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---
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
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: 0.0236
- 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
|