---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-parkinson-classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9090909090909091
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-finetuned-parkinson-classification

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4966
- Accuracy: 0.9091

## Model description

This model was created by importing the dataset of spiral drawings made by both parkinsons patients and healthy people into Google Colab from kaggle here: https://www.kaggle.com/datasets/kmader/parkinsons-drawings/data. I then used the image classification tutorial here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb

obtaining the following notebook:

https://colab.research.google.com/drive/1oRjwgHjmaQYRU1qf-TTV7cg1qMZXgMaO?usp=sharing

The possible classified data are:
<ul>
<li>Healthy</li>
<li>Parkinson</li>
</ul>

### Spiral drawing example:

![Screenshot](V13PE02.png)

## Intended uses & limitations

Acknowledgements

The data came from the paper: Zham P, Kumar DK, Dabnichki P, Poosapadi Arjunan S and Raghav S (2017) Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral. Front. Neurol. 8:435. doi: 10.3389/fneur.2017.00435

https://www.frontiersin.org/articles/10.3389/fneur.2017.00435/full

Data licence : https://creativecommons.org/licenses/by-nc-nd/4.0/

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 0.6801          | 0.4545   |
| No log        | 2.0   | 3    | 0.8005          | 0.3636   |
| No log        | 3.0   | 5    | 0.6325          | 0.6364   |
| No log        | 4.0   | 6    | 0.5494          | 0.8182   |
| No log        | 5.0   | 7    | 0.5214          | 0.8182   |
| No log        | 6.0   | 9    | 0.5735          | 0.7273   |
| 0.3063        | 7.0   | 11   | 0.4966          | 0.9091   |
| 0.3063        | 8.0   | 12   | 0.4557          | 0.9091   |
| 0.3063        | 9.0   | 13   | 0.4444          | 0.9091   |
| 0.3063        | 10.0  | 15   | 0.6226          | 0.6364   |
| 0.3063        | 11.0  | 17   | 0.8224          | 0.4545   |
| 0.3063        | 12.0  | 18   | 0.8127          | 0.4545   |
| 0.3063        | 13.0  | 19   | 0.7868          | 0.4545   |
| 0.2277        | 14.0  | 21   | 0.8195          | 0.4545   |
| 0.2277        | 15.0  | 23   | 0.7499          | 0.4545   |
| 0.2277        | 16.0  | 24   | 0.7022          | 0.5455   |
| 0.2277        | 17.0  | 25   | 0.6755          | 0.5455   |
| 0.2277        | 18.0  | 27   | 0.6277          | 0.6364   |
| 0.2277        | 19.0  | 29   | 0.5820          | 0.6364   |
| 0.1867        | 20.0  | 30   | 0.5784          | 0.6364   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0