My understanding of how non-max suppression works is that it suppresses all overlapping boxes that have a Jaccard overlap smaller than a threshold (e.g. 0.5). The boxes to be considered are on a confident score (may be 0.2 or something). So, if there are boxes that have a score over 0.2 (e.g. the score is 0.3 and overlap is 0.4) the boxes won't be suppressed.
In this way, one object will be predicted by many boxes, one high score box, and many low confident score boxes, but I found that the model predicts only one box for one object. Can someone enlighten me?
I currently viewing the ssd from https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection
Here is the code.
#Finding Jaccap Overlap and sorting scotes class_scores, sort_ind = class_scores.sort(dim=0, descending=True) class_decoded_locs = class_decoded_locs[sort_ind] # (n_min_score, 4) overlap = find_jaccard_overlap(class_decoded_locs, class_decoded_locs) suppress = torch.zeros((n_above_min_score), dtype=torch.uint8).to(device) for box in range(class_decoded_locs.size(0)): # If this box is already marked for suppression if suppress[box] == 1: continue suppress = torch.max(suppress, overlap[box] > max_overlap) suppress[box] = 0