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Monday, February 23, 2015

Working the Pete Warden Tutorial

Whereas last century's nets neural were shallow, and often just 3-layered, the current "deep" architectures are hierarchical. In the case of machine vision and image recognition much of the feature extraction is done by 2-D convolution modules.

Pete Warden has created a Vagrant machine which contains a canned version of the Caffe deep learning framework started by Yangqing Jia and the Berkeley Vision and Learning team, bundled  with some example data and pretrained nets; Pete's tutorial is really easy to work through.


 I got an image of a classic french car from wikimedia.





 I then copied the image file to  /vagrant/deux-chevaux.jpg in the VM, and submitted it to a network that’s based on the architecture used by Krizhevsky et al to win the Imagenet 2012 contest:


vagrant@vagrant-ubuntu-trusty-64:~/caffe$ python python/classify.py --print_results /vagrant/deux-chevaux.jpg foo
Classifying 1 inputs.
Done in 1.30 s.
[('pickup', '0.27585'), ('convertible', '0.24703'), ('beach wagon', '0.11597'), ('grille', '0.07221'), ('minivan', '0.06870')]


The net has clearly generalized the concept of a car, and in some sense "recognizes" this very unusual model.

 Now, that was a satisfying first experiment! Thank you Pete!


Edmund 




2 comments:

  1. Is there another link to download the vagrant file for the tutorial? The link in the article is dead.

    ReplyDelete
    Replies
    1. You could ask Pete. I will try and do my own tutorial with Keras.

      Delete

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