vit-base-patch16-224-in21k_male_or_female_eyes
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
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
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.1998 | 1.0 | 577 | 0.2365 | 0.9072 | 0.9196 | 0.9976 | 0.8530 |
| 0.0846 | 2.0 | 1154 | 0.0810 | 0.9727 | 0.9741 | 0.9666 | 0.9818 |
| 0.0309 | 3.0 | 1731 | 0.0852 | 0.9809 | 0.9821 | 0.9837 | 0.9805 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1
