Learn A-Z Deep Learning in 15 Days In this Lecture, will learn about Objection Detection with Retinanet You Only Look Once YOLO first takes an input image. The framework then divides the input image into grids (say a n X n grid- Each grid predict): Predicts B boundary boxes and each box has one box confidence score Pr(Object) ∗ IOU Detects one object only regardless of the number of boxes B, Predicts C conditional class probabilities (one...

Learn A-Z Deep Learning in 15 Days In this Lecture, will learn about Objection Detection with Retinanet Retinanet The one-stage RetinaNet network architecture uses a Feature Pyramid Network (FPN) backbone on top of a feedforward ResNet architecture (a) to generate a rich, multi-scale convolutional feature pyramid (b). To this backbone, RetinaNet attaches two subnetworks, one for classifying anchor boxes (c) and one for regressing from anchor...

Learn A-Z Deep Learning in 15 Days In this Lecture, will learn about Objection Detection with Region-CNN Object Detection is the process of localization and Recognition. Region-CNN or RCNN RCNN takes the input image- Performs the sliding window Proposes bounding box using Selective Search Algo. CNN classifies the Propose region Linear Regressor generates the tighter bounding box Disadvantages Slow: calculate a feature map (one CNN forward...

Learn A-Z Deep Learning in 15 Days In this Lecture, will learn about Classification with Transfer Learning What is Transfer Learning? Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point. The pre-trained model is a model created by someone else to...