---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- vision
- 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
should probably proofread and complete it, then remove this comment. -->

# 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: 0.0247
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3215        | 1.0   | 6375  | 0.0630          | 0.9856   |
| 0.4689        | 2.0   | 12750 | 0.0377          | 0.9906   |
| 0.3258        | 3.0   | 19125 | 0.0364          | 0.9908   |
| 0.3094        | 4.0   | 25500 | 0.0269          | 0.9936   |
| 0.2981        | 5.0   | 31875 | 0.0247          | 0.9949   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1