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khknoppRam81
khknopp
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Ram81
committedDec 9, 2018
Add Colornet to Fabrik Zoo (Cloud-CV#494)
* Add files via upload * Add files via upload * Add files via upload * Update tested_models.md * Update tested_models.md * Update tested_models.md
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‎example/caffe/colornet.prototxt

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name: "Colornet"
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layer {
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name: "img_lab"
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top: "img_lab" # Lab color space
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type: "Input"
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input_param { shape { dim: 1 dim: 3 dim: 227 dim: 227 } }
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}
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# **************************
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# ***** PROCESS COLORS *****
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# **************************
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# layer { # Convert to lab
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# name: "img_lab"
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# type: "ColorConv"
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# bottom: "data"
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# top: "img_lab"
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# propagate_down: false
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# color_conv_param {
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# input: 0 # BGR
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# output: 3 # Lab
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# }
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# }
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layer {
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name: "img_slice"
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type: "Slice"
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bottom: "img_lab"
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top: "img_l" # [0,100]
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top: "data_ab" # [-110,110]
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propagate_down: false
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slice_param {
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axis: 1
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slice_point: 1
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}
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}
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layer {
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name: "silence_ab"
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type: "Silence"
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bottom: "data_ab"
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}
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layer { # 0-center lightness channel
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name: "data_l"
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type: "Convolution"
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bottom: "img_l"
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top: "data_l" # scaled and centered lightness value
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propagate_down: false
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param {lr_mult: 0 decay_mult: 0}
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param {lr_mult: 0 decay_mult: 0}
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convolution_param {
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kernel_size: 1
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num_output: 1
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}
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}
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data_l"
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top: "conv1"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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convolution_param {
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num_output: 96
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kernel_size: 11
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stride: 4
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}
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layer {
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name: "pool1"
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type: "Pooling"
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bottom: "conv1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv2"
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type: "Convolution"
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bottom: "pool1"
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top: "conv2"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu2"
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type: "ReLU"
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bottom: "conv2"
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top: "conv2"
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}
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layer {
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name: "pool2"
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type: "Pooling"
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bottom: "conv2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv3"
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type: "Convolution"
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bottom: "pool2"
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top: "conv3"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu3"
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type: "ReLU"
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bottom: "conv3"
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top: "conv3"
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}
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layer {
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name: "conv4"
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type: "Convolution"
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bottom: "conv3"
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top: "conv4"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu4"
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type: "ReLU"
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bottom: "conv4"
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top: "conv4"
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}
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layer {
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name: "conv5"
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type: "Convolution"
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bottom: "conv4"
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top: "conv5"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 1
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}
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}
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}
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layer {
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name: "relu5"
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type: "ReLU"
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bottom: "conv5"
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top: "conv5"
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}
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layer {
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name: "pool5"
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type: "Pooling"
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bottom: "conv5"
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top: "pool5"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "fc6"
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type: "InnerProduct"
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bottom: "pool5"
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top: "fc6"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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inner_product_param {
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num_output: 4096
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}
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}
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layer {
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name: "relu6"
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type: "ReLU"
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bottom: "fc6"
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top: "fc6"
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}
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layer {
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name: "drop6"
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type: "Dropout"
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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dropout_ratio: 0.5
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}
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}
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layer {
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name: "fc7"
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type: "InnerProduct"
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bottom: "fc6"
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top: "fc7"
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param { lr_mult: 1 decay_mult: 1 }
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param { lr_mult: 2 decay_mult: 0 }
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inner_product_param {
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num_output: 4096
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}
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}
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layer {
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name: "relu7"
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type: "ReLU"
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bottom: "fc7"
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top: "fc7"
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}
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layer {
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name: "drop7"
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type: "Dropout"
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bottom: "fc7"
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top: "fc7"
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dropout_param {
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dropout_ratio: 0.5
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}
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}

‎ide/static/img/zoo/colornet.png

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‎ide/static/js/modelZoo.js

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this.refs.caption.className = " ";
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this.refs.segmentation.className = " ";
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this.refs.vqa.className = " ";
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}
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}
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else if (id == "recognition")
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{
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this.refs.recognition.className = " ";
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}
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}
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else if (id == "detection")
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{
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{
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this.refs.detection.className = " ";
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}
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}
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else if (id == "retrieval")
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{
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this.refs.retrieval.className = " ";
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}
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}
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else if (id == "seq2seq")
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{
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this.refs.seq2seq.className = " ";
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}
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}
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else if (id == "caption")
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{
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this.refs.caption.className = " ";
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}
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}
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else if (id == "segmentation")
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{
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this.refs.segmentation.className = " ";
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}
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}
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else if (id == "vqa")
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{
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this.refs.vqa.className = " ";
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}
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}
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}
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componentDidMount() {
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let filter = (pattern) => {
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let layerCompability = (searchQuery, layerName) => {
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}
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return {
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match: seq,
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full_match: full_match
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full_match: full_match
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};
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}
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for (let elem of $('.col-sm-6')) {
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}
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}
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$('#model-search-input').keyup((e) => {
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filter(e.target.value);
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filter(e.target.value);
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});
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}
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render() {
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return (
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<div className="sidebar-content">
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<div id="wrapper" className="toggle" ref="wrapper1">
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</div>
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<div ref="retrieval">
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<ModelElement importNet={this.props.importNet} framework="caffe" id="siamese_mnist" displayName="MNIST Siamese"> </ModelElement>
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<ModelElement importNet={this.props.importNet} framework="caffe" id="colornet" displayName="Colornet"> </ModelElement>
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</div>
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<div ref="seq2seq">
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<ModelElement importNet={this.props.importNet} framework="keras" id="textGeneration" displayName="Text Generation"> </ModelElement>

‎tutorials/tested_models.md

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### Retrieval
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* MNIST Siamese [\[Source\]](https://github.com/BVLC/caffe/tree/master/examples/siamese)[\[Visualise\]](http://fabrik.cloudcv.org/caffe/load?id=20171208113503xgnfd)
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* Colornet [\[Source\]](https://github.com/richzhang/colorization/blob/master/models/alexnet_deploy_lab.prototxt)[\[Visualise\]](http://fabrik.cloudcv.org/caffe/load?id=20181208162637cezkh)
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### Seq2Seq
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