Vit Base Patch16 224 In21k Male Or Female Eyes

🚀 vit-base-patch16-224-in21k_male_or_female_eyes

This model is a fine - tuned version of google/vit-base-patch16-224-in21k for binary classification of male and female eyes.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0810
• Accuracy: 0.9727
• F1: 0.9741
• Recall: 0.9666
• Precision: 0.9818

✨ Features

• Binary Classification: This is a binary classification model to distinguish between male and female eyes.
• High Performance: Achieves high accuracy, F1, recall, and precision scores on the evaluation set.

📚 Documentation

Model description

This is a binary classification model to distinguish between male and female eyes. 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/Male%20or%20Female%20Eyes/are_they_male_or_female_eyes_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/pavelbiz/eyes-rtte

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

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.1
• Datasets 2.8.0
• Tokenizers 0.12.1

📄 License

This project is licensed under the Apache 2.0 license.