Table 9 FScore analysis of DAC-GAN.

CNN Models	Accuracy with DAC-GAN Synthetic Nuts Images
CNN Models	Applying Raw Nuts images	Applying Sobel Nuts images	Applying Canny Edge Nuts images	Applying Kernel Isolated Nuts images	Applying Corner Key point nuts images
DenseNet121	68.18	69.91	71.83	75.67	77.75
VGG19	68.35	71.83	73.05	76.88	78.93
Inception	69.41	72.81	74.34	78.14	79.88
XCeption	70.66	73.92	75.12	79.16	80.87
MobileNet	71.89	75.22	76.31	79.93	82.12
ResNet-50	73.08	76.94	77.10	81.08	83.08
EfficientNet-B0	74.86	78.57	80.27	83.66	85.59
EfficientNet-B4	76.09	80.16	81.99	85.21	87.53
ConvNeXt	77.32	81.39	83.21	86.28	88.83
ViT CNN	78.61	82.35	84.04	87.14	89.39
SwinT	79.81	83.43	85.16	88.02	90.08
WGAN	80.81	84.61	87.21	86.32	94.99
CGAN	83.66	85.79	86.62	88.34	94.21
DAC-GAN	80.32	83.71	86.80	88.89	99.84

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