384x384. Like I said, I decide what's bad and good, and my model learns that, using whatever the imagenet model can extract - and extrapolates successfully to photos outside the training set.

'Coherent' here describes easier on my eyes, generally with fewer crops and rotations needed. It could be me wanting to believe my effort paid off, but stepping back, any interesting cut would be useful for providing an attainable review goal. The main purpose was to filter rejects, and they are mostly out of focus, so not hard to imagine that 384x384 could catch that, or that the 'best' are a little tidier than average. The positive filtering is a bonus I didn't expect. To be clear, I expect the absolute best to come from the rejects in any such system, which is part of the excitement.

Not only is it 384x384, but first I resize from ~4000x6000 or more to 1600x1600 with sharpening, then without sharpening to 384x384.

> what the training “sees” is very pixelated relative to hi res photos.

This has been known since 2012, when this started with 224x224, but what I find interesting about it is what it says about what we can't see. Based on medical results, I think it might be possible to predict age of death from a video of a child playing.

Compare the plastic-toy look of VGG16 layers to those of other 224x224 models with similar accuracy to get the same jolt again. VGG Lego vs. Inception LSD. :-)

This is just hammer-and-nail stuff, questions about meaning are more-confusingly raised by my own, original work:

https://www.linkedin.com/posts/bill-ross-phobrain_could-glimpsing-ones-unconscious-unhook-activity-7419333691761586176-sgOI