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

This model is a fine-tuned version of 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:

• Healthy
• Parkinson

Spiral drawing example:

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