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I want to build my own Faster-RCNN model from scratch for multi-object detection from image data.
Can somebody please refer me good sources to step by step approach to implement faster-RCNN?
Which one will be good YOLO or faster-RCNN in terms of accuracy and execution time?
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Faster R-CNN is a single-stage model that is trained end-to-end. It uses a novel region proposal network (RPN) for generating region proposals, which save time compared to traditional algorithms like Selective Search. It uses the ROI Pooling layer to extract a fixed-length feature vector from each region proposal.
In practical it runs a lot faster than faster rcnn due it's simpler architecture. Unlike faster RCNN, it's trained to do classification and bounding box regression at the same time.
If you are in computer vision go through https://www.pyimagesearch.com/ guy named Adrian has great work over there
Instead of starting from scratch use pre-build model as base model afterward you can go for implementation of your own intermediate layer
The architecture of faster RCNN
https://medium.com/@smallfishbigsea/faster-r-cnn-explained-864d4fb7e3f8
Actual implementation source -1
Actual implementation source-2
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