I have just managed to train a model using tensorflow to identify the digits from SVHN dataset using regression head. And I want to know the location of each classified digit. Here is a similar question. I have been googling around the terms RCNN (faster RCNN), regression head and classification head, and read the source code from some github projects solving the localization problem using different solutions, but it feels like I am hitting the wall in each of the solution
Use regression head to train on the bounding box. I face two difficulties here. First, to generate bounding box for digits as training data. Second, I don't know if it is possible combining classification + localization into one training label (vector with 5 elements), Here is the related project that I found.
RCNN is like a complete pipeline of many different things,and I am not ready for that. There is this method shown in leonardoaraujosantos. Step1. find the activation map that yields highest probability for classification in the fully connected layer Step2. resize the selected activation map to the size of original image size. The position with dot product value over certain threshold is the location, mark it. Found this project and wonder if it is the way to go, it is a bit difficult for me to understand the math though