.106

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3324        | 1.0   | 664  | 2.3352          | 0.0967   |
| 2.3489        | 2.0   | 1328 | 2.3288          | 0.1049   |
| 2.4899        | 3.0   | 1992 | 2.4473          | 0.0989   |
| 2.479         | 4.0   | 2656 | 2.4894          | 0.1      |
| 2.4179        | 5.0   | 3320 | 2.4404          | 0.0947   |
| 2.3881        | 6.0   | 3984 | 2.3931          | 0.102    |
| 2.3597        | 7.0   | 4648 | 2.3744          | 0.0967   |
| 2.3721        | 8.0   | 5312 | 2.3667          | 0.0935   |
| 2.3456        | 9.0   | 5976 | 2.3495          | 0.1036   |
| 2.3361        | 10.0  | 6640 | 2.3473          | 0.1025   |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2