--- 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](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 The possible classified data are: