Overview of the class, reminders from Machine Learning,…
Penalization, Dropout, Data augmentation
ImageNet, Revolution of depth, Classical classifiers architectures
Motivation, Principle, Types of TL, Weights unfreezing, Pre-training datasets, SoTA
Principle, IoU and mAP, Classical Object Detection, Segmentation and Mask-RCNN, SoTA
