CNN-Based Age Prediction System

CNN's

Trained a CNN-based age prediction model using PyTorch, the UTK dataset, and a ResNet10 architecture to predict ages with an average error of ±4 years—optimized via hyperparameter tuning and deployed with a Streamlit UI for real-time inference.

Published

September 17, 2024

Introduction

This project leverages a CNN-based approach to predict age from facial images using PyTorch and the UTK dataset. Utilizing a ResNet10 architecture, the model processes over 9,000 images to achieve an average prediction error of ±4 years. Through extensive hyperparameter optimization—including learning rate scheduling and regularization—the model’s accuracy improved by 28%, and a Streamlit UI was developed for real-time demographic analysis.

Output

Here is a screenshot from the Streamlit UI demonstrating real-time age prediction:

Age Prediction UI Screenshot

Models Used

  • ResNet10 CNN Architecture for age prediction
  • PyTorch for model training and inference
  • Streamlit for deploying a real-time user interface

Training

  • CNN Model Training
    • Includes data preprocessing, model training, and hyperparameter tuning.

Requirements

  • python 3.8+
  • pytorch
  • torchvision
  • pandas
  • numpy
  • streamlit
  • matplotlib or seaborn (for visualization)