Version 1.0

Face Detection Using TensorFlow and OpenCV

This release provides an implementation of a real-time face detection system using TensorFlow and OpenCV. The project involves building a deep learning model that can detect faces in images and video streams. Below is an overview of the key features and components included in this release.

Key Features

Data Collection and Annotation

• Capture Images: Use OpenCV to capture images from a webcam.
• Annotate Images: Annotate images using the labelme tool for preparing training data.

Data Preprocessing

• Load Images: Load and preprocess images using the TensorFlow Data Pipeline.
• Dataset Partitioning: Partition the dataset into training, testing, and validation sets.
• Data Augmentation: Apply data augmentation techniques using Albumentations to improve model robustness.

Model Architecture

• Base Network: Leverage the VGG16 model as a base network for feature extraction.
• Custom Model: Use TensorFlow's Keras API to build a custom model for face detection with localization and classification layers.

Training

• Custom Loss Functions: Implement custom loss functions for localization and classification.
• Optimizer: Train the model using the Adam optimizer with a learning rate schedule.
• Performance Monitoring: Monitor performance using TensorBoard for visualization of training metrics.

Evaluation and Prediction

• Model Evaluation: Evaluate the model's performance on test data.
• Real-time Detection: Implement real-time face detection using OpenCV with the trained model.

Model Saving

• Save Model: Save the trained model for future inference and deployment.

Installation

To get started with this project, ensure you have the necessary dependencies installed. You can install them using the following command:

pip install labelme tensorflow tensorflow-gpu opencv-python matplotlib albumentations