Table 2.

The details of hyper-parameters applied for various CNN models

CNN model	Batch-size	Learning-rate
AlexNet	48,24,16	8e$-$7,1e$-$8,1e$-$7,6e$-$7,1e$-$5,1e$-$6,1e$-$10
SqueezeNet	48,24,16	1e$-$8, 1e$-$7, 1e$-$6, 9e$-$5
GoogleNet	48,24,16	1e$-$8, 6e$-$7, 1e$-$7, 9e$-$6, 1e$-$6, 1e$-$5
MobileNetV2	24,16,8	9e$-$6, 3e$-$6,5e$-$6,1e$-$7,8e$-$5,1e$-$6
InceptionV3	48,24,16	8e$-$5,1e$-$6,9e$-$6,1e$-$5,1e$-$7
Our ensemble model (MobileNetV2 +InceptionV3 )	24,16,8	4e-7, 1e-7, 1e-5, 1e-6