The GoogLeNet architecture relies on a large number of relatively arbitrary hyperparameters, marking the beginning of deliberate network design experimentation at a block level. These hyperparameters include the number of output channels per branch in each Inception block, the number of blocks before each dimensionality reduction step, and the relative partitioning of capacity across channels. Much of this complexity stems from the fact that automated tools for architecture exploration were not yet available. Network design relied on costly manual specification by the experimenter, brute-force search, and genetic algorithms rather than modern automated architecture search methods. Despite being entirely manual, this structured, block-level approach to tuning is why GoogLeNet is arguably considered the first truly modern Convolutional Neural Network (CNN).