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: 0.0000
Accuracy: 1.0

Model description

This model was created by importing the dataset of the photos of ECG image into Google Colab from kaggle here: https://www.kaggle.com/datasets/erhmrai/ecg-image-data/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/1KC6twirtsc7N1kmlwY3IQKVUmSuK7zlh?usp=sharing

The possible classified data are:

N: Normal beat
S: Supraventricular premature beat
V: Premature ventricular contraction
F: Fusion of ventricular and normal beat
Q: Unclassifiable beat
M: myocardial infarction

ECG example:

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: 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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0476	1.0	697	0.0000	1.0

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

gianlab/swin-tiny-patch4-window7-224-finetuned-ecg-classification

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