[liuliu]

Hey, Donahue, I actually referenced Caffe extensively in detailed documentation: http://libccv.org/doc/doc-convnet/

This is a preliminary implementation, but is a complete one includes both training and testing code. The big difference is that ccv is a computer vision library in general and Caffe is a artificial neural network library. This does mean quite a few different ways of approaching things, for example, ccv's implementation does allow you to specify network topology, but doesn't have a implementation of local non-weight-sharing layer (because CIFAR-10 and ImageNet doesn't need such type of layer).

You can also chop off the last full connect layer and train a SVM on top of it with ccv, I actually plan to do exactly what you guys did with that and train on VOC 2012 dataset.

All in all, ccv 0.6 is a preliminary implementation of convnet, but it is important for a library claims to be "modern" to contain the said implementation. And providing the pre-trained data model with a liberal license (so that you can fine-tune your classification problem on top of the pre-trained data model) is also aligned with ccv's goal.

[jdonahue]

I hadn't seen the detailed documentation - thanks so much for the acknowledgments there!

And thanks for correcting me about CCV's support for custom architectures and training -- I'd just assumed that it wasn't supported since it wasn't mentioned in the post, but I guess this was more of a marketing decision as most users are probably just interested in feature extraction/classification from the pretrained net. :) I would argue that GPU support is pretty necessary for training modern network architectures a la Krizhevsky to be remotely practical, though.

I apologize if I came off as overly competitive or derisive, this is obviously very nice work and it seems like an attractive option for many users. Always happy to see deep learning made more accessible and open!