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Added 2 new graph models: SRCNN and VDSR
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name: "SRCNN"
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input: "data"
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input_dim: 1
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input_dim: 1
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input_dim: 768
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input_dim: 768
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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param {
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lr_mult: 1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 64
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kernel_size: 9
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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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: "conv2"
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type: "Convolution"
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bottom: "conv1"
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top: "conv2"
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param {
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lr_mult: 1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 32
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kernel_size: 1
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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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: "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: "conv3"
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type: "Convolution"
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bottom: "conv2"
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top: "conv3"
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param {
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lr_mult: 0.1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 1
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kernel_size: 5
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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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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name: "SRCNN"
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input: "data"
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input_dim: 1
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input_dim: 1
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input_dim: 256
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input_dim: 256
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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param {
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lr_mult: 1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 64
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kernel_size: 9
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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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: "conv2"
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type: "Convolution"
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bottom: "conv1"
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top: "conv2"
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param {
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lr_mult: 1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 32
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kernel_size: 1
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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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: "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: "conv3"
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type: "Convolution"
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bottom: "conv2"
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top: "conv3"
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param {
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lr_mult: 0.1
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}
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param {
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lr_mult: 0.1
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}
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convolution_param {
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num_output: 1
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kernel_size: 5
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stride: 1
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pad: 0
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weight_filler {
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type: "gaussian"
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std: 0.001
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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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version https://git-lfs.github.com/spec/v1
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oid sha256:57b02c71d12d4e3e203bafbe7db55ccedc27e2591bf9c230a17f7f26fd1af867
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size 33080
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version https://git-lfs.github.com/spec/v1
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oid sha256:0d211b866f54d30f5ce48d23c036cc477409f1b950a90db26c548d59b6e8b690
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size 32997
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SRCNN model from: https://github.com/Tonyfy/SRCNN
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Pruned Graphs
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1. SRCNN_deploynet.prototxt / SRCNN_iter_15000000_random_pruned.caffemodel
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- random pruned (60% conv, 85% fc)
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version https://git-lfs.github.com/spec/v1
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oid sha256:9199251105e65ccbe3c720192366383e12fca21afaa71a792595b13eb2d8ae9d
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size 2666880
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version https://git-lfs.github.com/spec/v1
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oid sha256:a9dd6eae8d0c50a99e01348bb8262b98802e9c3b3562484a0d9379ab4c165c5b
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size 2666806

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