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Refactoring all the code to simplify things #10

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@fmassa fmassa commented Oct 1, 2015

This PR substitutes #5.

  • Unified Fast-RCNN with the other frameworks (RCNN and SPP).
  • Tested Fast-RCNN and RCNN, both works as in the original implementations. Should test SPP again to verify that everything works fine
  • Adds a simple visualization function to plot the detections on top of the image

GPU single element access is much slower
Trying to mimic exactly Caffe's SGD
Cleaned a bit the Tester. Need to add argcheck to functions (but first need to figure out how to pass several parameters without it crashing, maybe using ordered arguments)
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fmassa commented Apr 9, 2016

Just a heads up on this branch.
I did several internal improvements/extensions to this branch over the past few weeks that I'll push here. I'll merge this in in about 1-2 weeks.

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Hi,

What's the status of this PR? Was there a problem that you didn't merge in the main code? Are you getting correct results?
Also which script should one use to train a Fast-RCNN model based on a custom architecture?

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fmassa commented Apr 27, 2018

Hey @erfannoury ,
Note that this is a lua-torch implementation, and has nothing to do with my re-implementation of Faster R-CNN in PyTorch (which is not public yet).
Do you actually want to use lua-torch (which is not maintained anymore) or pytorch?

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Hi @fmassa,

Yes, I understand. I'm looking for a working implementation of Fast R-CNN in either Torch or PyTorch, but unfortunately there aren't any other working implementations out there except Girshick's implementation in pycaffe.

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fmassa commented Apr 27, 2018

As far as I remember, the implementation of Fast R-CNN here was working.

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