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Looks like BVLC's Caffe and R-CNN are getting a lot of mindshare. Also interesting to see that the creator of Caffe, Yangqing Jia, was on team GoogLeNet and did
by cr4zy 12y ago
Looks like BVLC's Caffe and R-CNN are getting a lot of mindshare. Also interesting to see that the creator of Caffe, Yangqing Jia, was on team GoogLeNet and didn't appear to have used either.
Edit: Last years results with no Caffe (just Decaf, its predecessor) http://image-net.org/challenges/LSVRC/2013/results http://image-net.org/challenges/LSVRC/2013/results
- dave_sullivan 12y agoCuda convnet 2 was recently released, it's faster than caffe it seems. Caffe is very cool though, I've been playing with it a bit. https://code.google.com/p/cuda-convnet2/ https://code.google.com/p/cuda-convnet2/
- cr4zy 12y agoHey Dave! (Craig here) nice, achieved 6x speed up on an 8 gpu box (http://arxiv.org/pdf/1404.5997v2.pdf http://arxiv.org/pdf/1404.5997v2.pdf). Thanks for the pointer and hope to see you soon.
- dave_sullivan 12y agoHey Craig, nice to hear from you, I hope things are going well. That is a nice bump! The data and model parallelism they added is very cool. We'll be hosting a meetup soon, it's been a while, hope to see you there.
- jiayq 12y agoHi cr4zy! Thanks for mentioning me in the post. At google we have our own large-scale deep learning framework (the Google Brain) that is well constructed and fits the google infrastructure well, so it is understandable that we did not use Caffe at google for the challenge. Mathematically all libraries are alike though, so one is more or less free to choose whatever tool that comes most handy. (hint: you probably want to try out Caffe, or join Google :)) On the detection front we actually adopted the idea of R-CNN quite a lot, so I guess in the context of research, we are standing on the shoulder of existing great research results! Please stay tuned for our upcoming blog post about GoogLeNet and a more detailed introduction of the method at ECCV.