# Image Gender Classifier Using TensorFlow and ViT
This repository contains a deep learning project that uses TensorFlow and the Vision Transformer (ViT) model to classify images by gender. The model is trained on a dataset available on Kaggle, which can be found [here](https://www.kaggle.com/datasets/trainingdatapro/gender-detection-and-classification-image-dataset).
## Project Overview
The goal of this project is to accurately classify human images into male or female categories using a Vision Transformer (ViT), an approach that leverages the capabilities of transformers in the field of computer vision.
## Dataset
The dataset used for training is the Gender Detection and Classification Image Dataset from Kaggle. It contains labeled images of males and females, which are used to train the ViT model.
## Model
The model is built using TensorFlow and includes the following steps:
1. **Data Loading and Preprocessing**: The data is loaded using TensorFlow's `ImageDataGenerator` and preprocessed to match the input requirements of the ViT model.
2. **Model Definition**: A Vision Transformer model is defined and compiled with appropriate loss functions and optimizers.
3. **Training**: The model is trained on the dataset with specified hyperparameters.
4. **Evaluation**: After training, the model's performance is evaluated using a separate test set.
5. **Testing**: The model is tested with new data to predict genders.
## Installation and Usage
Instructions for setting up the environment, installing dependencies, and running the project are provided below.
### Prerequisites
- Python 3.x
- TensorFlow 2.x
- Access to the Kaggle dataset
### Setup
1. Clone the repository:
```bash
git clone <repository-url>
cd <repository-name>- Install required packages:
pip install -r requirements.txt- Download the dataset from Kaggle and place it in the appropriate directory.
To train and evaluate the model, run:
python train.pyTo test the model with new data, run:
python test.pyThe trained model achieves an accuracy of XX% on the test set. (Replace XX% with your actual results)
- Data Augmentation: To improve model generalization, consider using more extensive data augmentation techniques.
- Hyperparameter Tuning: Experiment with different learning rates, batch sizes, and other hyperparameters.
- Advanced Architectures: Explore using different or more complex architectures like EfficientNet or ensemble methods.
Contributions to this project are welcome. Please feel free to fork the repository, make changes, and submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- Dataset provided by Kaggle.
- TensorFlow team for providing a comprehensive deep learning framework.
Remember to replace `<repository-url>` and `<repository-name>` with your actual repository information, and update any sections to reflect the specifics of your project. The "Results" and "Suggestions for Improvement" sections are particularly important for readers to understand the performance of your model and potential areas for further research or development.
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