Deep Dive into Object Detection with YOLO

An understanding of different types of object detection models

An understanding of YOLO architecture and its significance in object detection

Familiarity with core concepts of YOLO, including Intersection over Union (IoU) and non-maximum suppression (NMS)

Hands-on experience implementing data augmentation techniques for improved model performance

Working knowledge of overfitting, its implications, and methods to prevent it

An understanding of topics like batch normalization, optimizers in YOLO, learning rate scheduling, mAP score evaluation, and loss calculations for object detection