"A tenth of the accuracy" of a 100% accurate model is 10%. So you're basically claiming that nobody had achieved anything with machine vision before NNs (you probably mean AlexNet in particular), which is patently absurd and completely unhistorical.
Why is it that this subject attracts the attention of so many people who know so little of what they're talking about?
Additionally very few machine vision systems are completely hand designed. They all use some form of machine learning and optimization. Just not necessarily NNs.
Edit: Btw I just had a look to confirm and what you're saying about "1000 equally balanced classes" is inexact, to put it midly. ImageNet has 100,000 SynSets (as in WordNet) and aims to have about 1000 images illustrating each. Individual images may illustrate several synsets- you don't get one image per class, say.
Also: AlexNet was not a cat recogniser and having unbalanced classes is a problem for machine learning, but not a dealbreaker, per se.
Classification is extremely representative of the challenge of machine vision. Good classifiers can often be adapted to do localization and other tasks, e.g. by scanning the image to see what parts contain an object.
The Imagenet Large Scale Visual Recognition Challenge has 1000 classes which you can browse here: http://image-net.org/challenges/LSVRC/2015/browse-synsets I forgot that Imagenet itself has more classes than that. But it's best known for this specific competition of 1000 classes, and everyone uses that as a benchmark.
>freaking AlexNet was not a fraking cat recogniser.
I never said it was... It's actually a dog recognizer mostly : )