3 ms·
Nice one! I don't remember all that much from reading the Mask-RCNN paper last year and have not seen many implementations so it's nice to be presented with thi
by edshiro 8y ago
Nice one! I don't remember all that much from reading the Mask-RCNN paper last year and have not seen many implementations so it's nice to be presented with this Pytorch implementation.
From what I recall about Faster R-CNN, the Regions Of Interest (ROI) are pre-determined via Selective Search, right? So I presume you would need to do the same thing with Mask-RCNN? I think this is the part I am the most confused with since I have never implemented Selective Search myself. Could you point me to introductory material on it?
Lastly, I can see the author of this work has read my blog post on understanding SSD MultiBox - glad it helped in some way :).
- deleted 8y ago[deleted]
- mliker 8y agoRCNN uses selective search to generate the ROIs. What makes Faster RCNN faster is not having to spend time on selective search.
- wannabeOG 8y agoIt uses an RPN to generate the region proposals so it completely does away with the selective search which was the bottleneck for speed in fast RCNN
- HolyMakarony 8y agoAnother popular implementation of MRCNN in Kerala+Tensorflow is found here: https://github.com/matterport/Mask_RCNN https://github.com/matterport/Mask_RCNN. Selective search is implemented in Fast-RCNN. Faster-RCNN improves upon that and uses a Region Proposal Me to propose RoI that may contain objects which speed up training and inference time.