Hey everyone. I have a simple tensorflow model that predicts if image is cat or not cat. I'm using a large image as an input. I wanna detect all cats in this input.

I'm using OpenCV SelectiveSearch Segmentation(opencv.ximgproc.segmentation.createSelectiveSearchSegmentation.switchToSelectiveSearchFast) for finding boxes in input. So lets say opencv gives me 6000 results. Even if I do preprocess and reduce the number to 3000. It will take too long to predict each result one by one with the model. Like 45-90 seconds. I was thinking of making a realtime application :P

TL;DR: OpenCV SelectiveSearch Segmentation gives me too many result. Even if I do preprocess, the number is high.

Is there any way to shorten this number? Or is there any other way to detect boxes without using opencv selective search?