I have recently started working on the Object detection using deep learning.
I have understood the basic steps of object detection are:
Input Image -> Object proposals -> Feature Extraction -> Training.
I have successfully implemented my own code up to the Object Proposal extraction. Now I want to use deep learning here from that point. For the input to deep learning I have fixed number of object proposals (i.e. 800 proposals from one image) and I need a labeled data.
I have a one class dataset. 1) Aircraft,
Images number 1-90 are aircraft.
and the above number of images have 800 aircraft.
Q: Here, I want to ask that how can I make the labels for the dataset with this configuration of class? According to the tutorials and the Matlab implemented examples I have seen, they used the images and the training labels as the input to the CNN. As I have training images I want to know how can i make training labels.
The input to the CNN network will be proposals (just a cropped regions) around 500 proposals. I want the output is to be if any of the proposals is matched as aircraft then it will appear a bounding box on the big image that this is a aircraft.