Vit Base Patch16 224 In21k Covid 19 Ct Scans

🚀 vit-base-patch16-224-in21k_covid_19_ct_scans

This model is a fine - tuned version of google/vit-base-patch16-224-in21k, used for binary classification of COVID - 19 CT scans.

🚀 Quick Start

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

✨ Features

• Binary Classification: This is a binary classification model to distinguish between CT scans that detect COVID - 19 and those who do not.
• High Performance: Achieves high accuracy, F1, recall, and precision on the evaluation set.

📚 Documentation

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:

🔧 Technical Details

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

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1
• Datasets 2.5.2
• Tokenizers 0.12.1

📄 License

This model is licensed under the Apache - 2.0 license.

Information Table