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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: swin-tiny-patch4-window7-224-finetuned-lungs-disease |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8745874587458746 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# swin-tiny-patch4-window7-224-finetuned-lungs-disease |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2817 |
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- Accuracy: 0.8746 |
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## Model description |
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This model was created by importing the dataset of the chest x-rays images into Google Colab from kaggle here: |
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https://www.kaggle.com/datasets/omkarmanohardalvi/lungs-disease-dataset-4-types . |
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I then used the image classification tutorial here: |
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https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb |
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obtaining the following notebook: |
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https://colab.research.google.com/drive/1rNKeA25BR05iMUvKFvRD8SkySBOlO4AC?usp=sharing |
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'Viral Pneumonia', 'Corona Virus Disease', 'Normal', 'Tuberculosis', 'Bacterial Pneumonia' |
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The possible classified data are: |
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<ul> |
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<li>Viral Pneumonia</li> |
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<li>Corona Virus Disease</li> |
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<li>Normal</li> |
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<li>Tuberculosis</li> |
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<li>Bacterial Pneumonia</li> |
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</ul> |
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### X-rays image example: |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.7851 | 0.98 | 21 | 0.4674 | 0.8152 | |
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| 0.4335 | 2.0 | 43 | 0.3662 | 0.8515 | |
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| 0.3231 | 2.98 | 64 | 0.3361 | 0.8581 | |
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| 0.3014 | 4.0 | 86 | 0.2817 | 0.8746 | |
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| 0.252 | 4.88 | 105 | 0.3071 | 0.8713 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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