Build a Deep Face Detection Model With Python and Tensorflow - Full Course

Dive into a comprehensive tutorial on building a deep face detection model using Python and TensorFlow. Learn to implement an Object Detection architecture through a step-by-step process, covering image collection, annotation, data partitioning, augmentation, model building, training, and real-time detection testing. Explore essential tools like labelme for annotation and Albumentations for data augmentation. Follow along as the instructor guides you through creating image loading functions, partitioning data, applying augmentations, preparing labels, and combining samples. Master the Functional API to construct a deep learning model, define custom loss functions and optimizers, and train your neural network. Conclude with testing and performing real-time detections to see your face detection model in action.

- START
- Explainer
- PART 1 - COLLECt IMAGES & ANNOTATE
- Breakdown Board
- Setting up and Getting Data
- PART 2 - PARTITION & AUGMENT DATA
- Review dataset and build Image Loading Function
- Partition Unaugmented Data
- Apply Image Augmentation on Images and Labels
- Build and Run Augmentation Pipeline
- Prepare Labels
- Combine Label and Image Samples
- PART 3 - BUILD & TRAIN THE DEEP LEARNING MODEL
- Build a Deep Learning Model using the Functional API
- Defining a Custom Loss Function & Optimizer
- Train a Neural Network
- PART 4 - TEST AND PERFORM REAL TIME DETECTIONS
- Final Results
- Ending