Research Article
A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant
Table 1
Overview of the network framework, featuring MobileNetV4 large as the backbone architecture.
| Name | Layer | Feature maps | Parameter |
| Input_1 | InputLayer | 3 | 0 | conv1 | Conv2D | 32 | 864 | conv1_bn | BatchNormalization | 32 | 128 | conv1_relu | ReLU | 32 | 0 | conv_dw_1 | DepthwiseConv2D | 32 | 288 | conv_dw_1_bn | BatchNormalization | 32 | 128 | conv_dw_1_relu | ReLU | 32 | 0 | conv_pw_1 | Conv2D | 64 | 2048 | conv_pw_1_bn | BatchNormalization | 64 | 256 | conv_pw_1_relu | ReLU | 64 | 0 | conv_pad_2 | ZeroPadding2D | 64 | 0 | conv_dw_2_bn | BatchNormalization | 64 | 256 | conv_dw_2_relu | ReLU | 64 | 0 | conv_pw_2 | Conv2D | 128 | 8192 | conv_pw_2_bn | BatchNormalization | 128 | 512 | conv_pw_2_relu | (ReLU) | 128 | 0 | conv_dw_3 | DepthwiseConv2D | 128 | 1152 | conv_dw_3_bn | BatchNormalization | 128 | 512 | conv_dw_3_relu | ReLU | 128 | 0 | conv_pw_7 | Conv2D | 128 | 11384 | conv_pw_7_bn | BatchNormalization | 128 | 512 | conv_dw_7 | DepthwiseConv2D | 1024 | 9216 | conv_dw_7_bn | BatchNormalization | 1024 | 4096 | conv_dw_7_relu | ReLU | 1024 | 0 | conv_pw_7 | Conv2D | 1024 | 18576 | sequential_1 | Sequential | 128 | 19728 | dense_1 | Dense | 5 | 645 |
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