Deep Learning

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 :) ...

First of all, I must congratulate the People’s Republic of China on its 70th anniversary. To celebrate the arrival of the National Day holiday, I took the time to go through the SSD object detection model and then struck while the iron was hot to update the blog… The biggest difference between the SSD algorithm and the previous RCNN series algorithms is that it is single-stage, whereas the RCNN series is multi-stage. See the details below. ...

This feeling of posting several articles in a row is very exciting, very refreshing. Without further ado, as the title says, this is a faster and stronger object detection algorithm, and it also comes from Ross Girshick… Honestly, this guy spent several papers in a row criticizing the models he had written before — impressive. Faster Rcnn adopts an efficient algorithm to generate candidate boxes, and also introduces the concept of Anchor. ...

Ha, since I happen to have some free time these days, let me quickly summarize the RCNN series papers I read before; this is the second post in the series. The author Ross Girshick could not stand how slowly the model he had proposed earlier (RCNN) ran, so he proposed an improved model (Fast RCNN). Below is a brief analysis of that model. The slowness of RCNN Compared with image recognition tasks, object detection is considerably harder because of the extra step of localizing the object positions. There are two main problems to solve: one is that the model needs a large number of object candidate boxes; the other is that these candidate boxes only roughly provide the approximate positions of the objects, so the model has to perform a regression operation. $RCNN$ is a multi-stage training model. The $RCNN$ model first fine-tunes a convolutional neural network on the object candidate boxes using a log loss, then passes the resulting feature maps to $SVM$s for object classification; these $SVM$s replace the $softmax$ layer that would otherwise need fine-tuning. In the third training stage the model learns bounding box regression. For training the $SVM$s and the bounding box regression, $RCNN$ writes the features of every object candidate box to disk, which undoubtedly costs an enormous amount of time and space. Secondly, the speed of object recognition is too slow: on a GPU, recognizing an image takes about 47 seconds per image. Another important reason for the slowness of $RCNN$ is that the model does not share the computation of the convolution layers. ...