>Traversing the trade-offs between state-of-the-art performance and the ability to serve our user base at scale, we implemented (in its latest iteration) an Eff

>Traversing the trade-offs between state-of-the-art performance and the ability to serve our user base at scale, we implemented (in its latest iteration) an EfficientNetv2-based binary classifier: a convolutional network that has faster training speed and overall better parameters efficiency. It uses a combination of better designed architecture and scaling, with layers like MBConv (that utilizes 1×1 convolutions to wide up the space and depth-wise convolutions for reducing the number of overall parameters) and FusedMBConv (that merges some steps of the vanilla MBConv above for faster execution), to jointly optimize training speed and parameter efficiency. The model has been trained leveraging our GPU powered data centers in a continuous exercise of dataset, network and hyperparameters (the settings used to speed up or improve the training performance) optimization. This is a lot of words to say, "we used an off-the-shelf architecture". Like it's literally just Google's public implementation copied over.