---
tags:
- image-classification
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit_cifar10_classification_tmp
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cifar10
      type: cifar10
      config: plain_text
      split: test
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9781
---

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# vit_cifar10_classification_tmp

This model is a fine-tuned version of [againeureka/vit_cifar10_classification_tmp](https://huggingface.co/againeureka/vit_cifar10_classification_tmp) on the cifar10 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0945
- Accuracy: 0.9781

## 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: 0.0002
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2199        | 0.26  | 100  | 0.1853          | 0.9678   |
| 0.0999        | 0.51  | 200  | 0.1270          | 0.9713   |
| 0.0944        | 0.77  | 300  | 0.0945          | 0.9781   |


### Framework versions

- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.2