---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: cifar
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cifar10
      type: cifar10
      config: plain_text
      split: train[:5000]
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.883
---

<!-- 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. -->

# cifar

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: 0.4714
- Accuracy: 0.883

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7956        | 0.99  | 62   | 1.6395          | 0.817    |
| 0.8981        | 2.0   | 125  | 0.8510          | 0.858    |
| 0.6049        | 2.99  | 187  | 0.6666          | 0.878    |
| 0.5427        | 4.0   | 250  | 0.5796          | 0.88     |
| 0.4318        | 4.99  | 312  | 0.5110          | 0.889    |
| 0.3952        | 6.0   | 375  | 0.4339          | 0.907    |
| 0.3544        | 6.99  | 437  | 0.4432          | 0.902    |
| 0.3612        | 8.0   | 500  | 0.4213          | 0.898    |
| 0.3522        | 8.99  | 562  | 0.4474          | 0.884    |
| 0.3096        | 9.92  | 620  | 0.4714          | 0.883    |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3