layer [b, x, y, c] b: Batch size x: Width of image y: Height of image c: Number of Channels Optimizing pre-processing in order to set the number of image channels to 4 or more Make the network structure of appropriate size (It is not good to be too large) Taking Global Average Pooling layer instead of Fully Connected layer Terms ・Start learning with random initial values. Transfer learning from ImageNet is not performed. ・Set Learning Rate in Stepwise. Optimizer does not affect accuracy very much. Batch Normalize layer is more important than Dropout layer. Incorporate the Residual layer 11,283,478 26,073,878 23,593,174 50,474,518 134,360,598 55,784,214 139,670,294 21,818,390 54,313,942 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000 140,000,000 160,000,000 Parameter size Fall into qualitative classification issue Good balance between accuracy and computational Deep learning engine for judgement process of printing image data