---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-letter
  results: []
datasets:
- pittawat/letter_recognition
language:
- en
---

<!-- 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-letter

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 pittawat/letter_recognition dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Accuracy: 0.9881

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5539        | 0.12  | 100  | 0.5576          | 0.9308   |
| 0.2688        | 0.25  | 200  | 0.2371          | 0.9665   |
| 0.1568        | 0.37  | 300  | 0.1829          | 0.9688   |
| 0.1684        | 0.49  | 400  | 0.1611          | 0.9662   |
| 0.1584        | 0.62  | 500  | 0.1340          | 0.9673   |
| 0.1569        | 0.74  | 600  | 0.1933          | 0.9531   |
| 0.0992        | 0.86  | 700  | 0.1031          | 0.9781   |
| 0.0573        | 0.98  | 800  | 0.1024          | 0.9781   |
| 0.0359        | 1.11  | 900  | 0.0950          | 0.9804   |
| 0.0961        | 1.23  | 1000 | 0.1200          | 0.9723   |
| 0.0334        | 1.35  | 1100 | 0.0995          | 0.975    |
| 0.0855        | 1.48  | 1200 | 0.0791          | 0.9815   |
| 0.0902        | 1.6   | 1300 | 0.0981          | 0.9765   |
| 0.0583        | 1.72  | 1400 | 0.1192          | 0.9712   |
| 0.0683        | 1.85  | 1500 | 0.0692          | 0.9846   |
| 0.1188        | 1.97  | 1600 | 0.0931          | 0.9785   |
| 0.0366        | 2.09  | 1700 | 0.0919          | 0.9804   |
| 0.0276        | 2.21  | 1800 | 0.0667          | 0.9846   |
| 0.0309        | 2.34  | 1900 | 0.0599          | 0.9858   |
| 0.0183        | 2.46  | 2000 | 0.0892          | 0.9769   |
| 0.0431        | 2.58  | 2100 | 0.0663          | 0.985    |
| 0.0424        | 2.71  | 2200 | 0.0643          | 0.9862   |
| 0.0453        | 2.83  | 2300 | 0.0646          | 0.9862   |
| 0.0528        | 2.95  | 2400 | 0.0550          | 0.985    |
| 0.0045        | 3.08  | 2500 | 0.0579          | 0.9846   |
| 0.007         | 3.2   | 2600 | 0.0517          | 0.9885   |
| 0.0048        | 3.32  | 2700 | 0.0584          | 0.9865   |
| 0.019         | 3.44  | 2800 | 0.0560          | 0.9873   |
| 0.0038        | 3.57  | 2900 | 0.0515          | 0.9881   |
| 0.0219        | 3.69  | 3000 | 0.0527          | 0.9881   |
| 0.0117        | 3.81  | 3100 | 0.0523          | 0.9888   |
| 0.0035        | 3.94  | 3200 | 0.0559          | 0.9865   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2