In real world datasets a single class may only be in 1% of the data to begin with. The vast majority of images on the internet do not contain cats. So making a cat recognizer that is even 10% accurate is a significant challenge, simply because of the low base rate of cats. Imagenet in particular has 1000 equally balanced classes, so each class makes up 0.1% of the data. And 10% accuracy is way better than chance.

Additionally very few machine vision systems are completely hand designed. They all use some form of machine learning and optimization. Just not necessarily NNs.

Classification is only one problem in machine vision. As in, not even 10% of the whole.

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 only one problem in machine vision. As in, not even 10% of the whole.

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 : )

No it's not, it's just the one that neural networks and amateurs on Kaggle are good at.