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hi there today were looking at high performance large-scale image recognition without normalization by andrew brock silham day samuel l smith and karen simonian of deepmind this is otherwise known as nf nets normalizer free networks so the point of this paper is to build networks in this case specifically convolutional residual style networks that have no batch normalization built in and well get to why in you know during looking at this paper but without the batch normalization usually these networks are performing not as well or cannot scale to larger batch sizes however this paper right here builds networks that can scale to large batch sizes and are more efficient than previous state-of-the-art methods so if you compare them to something like an efficient net and i called it i called it you shouldnt call your model efficientnet because a more efficient model is going to come around so nfnet are now officially efficient or net okay as you can see right here to docHub the same acc
