ritika-singh asked . 2021-05-31

Understanding the positive and negative overlap range

% Adjust NegativeOverlapRange and PositiveOverlapRange to ensure
% that training samples tightly overlap with ground truth

'PositiveOverlapRange' A two-element vector that specifies a range of
%                        bounding box overlap ratios between 0 and 1.
%                        Region proposals that overlap with ground truth
%                        bounding boxes within the specified range are used
%                        as positive training samples.
%
                        Default: [0.5 1]
%
'NegativeOverlapRange' A two-element vector that specifies a range of
%                        bounding box overlap ratios between 0 and 1.
%                        Region proposals that overlap with ground truth
%                        bounding boxes within the specified range are used
%                        as negative training samples.
%
                        Default: [0.1 0.5]

1. what is the threshold actually controlling/ doing for the positive and negative overlap range
2. Is there a link to understand this on youtube etc to get a simple break down of what this does or is? I have been trying this but maybe my terminology is incrorrect!!
3. I specified only the negative range, what happends when I don't specify the positive range?
4. what happends when i specify both positive and negative ranges?
5. what am I really telling the system to do actually?!!!?!?!!!?!
6. if I modify the Positive Overlap Range, What am I Actually Doing, Same for the Negative Over Lap Range?

rcnn = trainRCNNObjectDetector(BCombineData, Tlayers, options, 'NegativeOverlapRange', [0 0.3]);
rcnn = trainRCNNObjectDetector(BCombineData, Tlayers, options, 'PositiveOverlapRange', [0.5 1] ,'NegativeOverlapRange', [0 0.3]);
rcnn.RegionProposalFcn;
network = rcnn.Network;
layers = network.Layers;

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