Gender and Age Classification

This project leverages deep learning to classify gender and predict age from images. It utilizes a custom CNN for age prediction and a pre-trained VGG16 model for gender classification. The models are deployed through a user-friendly web interface built using Streamlit

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Table of Contents

• About the Project
• Features
• Getting Started
• Installation
• Usage
• Project Structure
• Example Output

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About the Project

The Gender and Age Classification project is a tool that predicts:

• Gender: Identifies if the person in the image is male or female.
• Age Category: Classifies the age into one of the following categories:
  • Young (0-19 years)
  • Youth (20-40 years)
  • Senior (40-60 years)
  • Old (60+ years)

The age prediction model is built using a custom CNN architecture, while the gender classification model leverages the pre-trained VGG16 . Both models are deployed using Streamlit to provide an interactive web interface.

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Features

• Data Processing: Preprocesses uploaded images for compatibility with the trained models.
• Gender Classification: Uses the pre-trained VGG16 model to predict male or female.
• Age Categorization: Uses another CNN to classify age into predefined categories.
• Web Application: Provides an intuitive interface for uploading images and viewing results.

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Getting Started

Follow these steps to set up and run the project locally:

Prerequisites

• Python 3.7+
• Streamlit library
• Pre-trained models (age_model.h5 and gender_classification_cnn_model.h5)

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Installation

1. Clone the repository:

   git clone <repository-link>
   cd Gender_and_Age_Classification

2. Install the required Python packages:

   pip install -r requirements.txt

3. Ensure the pre-trained models are in the project directory:

   • age_model.h5: For age prediction.
   • gender_classification_cnn_model.h5: For gender classification.

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Usage

To use the application, follow these steps:

1. Open the web application directly by clicking here.
2. Upload an image using the provided interface.
3. The application will process the image and display the predicted gender and age category.

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Project Structure

The project files are organized as follows:

Gender_and_Age_Classification/
├── Age_model.ipynb               # Notebook for training the age prediction model
├── Gender_model_vgg16-cnn.ipynb  # Notebook for training the gender classification model
├── deployment.py                 # Streamlit application script
├── requirements.txt              # Python dependencies file
├── age_model.h5                  # Pre-trained age prediction model
├── gender_classification_cnn_model.h5  # Pre-trained gender classification model
└── README.md                     # Project documentation

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Example Output

User Interface

Predicted Results