swin-tiny-patch4-window7-224-finetuned-lungs-disease

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swin-tiny-patch4-window7-224-finetuned-lungs-disease

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: 0.2817
• 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

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

Framework versions

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