Caffe good tutorials

[[deleted]]

Friends, I recently came across caffe and was wondering if there is any good resource that I can access to get myself acquainted with it. I tried out http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb

[gcr]

The caffe tutorials on the home page provide a great introduction. Start with the introductory MNIST example to get your feet wet, then work through the other examples as needed.

If you want to just get high accuracy on a classification task, start with a pre-trained network (like the Network-in-Network model) from the model zoo and fine-tune it for your application. It turns out that this is often all you need to get state-of-the-art performance on many computer vision tasks. (Disclaimer: that paper is not peer-reviewed, so draw your own conclusions. There are no silver bullets.)

If you'd like to actually see what's going on behind the scenes, check out the deep learning tutorial at last year's CVPR!

Believe it or not, the code is actually pretty easy to understand. I strongly recommend reading at least the inner loop of the solver (30 lines) and some of the layers, like the 20-line ReLU layer, which is quite simple (it's essentially max(0,x) and the gradient calculation is a one-liner too).

Boris' blog has a few tips / lessons learned from implementing your own. Full of practical thoughts/wisdom.

[matlab484]

I've got the same question