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The price is steep because charity. I used output from Caffe[1] which is open source software. You can get a similar output yourself, just by running their Pyth

The price is steep because charity. I used output from Caffe[1] which is open source software. You can get a similar output yourself, just by running their Python notebook[2]. The festival website is linked within the post[3]. I am using Tumblr to see if it's a viable medium for mini fundraisers. Thanks for the feedback though, I will set up a landing page instead. [1]: http://caffe.berkeleyvision.org/ http://caffe.berkeleyvision.org/ [2]: http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/ex... [3]: http://kudplac.si/portfolio/tknp/ http://kudplac.si/portfolio/tknp/

therobot24 11y ago
Looks like the filters from the notebook are a re-organized view of figure 3 from http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf (as the caffe page cites at the top). I'm not a lawyer, so i don't really know if making a 'new' image using the components of the copyrighted image is derivative or not. > Thanks for the feedback though, I will set up a landing page instead. Please do, if this is really legit, it's in your best interest to set it up in the most professional way possible. It'd probably be better to get the organizers of the festival to put the content of the post in one of their pages and you link there.
- some1else 11y ago
  The first layer only contains basic features, which are shared among different sets of images[1]. It seems that the ImageNet photos themselves are indeed very protected[2], so I should probably use another dataset as input. Plugging in my iPhone camera roll should result in very similar filters. It will be very interesting to see how much they differ. Update: The author of Caffe just got back to me with word that the filters are in public domain, therefore safe to use as design elements. [1]: http://i.stack.imgur.com/Hl2H6.png http://i.stack.imgur.com/Hl2H6.png [2]: http://www.princeton.edu/main/administration/legal_compliance/copyright/ http://www.princeton.edu/main/administration/legal_complianc...

therobot24 11y ago

Looks like the filters from the notebook are a re-organized view of figure 3 from http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf (as the caffe page cites at the top). I'm not a lawyer, so i don't really know if making a 'new' image using the components of the copyrighted image is derivative or not. > Thanks for the feedback though, I will set up a landing page instead. Please do, if this is really legit, it's in your best interest to set it up in the most professional way possible. It'd probably be better to get the organizers of the festival to put the content of the post in one of their pages and you link there.

some1else 11y ago
The first layer only contains basic features, which are shared among different sets of images[1]. It seems that the ImageNet photos themselves are indeed very protected[2], so I should probably use another dataset as input. Plugging in my iPhone camera roll should result in very similar filters. It will be very interesting to see how much they differ. Update: The author of Caffe just got back to me with word that the filters are in public domain, therefore safe to use as design elements. [1]: http://i.stack.imgur.com/Hl2H6.png http://i.stack.imgur.com/Hl2H6.png [2]: http://www.princeton.edu/main/administration/legal_compliance/copyright/ http://www.princeton.edu/main/administration/legal_complianc...