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.