Region based CNN - RCNN | complete deep learning course in Telugu | part 9 скачать в хорошем качестве

Region based CNN - RCNN | complete deep learning course in Telugu | part 9

This video explains R-CNN (Region-based Convolutional Neural Network) from first principles, focusing on why it was introduced and how it works internally. We break down the complete R-CNN pipeline: Why object detection is different from image classification How region proposals are generated using selective search How CNN features are extracted from each region Why R-CNN uses separate stages for classification and bounding box regression More importantly, this video highlights the core limitations of R-CNN — slow inference, repeated computation, and lack of end-to-end training — which directly motivated later models like Fast R-CNN, Faster R-CNN, YOLO, and SSD. This is a conceptual and theoretical explanation, intended to build a solid foundation before moving to optimized and real-time object detection architectures.