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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-base-patch16-224-in21k_covid_19_ct_scans
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.94
- name: F1
type: f1
value: 0.9379310344827586
- name: Recall
type: recall
value: 0.8947368421052632
- name: Precision
type: precision
value: 0.9855072463768116
language:
- en
pipeline_tag: image-classification
---
# 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:_
## 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 |