SOTA is 'state of the art'. Image recognition is a task classically appraised by calculating the accuracy on the ImageNet dataset, which requires a system to classify images each as one of 1,000 pre-determined classes.

so how many images does the current SOTA take to train a classifier? Trying to gauge how much of an improvement Deepmind has made here.

The results that should be used to compare their results to others involves training on just under 1.3 million images across the 1,000 classes.

Their best results involve 'pretraining' on a dataset of 300 million examples, before 'tuning' it on the actual ImageNet training dataset as above.

State of The Art == SOTA

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