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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