I want to play around with some Deep Learning algorithms.
After some readings, I decided to do some experiments with Caffe.
Caffe is a deep learning framework made with expression, speed, and modularity in mind.
It is fairly easy to start using some Deep Neural Nets with Caffe.
There are plenty of pre-trained model that you can use right away to do some experiments.
In this post I want to share my experience with Caffe under Ubuntu 14.04 LTS.
During the installation I mainly followed this guide.
Finally I had to install some packages required by the python module…
… and I added the caffe’s python folder in the environment variable PYTHONPATH.
Now we are ready to go… let’s do some test!
I started usign this tutorial.
The goal is to have a feature extractor that is working using the pre-trained model from Krizhevsky et al. that is working on the ImageNet dataset.
We need to download the model and the labels first.
I downloaded few pictures and added to the images folder.
And with the following script I run the feature-extraction/classification.
The script is really simple, however I’m planning to write ASAP a script that can deal with tons of images.
And the results is the following, quite impressive ;)