---
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-lungs-disease
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8745874587458746
---

<!-- 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-lungs-disease

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: 0.2817
- Accuracy: 0.8746

## Model description

This model was created by importing the dataset of the chest x-rays images into Google Colab from kaggle here: 

https://www.kaggle.com/datasets/omkarmanohardalvi/lungs-disease-dataset-4-types . 

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/1rNKeA25BR05iMUvKFvRD8SkySBOlO4AC?usp=sharing

'Viral Pneumonia', 'Corona Virus Disease', 'Normal', 'Tuberculosis', 'Bacterial Pneumonia'

The possible classified data are:
<ul>
<li>Viral Pneumonia</li>
<li>Corona Virus Disease</li>
<li>Normal</li>
<li>Tuberculosis</li>
<li>Bacterial Pneumonia</li>
</ul>

### X-rays image example:

![Screenshot](lung.png)


## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7851        | 0.98  | 21   | 0.4674          | 0.8152   |
| 0.4335        | 2.0   | 43   | 0.3662          | 0.8515   |
| 0.3231        | 2.98  | 64   | 0.3361          | 0.8581   |
| 0.3014        | 4.0   | 86   | 0.2817          | 0.8746   |
| 0.252         | 4.88  | 105  | 0.3071          | 0.8713   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2