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---
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:
## 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
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