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Table 3 The structure of VGG16-Net

From: Precipitation cloud identification based on faster-RCNN for Doppler weather radar

Layer Kernal Input Output
Conv1(Conv2D) (1,3,3,64) (1,400, 400, 5/7) (1,400,400,64)
Conv2(Conv2D) (1,3,3,64) (1,400, 400, 64) (1,400,400,64)
maxPooling1 (2,2) (1,400, 400, 64) (1,200, 200, 64)
Conv3(Conv2D) (1,3,3,128) (1,200, 200, 64) (1,200, 200,128)
Conv4(Conv2D) (1,3,3,128) (1,200,200,128) (1,200, 200,128)
maxPooling2 (2,2) (1,200,200,128) (1,100,100,128)
Conv5(Conv2D) (1,3,3,256) (1,100,100, 128) (1,100,100,256)
Conv6(Conv2D) (1,3,3,256) (1,100,100, 256) (1,100,100,256)
Conv7(Conv2D) (1,3,3,256) (1,100,100, 256) (1,100,100,256)
maxPooling3 (2,2) (1,100,100, 256) (1,50, 50, 256)
Conv8(Conv2D) (1,3,3,512) (1,50, 50, 256) (1,50, 50,512)
Conv9(Conv2D) (1,3,3,512) (1,50, 50,512) (1,50, 50,512)
maxPooling4 (2,2) (1,50, 50,512) (1,25,25,512)
Conv11(Conv2D) (1,3,3,512) (1,25,25,512) (1,25,25,512)
Conv12(Conv2D) (1,3,3,512) (1,25,25,512) (1,25,25,512)
Conv13(Conv2D) (1,3,3,512) (1,25,25,512) (1,25,25,512)
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