Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

Table 9

Specificity of batch sizes 32, 64, and 128 for classifiers and optimizers.

Optimization
	Model
	Batch size 32				Batch size 64				Batch size 128
	IV3	MN	XN	RN	IV3	MN	XN	RN	IV3	MN	XN	RN
Adam	92.34	90.87	91.43	86.23	95.65	92.36	88.54	92.38	95.32	90.74	89.45	89.29
SGD	89.64	90.34	94.23	92.37	94.47	94.95	90.67	92.93	93.67	90.34	86.67	91.1
RMSProp	93.82	95.81	89.67	92.93	95.84	95.98	92.23	90.28	94.97	96.28	88.24	94.25
Nadam	90.12	94.45	87.34	91.1	95.43	93.93	93.46	89.53	94.34	89.86	89.75	92.36
Adadelta	52.32	89.32	83.95	89.02	65.27	89.72	89.87	93.17	56.93	85.95	86.83	93.75
Adagrad	77.98	94.87	91.37	93.47	87.94	81.23	89.12	93.83	86.26	86.36	90.46	92.64