---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mnist
metrics:
- accuracy
model-index:
- name: vit-base-mnist
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: mnist
      type: mnist
      config: mnist
      split: train
      args: mnist
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9948888888888889
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-mnist

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 mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0236
- Accuracy: 0.9949

## 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: 2e-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  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3717        | 1.0   | 6375  | 0.0522          | 0.9893   |
| 0.3453        | 2.0   | 12750 | 0.0370          | 0.9906   |
| 0.3736        | 3.0   | 19125 | 0.0308          | 0.9916   |
| 0.3224        | 4.0   | 25500 | 0.0269          | 0.9939   |
| 0.2846        | 5.0   | 31875 | 0.0236          | 0.9949   |


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.4.0
- Tokenizers 0.12.1