Age And Gender Classification using ResNet-50

Description

This project utilizes a pre-trained ResNet-50 model for age and gender prediction on the UTKFace dataset. The model is fine-tuned to predict the age and gender of individuals in facial images. The age is predicted as a regression task, while gender is treated as a binary classification problem.

Requirements

Make sure you have the following dependencies installed:

• Python (>=3.6)
• TensorFlow (>=2.0)
• NumPy
• Pillow (PIL)
• scikit-learn

Usage

1. Dataset Preparation:

   • Download the UTKFace dataset.
   • Organize the dataset into a directory named "UTKFace."

2. Preprocessing:

   • Adjust the batch_size variable to your liking.
   • Run the preprocessing script to load and process the images.

3. Model Training:

   • The pre-trained ResNet-50 model is loaded and fine-tuned on the UTKFace dataset.
   • Age is predicted using Mean Squared Error loss, and gender is predicted using Categorical Crossentropy loss.

4. Save Model:

   • The fine-tuned model is saved as 'model.h5.'

Testing

The Python script (gui.py) provides a graphical user interface (GUI) for testing the trained deep learning model used for gender and age prediction. The model has been fine-tuned on the UTKFace dataset and is loaded from the 'model.h5' file.

Requirements

Make sure you have the following dependencies installed:

• Python (>=3.6)
• OpenCV
• NumPy
• Keras
• Pillow (PIL)
• Matplotlib
• Tkinter

Ensure that the 'model.h5' file containing the trained model is available.

Usage

1. Run the GUI(gui.py file):

   • Execute the script to launch the Tkinter GUI.
   • Use the "Real-time" button to start capturing video from your webcam with real-time predictions.
   • Alternatively, use the "Upload an Image" button to select an image for prediction.

2. Real-time Prediction:

   • Press 'q' to exit the real-time prediction loop.

3. Image Upload:

   • Select an image using the file dialog.
   • The application will display the original image and predictions for gender and age.

Notes

• The application utilizes a pre-trained ResNet-50 model fine-tuned for gender and age prediction.
• Real-time prediction uses OpenCV for video capturing.
• Tkinter is used for the graphical user interface.
• Ensure the necessary dependencies are installed before running the application.