I need to build some CNN's using very large numbers of photos, to classify which photos contain certain features.
I've run into a number of road blocks.
As much as I love both Linux and virtualization, I've found that Linux-only solutions like Berkeley's Caffe and NYU's pre-trained OverFeat don't work for large scale data aren't viable because virtualization has a lot of problems in an HPC and big data scenario, especially when the tools depend heavily on specific hardware and drivers to be effective (even if you have the hardware and drivers, getting it to work on a VM is something different).
I've found a number of solutions in R, Python, and ImageJ which work well on the small scale or with specialized types of photos like medical images, satellite or microscopic images, but not for real world photos with lots of "noise" and features which can vary a good bit between photos.
Is there a Windows-friendly or platform-agnostic scalable solution that's free and minimally complicated?