vit-base-patch16-224-in21k_covid_19_ct_scans

This model is a fine-tuned version of 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