being new to Deep Learning i am struggling to understand the difference between different state of the art algos and their uses. like how is resnet or vgg diff from yolo or rcnn family. are they subcomponents of these detection models? also are SSDs another family like yolo or rcnn?
ResNet is a family of neural networks (using residual functions). A lot of neural network use ResNet architecture, for example:
It is commonly used as a backbone (also called encoder or feature extractor) for image classification, object detection, object segmentation and many more. There is others families of nets like VGG, EfficientNets etc...
FasterRCNN/RCN, YOLO and SSD are more like "pipeline" for object detection. For example, FasterRCNN use a backbone for feature extraction (like ResNet50) and a second network called RPN (Region Proposal Network). Take a look a this article which present the most common "pipeline" for object detection.