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caffe_mobilenetv1.opt.param
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caffe_mobilenetv1.opt.param
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Input data 0 1 data -23330=4,3,224,224,3 0=224 1=224 2=3
Convolution conv1 1 1 data conv1_relu1 -23330=4,3,112,112,32 0=32 1=3 3=2 4=1 5=1 6=864 9=1
ConvolutionDepthWise conv2_1/dw 1 1 conv1_relu1 conv2_1/dw_relu2_1/dw -23330=4,3,112,112,32 0=32 1=3 4=1 5=1 6=288 7=32 9=1
Convolution conv2_1/sep 1 1 conv2_1/dw_relu2_1/dw conv2_1/sep_relu2_1/sep -23330=4,3,112,112,64 0=64 1=1 5=1 6=2048 9=1
ConvolutionDepthWise conv2_2/dw 1 1 conv2_1/sep_relu2_1/sep conv2_2/dw_relu2_2/dw -23330=4,3,56,56,64 0=64 1=3 3=2 4=1 5=1 6=576 7=64 9=1
Convolution conv2_2/sep 1 1 conv2_2/dw_relu2_2/dw conv2_2/sep_relu2_2/sep -23330=4,3,56,56,128 0=128 1=1 5=1 6=8192 9=1
ConvolutionDepthWise conv3_1/dw 1 1 conv2_2/sep_relu2_2/sep conv3_1/dw_relu3_1/dw -23330=4,3,56,56,128 0=128 1=3 4=1 5=1 6=1152 7=128 9=1
Convolution conv3_1/sep 1 1 conv3_1/dw_relu3_1/dw conv3_1/sep_relu3_1/sep -23330=4,3,56,56,128 0=128 1=1 5=1 6=16384 9=1
ConvolutionDepthWise conv3_2/dw 1 1 conv3_1/sep_relu3_1/sep conv3_2/dw_relu3_2/dw -23330=4,3,28,28,128 0=128 1=3 3=2 4=1 5=1 6=1152 7=128 9=1
Convolution conv3_2/sep 1 1 conv3_2/dw_relu3_2/dw conv3_2/sep_relu3_2/sep -23330=4,3,28,28,256 0=256 1=1 5=1 6=32768 9=1
ConvolutionDepthWise conv4_1/dw 1 1 conv3_2/sep_relu3_2/sep conv4_1/dw_relu4_1/dw -23330=4,3,28,28,256 0=256 1=3 4=1 5=1 6=2304 7=256 9=1
Convolution conv4_1/sep 1 1 conv4_1/dw_relu4_1/dw conv4_1/sep_relu4_1/sep -23330=4,3,28,28,256 0=256 1=1 5=1 6=65536 9=1
ConvolutionDepthWise conv4_2/dw 1 1 conv4_1/sep_relu4_1/sep conv4_2/dw_relu4_2/dw -23330=4,3,14,14,256 0=256 1=3 3=2 4=1 5=1 6=2304 7=256 9=1
Convolution conv4_2/sep 1 1 conv4_2/dw_relu4_2/dw conv4_2/sep_relu4_2/sep -23330=4,3,14,14,512 0=512 1=1 5=1 6=131072 9=1
ConvolutionDepthWise conv5_1/dw 1 1 conv4_2/sep_relu4_2/sep conv5_1/dw_relu5_1/dw -23330=4,3,14,14,512 0=512 1=3 4=1 5=1 6=4608 7=512 9=1
Convolution conv5_1/sep 1 1 conv5_1/dw_relu5_1/dw conv5_1/sep_relu5_1/sep -23330=4,3,14,14,512 0=512 1=1 5=1 6=262144 9=1
ConvolutionDepthWise conv5_2/dw 1 1 conv5_1/sep_relu5_1/sep conv5_2/dw_relu5_2/dw -23330=4,3,14,14,512 0=512 1=3 4=1 5=1 6=4608 7=512 9=1
Convolution conv5_2/sep 1 1 conv5_2/dw_relu5_2/dw conv5_2/sep_relu5_2/sep -23330=4,3,14,14,512 0=512 1=1 5=1 6=262144 9=1
ConvolutionDepthWise conv5_3/dw 1 1 conv5_2/sep_relu5_2/sep conv5_3/dw_relu5_3/dw -23330=4,3,14,14,512 0=512 1=3 4=1 5=1 6=4608 7=512 9=1
Convolution conv5_3/sep 1 1 conv5_3/dw_relu5_3/dw conv5_3/sep_relu5_3/sep -23330=4,3,14,14,512 0=512 1=1 5=1 6=262144 9=1
ConvolutionDepthWise conv5_4/dw 1 1 conv5_3/sep_relu5_3/sep conv5_4/dw_relu5_4/dw -23330=4,3,14,14,512 0=512 1=3 4=1 5=1 6=4608 7=512 9=1
Convolution conv5_4/sep 1 1 conv5_4/dw_relu5_4/dw conv5_4/sep_relu5_4/sep -23330=4,3,14,14,512 0=512 1=1 5=1 6=262144 9=1
ConvolutionDepthWise conv5_5/dw 1 1 conv5_4/sep_relu5_4/sep conv5_5/dw_relu5_5/dw -23330=4,3,14,14,512 0=512 1=3 4=1 5=1 6=4608 7=512 9=1
Convolution conv5_5/sep 1 1 conv5_5/dw_relu5_5/dw conv5_5/sep_relu5_5/sep -23330=4,3,14,14,512 0=512 1=1 5=1 6=262144 9=1
ConvolutionDepthWise conv5_6/dw 1 1 conv5_5/sep_relu5_5/sep conv5_6/dw_relu5_6/dw -23330=4,3,7,7,512 0=512 1=3 3=2 4=1 5=1 6=4608 7=512 9=1
Convolution conv5_6/sep 1 1 conv5_6/dw_relu5_6/dw conv5_6/sep_relu5_6/sep -23330=4,3,7,7,1024 0=1024 1=1 5=1 6=524288 9=1
ConvolutionDepthWise conv6/dw 1 1 conv5_6/sep_relu5_6/sep conv6/dw_relu6/dw -23330=4,3,7,7,1024 0=1024 1=3 4=1 5=1 6=9216 7=1024 9=1
Convolution conv6/sep 1 1 conv6/dw_relu6/dw conv6/sep_relu6/sep -23330=4,3,7,7,1024 0=1024 1=1 5=1 6=1048576 9=1
Pooling pool6 1 1 conv6/sep_relu6/sep pool6 -23330=4,1,1024,1,1 0=1 4=1
InnerProduct fc7 1 1 pool6 fc7 -23330=4,1,1000,1,1 0=1000 1=1 2=1024000
Softmax output 1 1 fc7 output -23330=4,1,1000,1,1