After the yolov1 algorithm was released it caused a strong reaction among the public, and many researchers compared the algorithm with Fast Rcnn; yolov1 showed a fair number of deviations in object localization and had low recall. So in almost the same year the authors released yolov2, along with yolo9000 for detection over a huge number of classes — the iteration speed of this algorithm leaves one baffled (it really is awesome). ...
Object detection models come in single-stage and two-stage variants. Faster Rcnn is the representative of the two-stage kind; among single-stage models, besides SSD we also have another mainstream model, YoLo. Introduction Yolo turns the object detection problem into a regression problem over bounding boxes and class probabilities. It uses a single convolutional network to handle the prediction of multiple bounding boxes together with the class probabilities of those boxes, so the model runs very fast, though there is some loss in accuracy, since the candidate box generation process of something like Faster RCnn is gone. Secondly, the Yolo model has a lower background false positive rate and good generalization properties; the authors even ran object detection on abstract paintings, and the result was not bad :) ...