al., would also be willing to reduce the dimensionality and recompress, further reducing storage overhead. I would also assume that many algorithms, like the type that attack CIFAR10 et. ImageNet (~1.2 TB) only took me 45 days on a residential (<20 MB connection), and I wold assume that this dataset would be downloaded by entities with much higher download b/w.

Heck, you could probably make use of grayscale only to reduce the size by a factor of 3 - ~ 17 TB is feasible (though still pretyt insane) to store locally. This also assumes that you can't reduce/compress the info any further than what flickr provides and that you require access to the entire dataset - if any of the images are 1024x1024 or larger most feature extractions do not need that kind of fidelity.

Most people who will work on this could reasonably store a large chunk locally, if not all (~10 TB). I also don't know about your other conclusion, there is no reason you couldn't download this dataset given enough time/bandwidth/storage to process locally. This looks like buzzword soup to me (SACC_Pitch, Tonality? What the heck?!? These seem like audio features - where are the formulas!). Why the heck would you do MFCC on images? Mel filters try to replicate the perception of human ears on audio.

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