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How does a bounding box Regressor work?
Bounding-box regression is a popular technique to refine or predict localization boxes in recent object detection approaches. Typically, bounding-box regressors are trained to regress from either region proposals or fixed anchor boxes to nearby bounding boxes of a pre-defined target object classes.
Why do we need bounding box?
Using Bounding Boxes for Object Detection In other words, to detect an object in an image, the computer needs to know what it is and where it is. An annotator will draw bounding boxes around other vehicles and label them. This helps train an algorithm to understand what vehicles look like.
How does the bounding box regression work in Fast R-CNN?
A very clear and in-depth explanation is provided by the slow R-CNN paper by Author (Girshick et. al) on page 12: C. Bounding-box regression and I simply paste here for quick reading: Moreover, the author took inspiration from an earlier paper and talked about the difference in the two techniques is below:
How does the bounding box regressor work in deep learning?
These values are predicted using regression method. Unlike other neural network outputs, these values do not represent the probability of output classes. Rather, they are physical values representing position and size of a bounding box.
How does R-CNN work and how fast does it work?
How Fast R-CNN works is summarized as follows; many steps are same as in R-CNN: First, pre-train a convolutional neural network on image classification tasks. Propose regions by selective search (~2k candidates per image). Alter the pre-trained CNN: Replace the last max pooling layer of the pre-trained CNN with a RoI pooling layer.
What’s the difference between fast RCNN and fast Bbox?
The changes from RCNN is that they’ve got rid of the SVM classifier and used Softmax instead. The loss function used for Bbox is a smooth L1 loss. The result of Fast RCNN is an exponential increase in terms of speed. In terms of accuracy, there’s not much improvement.