metadata

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

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

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

The possible classified data are:

Viral Pneumonia
Corona Virus Disease
Normal
Tuberculosis
Bacterial Pneumonia

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