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--- |
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license: apache-2.0 |
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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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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: vit-base-patch16-224-in21k_covid_19_ct_scans |
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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: train |
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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: 0.94 |
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- name: F1 |
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type: f1 |
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value: 0.9379310344827586 |
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- name: Recall |
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type: recall |
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value: 0.8947368421052632 |
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- name: Precision |
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type: precision |
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value: 0.9855072463768116 |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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# vit-base-patch16-224-in21k_covid_19_ct_scans |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1727 |
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- Accuracy: 0.94 |
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- F1: 0.9379 |
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- Recall: 0.8947 |
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- Precision: 0.9855 |
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## Model description |
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This is a binary classification model to distinguish between CT scans that detect COVID-19 and those who do not. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Binary%20Classification/COVID19%20Lung%20CT%20Scans/COVID19_Lung_CT_Scans_ViT.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/luisblanche/covidct |
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_Sample Images From Dataset:_ |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.6742 | 1.0 | 38 | 0.4309 | 0.9 | 0.8993 | 0.8816 | 0.9178 | |
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| 0.6742 | 2.0 | 76 | 0.3739 | 0.8467 | 0.8686 | 1.0 | 0.7677 | |
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| 0.6742 | 3.0 | 114 | 0.1727 | 0.94 | 0.9379 | 0.8947 | 0.9855 | |
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### Framework versions |
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- Transformers 4.22.2 |
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- Pytorch 1.12.1 |
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- Datasets 2.5.2 |
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- Tokenizers 0.12.1 |
