
# vit-base-patch16-224-in21k_covid_19_ct_scans

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).

It achieves the following results on the evaluation set:
- Loss: 0.1727
- Accuracy: 0.94
- F1: 0.9379
- Recall: 0.8947
- Precision: 0.9855

## Model description

This is a binary classification model to distinguish between CT scans that detect COVID-19 and those who do not.

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

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/luisblanche/covidct

_Sample Images From Dataset:_

![Sample Images](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Binary%20Classification/COVID19%20Lung%20CT%20Scans/Images/Sample%20Images.png)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6742        | 1.0   | 38   | 0.4309          | 0.9      | 0.8993 | 0.8816 | 0.9178    |
| 0.6742        | 2.0   | 76   | 0.3739          | 0.8467   | 0.8686 | 1.0    | 0.7677    |
| 0.6742        | 3.0   | 114  | 0.1727          | 0.94     | 0.9379 | 0.8947 | 0.9855    |


### Framework versions

- Transformers 4.22.2
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.12.1
ct_covid ct_noncovid