
Building powerful image classification models using very little data - fchollet
http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
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gajomi
I am just starting to look into these corpuses of pretrained neural networks.
Coes anyone know if there are any image corpora with pretrained wieghts that
would be suitable for analysis of "high-resolution" images derived from LIDAR
data (where in this case high resolution corresponds to a resolution limit of
a few meters). In the not unlikely case that there is nothing so specific, can
anyone comment on the same for optical satellite imagery?

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alexbeloi
With high resolution images you need large amount of layers to for the later
layers to be able to 'see' the whole picture. But such a network would likely
be unreasonably large for such large inputs, you'd have to look for
distributed model solutions.

So it kind of depends on what you are trying to extract from such large
images. If it's macroscopic features then you should just downsample your
image to something reasonable and feed it into some pretrained network like
Alexnet or VGGnet. If you want microscopic feature detection, but don't care
about macro features, then you should use a shallower convnet.

If you insist on having both but the resulting network can't be stored in
memory and distributed system is not an option, you might want to look into
using recurrent networks with visual attention. These sort of look at
manageable chunks of the image and decide where else to look based on what
they've seen so far.

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jc4p
I love these blog posts. The timing is perfect for me too, I committed some
code a week ago that said "I'm doing this manually because I can't figure out
how to use `fit_generator`", thanks for the write-up Francois!

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holografix
Great post, fascinating and practical. Which is not something that happens a
lot.

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TheAlchemist
Another great post. Thanks for awesome learning material !

