+++ /dev/null
-*.caffemodel
+++ /dev/null
-name: "MobileNet-SSD"
-input: "data"
-input_shape {
- dim: 1
- dim: 3
- dim: 300
- dim: 300
-}
-layer {
- name: "conv0"
- type: "Convolution"
- bottom: "data"
- top: "conv0"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 32
- bias_term: false
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv0/bn"
- type: "BatchNorm"
- bottom: "conv0"
- top: "conv0"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv0/scale"
- type: "Scale"
- bottom: "conv0"
- top: "conv0"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv0/relu"
- type: "ReLU"
- bottom: "conv0"
- top: "conv0"
-}
-layer {
- name: "conv1/dw"
- type: "Convolution"
- bottom: "conv0"
- top: "conv1/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 32
- bias_term: false
- pad: 1
- kernel_size: 3
- group: 32
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv1/dw/bn"
- type: "BatchNorm"
- bottom: "conv1/dw"
- top: "conv1/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv1/dw/scale"
- type: "Scale"
- bottom: "conv1/dw"
- top: "conv1/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv1/dw/relu"
- type: "ReLU"
- bottom: "conv1/dw"
- top: "conv1/dw"
-}
-layer {
- name: "conv1"
- type: "Convolution"
- bottom: "conv1/dw"
- top: "conv1"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 64
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv1/bn"
- type: "BatchNorm"
- bottom: "conv1"
- top: "conv1"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv1/scale"
- type: "Scale"
- bottom: "conv1"
- top: "conv1"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv1/relu"
- type: "ReLU"
- bottom: "conv1"
- top: "conv1"
-}
-layer {
- name: "conv2/dw"
- type: "Convolution"
- bottom: "conv1"
- top: "conv2/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 64
- bias_term: false
- pad: 1
- kernel_size: 3
- stride: 2
- group: 64
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv2/dw/bn"
- type: "BatchNorm"
- bottom: "conv2/dw"
- top: "conv2/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv2/dw/scale"
- type: "Scale"
- bottom: "conv2/dw"
- top: "conv2/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv2/dw/relu"
- type: "ReLU"
- bottom: "conv2/dw"
- top: "conv2/dw"
-}
-layer {
- name: "conv2"
- type: "Convolution"
- bottom: "conv2/dw"
- top: "conv2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 128
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv2/bn"
- type: "BatchNorm"
- bottom: "conv2"
- top: "conv2"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv2/scale"
- type: "Scale"
- bottom: "conv2"
- top: "conv2"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv2/relu"
- type: "ReLU"
- bottom: "conv2"
- top: "conv2"
-}
-layer {
- name: "conv3/dw"
- type: "Convolution"
- bottom: "conv2"
- top: "conv3/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 128
- bias_term: false
- pad: 1
- kernel_size: 3
- group: 128
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv3/dw/bn"
- type: "BatchNorm"
- bottom: "conv3/dw"
- top: "conv3/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv3/dw/scale"
- type: "Scale"
- bottom: "conv3/dw"
- top: "conv3/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv3/dw/relu"
- type: "ReLU"
- bottom: "conv3/dw"
- top: "conv3/dw"
-}
-layer {
- name: "conv3"
- type: "Convolution"
- bottom: "conv3/dw"
- top: "conv3"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 128
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv3/bn"
- type: "BatchNorm"
- bottom: "conv3"
- top: "conv3"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv3/scale"
- type: "Scale"
- bottom: "conv3"
- top: "conv3"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv3/relu"
- type: "ReLU"
- bottom: "conv3"
- top: "conv3"
-}
-layer {
- name: "conv4/dw"
- type: "Convolution"
- bottom: "conv3"
- top: "conv4/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 128
- bias_term: false
- pad: 1
- kernel_size: 3
- stride: 2
- group: 128
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv4/dw/bn"
- type: "BatchNorm"
- bottom: "conv4/dw"
- top: "conv4/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv4/dw/scale"
- type: "Scale"
- bottom: "conv4/dw"
- top: "conv4/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv4/dw/relu"
- type: "ReLU"
- bottom: "conv4/dw"
- top: "conv4/dw"
-}
-layer {
- name: "conv4"
- type: "Convolution"
- bottom: "conv4/dw"
- top: "conv4"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 256
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv4/bn"
- type: "BatchNorm"
- bottom: "conv4"
- top: "conv4"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv4/scale"
- type: "Scale"
- bottom: "conv4"
- top: "conv4"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv4/relu"
- type: "ReLU"
- bottom: "conv4"
- top: "conv4"
-}
-layer {
- name: "conv5/dw"
- type: "Convolution"
- bottom: "conv4"
- top: "conv5/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 256
- bias_term: false
- pad: 1
- kernel_size: 3
- group: 256
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv5/dw/bn"
- type: "BatchNorm"
- bottom: "conv5/dw"
- top: "conv5/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv5/dw/scale"
- type: "Scale"
- bottom: "conv5/dw"
- top: "conv5/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv5/dw/relu"
- type: "ReLU"
- bottom: "conv5/dw"
- top: "conv5/dw"
-}
-layer {
- name: "conv5"
- type: "Convolution"
- bottom: "conv5/dw"
- top: "conv5"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 256
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv5/bn"
- type: "BatchNorm"
- bottom: "conv5"
- top: "conv5"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv5/scale"
- type: "Scale"
- bottom: "conv5"
- top: "conv5"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv5/relu"
- type: "ReLU"
- bottom: "conv5"
- top: "conv5"
-}
-layer {
- name: "conv6/dw"
- type: "Convolution"
- bottom: "conv5"
- top: "conv6/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 256
- bias_term: false
- pad: 1
- kernel_size: 3
- stride: 2
- group: 256
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv6/dw/bn"
- type: "BatchNorm"
- bottom: "conv6/dw"
- top: "conv6/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv6/dw/scale"
- type: "Scale"
- bottom: "conv6/dw"
- top: "conv6/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv6/dw/relu"
- type: "ReLU"
- bottom: "conv6/dw"
- top: "conv6/dw"
-}
-layer {
- name: "conv6"
- type: "Convolution"
- bottom: "conv6/dw"
- top: "conv6"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 512
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv6/bn"
- type: "BatchNorm"
- bottom: "conv6"
- top: "conv6"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv6/scale"
- type: "Scale"
- bottom: "conv6"
- top: "conv6"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv6/relu"
- type: "ReLU"
- bottom: "conv6"
- top: "conv6"
-}
-layer {
- name: "conv7/dw"
- type: "Convolution"
- bottom: "conv6"
- top: "conv7/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 512
- bias_term: false
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv7/dw/bn"
- type: "BatchNorm"
- bottom: "conv7/dw"
- top: "conv7/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv7/dw/scale"
- type: "Scale"
- bottom: "conv7/dw"
- top: "conv7/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv7/dw/relu"
- type: "ReLU"
- bottom: "conv7/dw"
- top: "conv7/dw"
-}
-layer {
- name: "conv7"
- type: "Convolution"
- bottom: "conv7/dw"
- top: "conv7"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 512
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv7/bn"
- type: "BatchNorm"
- bottom: "conv7"
- top: "conv7"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv7/scale"
- type: "Scale"
- bottom: "conv7"
- top: "conv7"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv7/relu"
- type: "ReLU"
- bottom: "conv7"
- top: "conv7"
-}
-layer {
- name: "conv8/dw"
- type: "Convolution"
- bottom: "conv7"
- top: "conv8/dw"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 512
- bias_term: false
- pad: 1
- kernel_size: 3
- group: 512
- engine: CAFFE
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv8/dw/bn"
- type: "BatchNorm"
- bottom: "conv8/dw"
- top: "conv8/dw"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv8/dw/scale"
- type: "Scale"
- bottom: "conv8/dw"
- top: "conv8/dw"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv8/dw/relu"
- type: "ReLU"
- bottom: "conv8/dw"
- top: "conv8/dw"
-}
-layer {
- name: "conv8"
- type: "Convolution"
- bottom: "conv8/dw"
- top: "conv8"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 512
- bias_term: false
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv8/bn"
- type: "BatchNorm"
- bottom: "conv8"
- top: "conv8"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv8/scale"
- type: "Scale"
- bottom: "conv8"
- top: "conv8"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
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- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv17_1/scale"
- type: "Scale"
- bottom: "conv17_1"
- top: "conv17_1"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv17_1/relu"
- type: "ReLU"
- bottom: "conv17_1"
- top: "conv17_1"
-}
-layer {
- name: "conv17_2"
- type: "Convolution"
- bottom: "conv17_1"
- top: "conv17_2"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- convolution_param {
- num_output: 128
- bias_term: false
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "msra"
- }
- }
-}
-layer {
- name: "conv17_2/bn"
- type: "BatchNorm"
- bottom: "conv17_2"
- top: "conv17_2"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
-}
-layer {
- name: "conv17_2/scale"
- type: "Scale"
- bottom: "conv17_2"
- top: "conv17_2"
- param {
- lr_mult: 0.1
- decay_mult: 0.0
- }
- param {
- lr_mult: 0.2
- decay_mult: 0.0
- }
- scale_param {
- filler {
- value: 1
- }
- bias_term: true
- bias_filler {
- value: 0
- }
- }
-}
-layer {
- name: "conv17_2/relu"
- type: "ReLU"
- bottom: "conv17_2"
- top: "conv17_2"
-}
-layer {
- name: "conv11_mbox_loc"
- type: "Convolution"
- bottom: "conv11"
- top: "conv11_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 12
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv11_mbox_loc_perm"
- type: "Permute"
- bottom: "conv11_mbox_loc"
- top: "conv11_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv11_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv11_mbox_loc_perm"
- top: "conv11_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv11_mbox_conf"
- type: "Convolution"
- bottom: "conv11"
- top: "conv11_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 63
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv11_mbox_conf_perm"
- type: "Permute"
- bottom: "conv11_mbox_conf"
- top: "conv11_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv11_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv11_mbox_conf_perm"
- top: "conv11_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv11_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv11"
- bottom: "data"
- top: "conv11_mbox_priorbox"
- prior_box_param {
- min_size: 60.0
- aspect_ratio: 2.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv13_mbox_loc"
- type: "Convolution"
- bottom: "conv13"
- top: "conv13_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv13_mbox_loc_perm"
- type: "Permute"
- bottom: "conv13_mbox_loc"
- top: "conv13_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv13_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv13_mbox_loc_perm"
- top: "conv13_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv13_mbox_conf"
- type: "Convolution"
- bottom: "conv13"
- top: "conv13_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv13_mbox_conf_perm"
- type: "Permute"
- bottom: "conv13_mbox_conf"
- top: "conv13_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv13_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv13_mbox_conf_perm"
- top: "conv13_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv13_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv13"
- bottom: "data"
- top: "conv13_mbox_priorbox"
- prior_box_param {
- min_size: 105.0
- max_size: 150.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv14_2_mbox_loc"
- type: "Convolution"
- bottom: "conv14_2"
- top: "conv14_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv14_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv14_2_mbox_loc"
- top: "conv14_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv14_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv14_2_mbox_loc_perm"
- top: "conv14_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv14_2_mbox_conf"
- type: "Convolution"
- bottom: "conv14_2"
- top: "conv14_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv14_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv14_2_mbox_conf"
- top: "conv14_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv14_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv14_2_mbox_conf_perm"
- top: "conv14_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv14_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv14_2"
- bottom: "data"
- top: "conv14_2_mbox_priorbox"
- prior_box_param {
- min_size: 150.0
- max_size: 195.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv15_2_mbox_loc"
- type: "Convolution"
- bottom: "conv15_2"
- top: "conv15_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv15_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv15_2_mbox_loc"
- top: "conv15_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv15_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv15_2_mbox_loc_perm"
- top: "conv15_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv15_2_mbox_conf"
- type: "Convolution"
- bottom: "conv15_2"
- top: "conv15_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv15_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv15_2_mbox_conf"
- top: "conv15_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv15_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv15_2_mbox_conf_perm"
- top: "conv15_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv15_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv15_2"
- bottom: "data"
- top: "conv15_2_mbox_priorbox"
- prior_box_param {
- min_size: 195.0
- max_size: 240.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv16_2_mbox_loc"
- type: "Convolution"
- bottom: "conv16_2"
- top: "conv16_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv16_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv16_2_mbox_loc"
- top: "conv16_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv16_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv16_2_mbox_loc_perm"
- top: "conv16_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv16_2_mbox_conf"
- type: "Convolution"
- bottom: "conv16_2"
- top: "conv16_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv16_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv16_2_mbox_conf"
- top: "conv16_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv16_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv16_2_mbox_conf_perm"
- top: "conv16_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv16_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv16_2"
- bottom: "data"
- top: "conv16_2_mbox_priorbox"
- prior_box_param {
- min_size: 240.0
- max_size: 285.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "conv17_2_mbox_loc"
- type: "Convolution"
- bottom: "conv17_2"
- top: "conv17_2_mbox_loc"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv17_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv17_2_mbox_loc"
- top: "conv17_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv17_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv17_2_mbox_loc_perm"
- top: "conv17_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv17_2_mbox_conf"
- type: "Convolution"
- bottom: "conv17_2"
- top: "conv17_2_mbox_conf"
- param {
- lr_mult: 1.0
- decay_mult: 1.0
- }
- param {
- lr_mult: 2.0
- decay_mult: 0.0
- }
- convolution_param {
- num_output: 126
- kernel_size: 1
- weight_filler {
- type: "msra"
- }
- bias_filler {
- type: "constant"
- value: 0.0
- }
- }
-}
-layer {
- name: "conv17_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv17_2_mbox_conf"
- top: "conv17_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv17_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv17_2_mbox_conf_perm"
- top: "conv17_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv17_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv17_2"
- bottom: "data"
- top: "conv17_2_mbox_priorbox"
- prior_box_param {
- min_size: 285.0
- max_size: 300.0
- aspect_ratio: 2.0
- aspect_ratio: 3.0
- flip: true
- clip: false
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- offset: 0.5
- }
-}
-layer {
- name: "mbox_loc"
- type: "Concat"
- bottom: "conv11_mbox_loc_flat"
- bottom: "conv13_mbox_loc_flat"
- bottom: "conv14_2_mbox_loc_flat"
- bottom: "conv15_2_mbox_loc_flat"
- bottom: "conv16_2_mbox_loc_flat"
- bottom: "conv17_2_mbox_loc_flat"
- top: "mbox_loc"
- concat_param {
- axis: 1
- }
-}
-layer {
- name: "mbox_conf"
- type: "Concat"
- bottom: "conv11_mbox_conf_flat"
- bottom: "conv13_mbox_conf_flat"
- bottom: "conv14_2_mbox_conf_flat"
- bottom: "conv15_2_mbox_conf_flat"
- bottom: "conv16_2_mbox_conf_flat"
- bottom: "conv17_2_mbox_conf_flat"
- top: "mbox_conf"
- concat_param {
- axis: 1
- }
-}
-layer {
- name: "mbox_priorbox"
- type: "Concat"
- bottom: "conv11_mbox_priorbox"
- bottom: "conv13_mbox_priorbox"
- bottom: "conv14_2_mbox_priorbox"
- bottom: "conv15_2_mbox_priorbox"
- bottom: "conv16_2_mbox_priorbox"
- bottom: "conv17_2_mbox_priorbox"
- top: "mbox_priorbox"
- concat_param {
- axis: 2
- }
-}
-layer {
- name: "mbox_conf_reshape"
- type: "Reshape"
- bottom: "mbox_conf"
- top: "mbox_conf_reshape"
- reshape_param {
- shape {
- dim: 0
- dim: -1
- dim: 21
- }
- }
-}
-layer {
- name: "mbox_conf_softmax"
- type: "Softmax"
- bottom: "mbox_conf_reshape"
- top: "mbox_conf_softmax"
- softmax_param {
- axis: 2
- }
-}
-layer {
- name: "mbox_conf_flatten"
- type: "Flatten"
- bottom: "mbox_conf_softmax"
- top: "mbox_conf_flatten"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "detection_out"
- type: "DetectionOutput"
- bottom: "mbox_loc"
- bottom: "mbox_conf_flatten"
- bottom: "mbox_priorbox"
- top: "detection_out"
- include {
- phase: TEST
- }
- detection_output_param {
- num_classes: 21
- share_location: true
- background_label_id: 0
- nms_param {
- nms_threshold: 0.45
- top_k: 100
- }
- code_type: CENTER_SIZE
- keep_top_k: 100
- confidence_threshold: 0.25
- }
-}
\ No newline at end of file
+++ /dev/null
-name: "VGG_VOC0712_SSD_300x300_deploy"
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 300
-input_dim: 300
-layer {
- name: "conv1_1"
- type: "Convolution"
- bottom: "data"
- top: "conv1_1"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu1_1"
- type: "ReLU"
- bottom: "conv1_1"
- top: "conv1_1"
-}
-layer {
- name: "conv1_2"
- type: "Convolution"
- bottom: "conv1_1"
- top: "conv1_2"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu1_2"
- type: "ReLU"
- bottom: "conv1_2"
- top: "conv1_2"
-}
-layer {
- name: "pool1"
- type: "Pooling"
- bottom: "conv1_2"
- top: "pool1"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv2_1"
- type: "Convolution"
- bottom: "pool1"
- top: "conv2_1"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu2_1"
- type: "ReLU"
- bottom: "conv2_1"
- top: "conv2_1"
-}
-layer {
- name: "conv2_2"
- type: "Convolution"
- bottom: "conv2_1"
- top: "conv2_2"
- param {
- lr_mult: 0
- decay_mult: 0
- }
- param {
- lr_mult: 0
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu2_2"
- type: "ReLU"
- bottom: "conv2_2"
- top: "conv2_2"
-}
-layer {
- name: "pool2"
- type: "Pooling"
- bottom: "conv2_2"
- top: "pool2"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv3_1"
- type: "Convolution"
- bottom: "pool2"
- top: "conv3_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu3_1"
- type: "ReLU"
- bottom: "conv3_1"
- top: "conv3_1"
-}
-layer {
- name: "conv3_2"
- type: "Convolution"
- bottom: "conv3_1"
- top: "conv3_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu3_2"
- type: "ReLU"
- bottom: "conv3_2"
- top: "conv3_2"
-}
-layer {
- name: "conv3_3"
- type: "Convolution"
- bottom: "conv3_2"
- top: "conv3_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu3_3"
- type: "ReLU"
- bottom: "conv3_3"
- top: "conv3_3"
-}
-layer {
- name: "pool3"
- type: "Pooling"
- bottom: "conv3_3"
- top: "pool3"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv4_1"
- type: "Convolution"
- bottom: "pool3"
- top: "conv4_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu4_1"
- type: "ReLU"
- bottom: "conv4_1"
- top: "conv4_1"
-}
-layer {
- name: "conv4_2"
- type: "Convolution"
- bottom: "conv4_1"
- top: "conv4_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu4_2"
- type: "ReLU"
- bottom: "conv4_2"
- top: "conv4_2"
-}
-layer {
- name: "conv4_3"
- type: "Convolution"
- bottom: "conv4_2"
- top: "conv4_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- kernel_size: 3
- weight_filler {
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- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu4_3"
- type: "ReLU"
- bottom: "conv4_3"
- top: "conv4_3"
-}
-layer {
- name: "pool4"
- type: "Pooling"
- bottom: "conv4_3"
- top: "pool4"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv5_1"
- type: "Convolution"
- bottom: "pool4"
- top: "conv5_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
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- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu5_1"
- type: "ReLU"
- bottom: "conv5_1"
- top: "conv5_1"
-}
-layer {
- name: "conv5_2"
- type: "Convolution"
- bottom: "conv5_1"
- top: "conv5_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu5_2"
- type: "ReLU"
- bottom: "conv5_2"
- top: "conv5_2"
-}
-layer {
- name: "conv5_3"
- type: "Convolution"
- bottom: "conv5_2"
- top: "conv5_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
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- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu5_3"
- type: "ReLU"
- bottom: "conv5_3"
- top: "conv5_3"
-}
-layer {
- name: "pool5"
- type: "Pooling"
- bottom: "conv5_3"
- top: "pool5"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "fc6"
- type: "Convolution"
- bottom: "pool5"
- top: "fc6"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 1024
- pad: 6
- kernel_size: 3
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- dilation: 6
- }
-}
-layer {
- name: "relu6"
- type: "ReLU"
- bottom: "fc6"
- top: "fc6"
-}
-layer {
- name: "fc7"
- type: "Convolution"
- bottom: "fc6"
- top: "fc7"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 1024
- kernel_size: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "relu7"
- type: "ReLU"
- bottom: "fc7"
- top: "fc7"
-}
-layer {
- name: "conv6_1"
- type: "Convolution"
- bottom: "fc7"
- top: "conv6_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 0
- kernel_size: 1
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv6_1_relu"
- type: "ReLU"
- bottom: "conv6_1"
- top: "conv6_1"
-}
-layer {
- name: "conv6_2"
- type: "Convolution"
- bottom: "conv6_1"
- top: "conv6_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv6_2_relu"
- type: "ReLU"
- bottom: "conv6_2"
- top: "conv6_2"
-}
-layer {
- name: "conv7_1"
- type: "Convolution"
- bottom: "conv6_2"
- top: "conv7_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 0
- kernel_size: 1
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv7_1_relu"
- type: "ReLU"
- bottom: "conv7_1"
- top: "conv7_1"
-}
-layer {
- name: "conv7_2"
- type: "Convolution"
- bottom: "conv7_1"
- top: "conv7_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv7_2_relu"
- type: "ReLU"
- bottom: "conv7_2"
- top: "conv7_2"
-}
-layer {
- name: "conv8_1"
- type: "Convolution"
- bottom: "conv7_2"
- top: "conv8_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 0
- kernel_size: 1
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv8_1_relu"
- type: "ReLU"
- bottom: "conv8_1"
- top: "conv8_1"
-}
-layer {
- name: "conv8_2"
- type: "Convolution"
- bottom: "conv8_1"
- top: "conv8_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 2
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv8_2_relu"
- type: "ReLU"
- bottom: "conv8_2"
- top: "conv8_2"
-}
-layer {
- name: "pool6"
- type: "Pooling"
- bottom: "conv8_2"
- top: "pool6"
- pooling_param {
- pool: AVE
- global_pooling: true
- }
-}
-layer {
- name: "conv4_3_norm"
- type: "NormalizeBBox"
- bottom: "conv4_3"
- top: "conv4_3_norm"
- normalize_bbox_param {
- across_spatial: false
- scale_filler {
- type: "constant"
- value: 20
- }
- channel_shared: false
- }
-}
-layer {
- name: "conv4_3_norm_mbox_loc"
- type: "Convolution"
- bottom: "conv4_3_norm"
- top: "conv4_3_norm_mbox_loc"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 12
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv4_3_norm_mbox_loc_perm"
- type: "Permute"
- bottom: "conv4_3_norm_mbox_loc"
- top: "conv4_3_norm_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv4_3_norm_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv4_3_norm_mbox_loc_perm"
- top: "conv4_3_norm_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv4_3_norm_mbox_conf"
- type: "Convolution"
- bottom: "conv4_3_norm"
- top: "conv4_3_norm_mbox_conf"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 63
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv4_3_norm_mbox_conf_perm"
- type: "Permute"
- bottom: "conv4_3_norm_mbox_conf"
- top: "conv4_3_norm_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv4_3_norm_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv4_3_norm_mbox_conf_perm"
- top: "conv4_3_norm_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv4_3_norm_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv4_3_norm"
- bottom: "data"
- top: "conv4_3_norm_mbox_priorbox"
- prior_box_param {
- min_size: 30.0
- aspect_ratio: 2
- flip: true
- clip: true
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- }
-}
-layer {
- name: "fc7_mbox_loc"
- type: "Convolution"
- bottom: "fc7"
- top: "fc7_mbox_loc"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "fc7_mbox_loc_perm"
- type: "Permute"
- bottom: "fc7_mbox_loc"
- top: "fc7_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "fc7_mbox_loc_flat"
- type: "Flatten"
- bottom: "fc7_mbox_loc_perm"
- top: "fc7_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "fc7_mbox_conf"
- type: "Convolution"
- bottom: "fc7"
- top: "fc7_mbox_conf"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 126
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "fc7_mbox_conf_perm"
- type: "Permute"
- bottom: "fc7_mbox_conf"
- top: "fc7_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "fc7_mbox_conf_flat"
- type: "Flatten"
- bottom: "fc7_mbox_conf_perm"
- top: "fc7_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "fc7_mbox_priorbox"
- type: "PriorBox"
- bottom: "fc7"
- bottom: "data"
- top: "fc7_mbox_priorbox"
- prior_box_param {
- min_size: 60.0
- max_size: 114.0
- aspect_ratio: 2
- aspect_ratio: 3
- flip: true
- clip: true
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- }
-}
-layer {
- name: "conv6_2_mbox_loc"
- type: "Convolution"
- bottom: "conv6_2"
- top: "conv6_2_mbox_loc"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv6_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv6_2_mbox_loc"
- top: "conv6_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv6_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv6_2_mbox_loc_perm"
- top: "conv6_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv6_2_mbox_conf"
- type: "Convolution"
- bottom: "conv6_2"
- top: "conv6_2_mbox_conf"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 126
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv6_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv6_2_mbox_conf"
- top: "conv6_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv6_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv6_2_mbox_conf_perm"
- top: "conv6_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv6_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv6_2"
- bottom: "data"
- top: "conv6_2_mbox_priorbox"
- prior_box_param {
- min_size: 114.0
- max_size: 168.0
- aspect_ratio: 2
- aspect_ratio: 3
- flip: true
- clip: true
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- }
-}
-layer {
- name: "conv7_2_mbox_loc"
- type: "Convolution"
- bottom: "conv7_2"
- top: "conv7_2_mbox_loc"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv7_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv7_2_mbox_loc"
- top: "conv7_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv7_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv7_2_mbox_loc_perm"
- top: "conv7_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv7_2_mbox_conf"
- type: "Convolution"
- bottom: "conv7_2"
- top: "conv7_2_mbox_conf"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 126
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv7_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv7_2_mbox_conf"
- top: "conv7_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv7_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv7_2_mbox_conf_perm"
- top: "conv7_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv7_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv7_2"
- bottom: "data"
- top: "conv7_2_mbox_priorbox"
- prior_box_param {
- min_size: 168.0
- max_size: 222.0
- aspect_ratio: 2
- aspect_ratio: 3
- flip: true
- clip: true
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- }
-}
-layer {
- name: "conv8_2_mbox_loc"
- type: "Convolution"
- bottom: "conv8_2"
- top: "conv8_2_mbox_loc"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv8_2_mbox_loc_perm"
- type: "Permute"
- bottom: "conv8_2_mbox_loc"
- top: "conv8_2_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv8_2_mbox_loc_flat"
- type: "Flatten"
- bottom: "conv8_2_mbox_loc_perm"
- top: "conv8_2_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv8_2_mbox_conf"
- type: "Convolution"
- bottom: "conv8_2"
- top: "conv8_2_mbox_conf"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 126
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "conv8_2_mbox_conf_perm"
- type: "Permute"
- bottom: "conv8_2_mbox_conf"
- top: "conv8_2_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "conv8_2_mbox_conf_flat"
- type: "Flatten"
- bottom: "conv8_2_mbox_conf_perm"
- top: "conv8_2_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "conv8_2_mbox_priorbox"
- type: "PriorBox"
- bottom: "conv8_2"
- bottom: "data"
- top: "conv8_2_mbox_priorbox"
- prior_box_param {
- min_size: 222.0
- max_size: 276.0
- aspect_ratio: 2
- aspect_ratio: 3
- flip: true
- clip: true
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- }
-}
-layer {
- name: "pool6_mbox_loc"
- type: "Convolution"
- bottom: "pool6"
- top: "pool6_mbox_loc"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "pool6_mbox_loc_perm"
- type: "Permute"
- bottom: "pool6_mbox_loc"
- top: "pool6_mbox_loc_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "pool6_mbox_loc_flat"
- type: "Flatten"
- bottom: "pool6_mbox_loc_perm"
- top: "pool6_mbox_loc_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "pool6_mbox_conf"
- type: "Convolution"
- bottom: "pool6"
- top: "pool6_mbox_conf"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 126
- pad: 1
- kernel_size: 3
- stride: 1
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "pool6_mbox_conf_perm"
- type: "Permute"
- bottom: "pool6_mbox_conf"
- top: "pool6_mbox_conf_perm"
- permute_param {
- order: 0
- order: 2
- order: 3
- order: 1
- }
-}
-layer {
- name: "pool6_mbox_conf_flat"
- type: "Flatten"
- bottom: "pool6_mbox_conf_perm"
- top: "pool6_mbox_conf_flat"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "pool6_mbox_priorbox"
- type: "PriorBox"
- bottom: "pool6"
- bottom: "data"
- top: "pool6_mbox_priorbox"
- prior_box_param {
- min_size: 276.0
- max_size: 330.0
- aspect_ratio: 2
- aspect_ratio: 3
- flip: true
- clip: true
- variance: 0.1
- variance: 0.1
- variance: 0.2
- variance: 0.2
- }
-}
-layer {
- name: "mbox_loc"
- type: "Concat"
- bottom: "conv4_3_norm_mbox_loc_flat"
- bottom: "fc7_mbox_loc_flat"
- bottom: "conv6_2_mbox_loc_flat"
- bottom: "conv7_2_mbox_loc_flat"
- bottom: "conv8_2_mbox_loc_flat"
- bottom: "pool6_mbox_loc_flat"
- top: "mbox_loc"
- concat_param {
- axis: 1
- }
-}
-layer {
- name: "mbox_conf"
- type: "Concat"
- bottom: "conv4_3_norm_mbox_conf_flat"
- bottom: "fc7_mbox_conf_flat"
- bottom: "conv6_2_mbox_conf_flat"
- bottom: "conv7_2_mbox_conf_flat"
- bottom: "conv8_2_mbox_conf_flat"
- bottom: "pool6_mbox_conf_flat"
- top: "mbox_conf"
- concat_param {
- axis: 1
- }
-}
-layer {
- name: "mbox_priorbox"
- type: "Concat"
- bottom: "conv4_3_norm_mbox_priorbox"
- bottom: "fc7_mbox_priorbox"
- bottom: "conv6_2_mbox_priorbox"
- bottom: "conv7_2_mbox_priorbox"
- bottom: "conv8_2_mbox_priorbox"
- bottom: "pool6_mbox_priorbox"
- top: "mbox_priorbox"
- concat_param {
- axis: 2
- }
-}
-layer {
- name: "mbox_conf_reshape"
- type: "Reshape"
- bottom: "mbox_conf"
- top: "mbox_conf_reshape"
- reshape_param {
- shape {
- dim: 0
- dim: -1
- dim: 21
- }
- }
-}
-layer {
- name: "mbox_conf_softmax"
- type: "Softmax"
- bottom: "mbox_conf_reshape"
- top: "mbox_conf_softmax"
- softmax_param {
- axis: 2
- }
-}
-layer {
- name: "mbox_conf_flatten"
- type: "Flatten"
- bottom: "mbox_conf_softmax"
- top: "mbox_conf_flatten"
- flatten_param {
- axis: 1
- }
-}
-layer {
- name: "detection_out"
- type: "DetectionOutput"
- bottom: "mbox_loc"
- bottom: "mbox_conf_flatten"
- bottom: "mbox_priorbox"
- top: "detection_out"
- include {
- phase: TEST
- }
- detection_output_param {
- num_classes: 21
- share_location: true
- background_label_id: 0
- nms_threshold: 0.45
- top_k: 400
- code_type: CENTER_SIZE
- keep_top_k: 200
- confidence_threshold: 0.01
- }
-}
+++ /dev/null
-name: "GoogleNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 224
-input_dim: 224
-layer {
- name: "conv1/7x7_s2"
- type: "Convolution"
- bottom: "data"
- top: "conv1/7x7_s2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 3
- kernel_size: 7
- stride: 2
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "conv1/relu_7x7"
- type: "ReLU"
- bottom: "conv1/7x7_s2"
- top: "conv1/7x7_s2"
-}
-layer {
- name: "pool1/3x3_s2"
- type: "Pooling"
- bottom: "conv1/7x7_s2"
- top: "pool1/3x3_s2"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 2
- }
-}
-layer {
- name: "pool1/norm1"
- type: "LRN"
- bottom: "pool1/3x3_s2"
- top: "pool1/norm1"
- lrn_param {
- local_size: 5
- alpha: 0.0001
- beta: 0.75
- }
-}
-layer {
- name: "conv2/3x3_reduce"
- type: "Convolution"
- bottom: "pool1/norm1"
- top: "conv2/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "conv2/relu_3x3_reduce"
- type: "ReLU"
- bottom: "conv2/3x3_reduce"
- top: "conv2/3x3_reduce"
-}
-layer {
- name: "conv2/3x3"
- type: "Convolution"
- bottom: "conv2/3x3_reduce"
- top: "conv2/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 192
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "conv2/relu_3x3"
- type: "ReLU"
- bottom: "conv2/3x3"
- top: "conv2/3x3"
-}
-layer {
- name: "conv2/norm2"
- type: "LRN"
- bottom: "conv2/3x3"
- top: "conv2/norm2"
- lrn_param {
- local_size: 5
- alpha: 0.0001
- beta: 0.75
- }
-}
-layer {
- name: "pool2/3x3_s2"
- type: "Pooling"
- bottom: "conv2/norm2"
- top: "pool2/3x3_s2"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 2
- }
-}
-layer {
- name: "inception_3a/1x1"
- type: "Convolution"
- bottom: "pool2/3x3_s2"
- top: "inception_3a/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3a/relu_1x1"
- type: "ReLU"
- bottom: "inception_3a/1x1"
- top: "inception_3a/1x1"
-}
-layer {
- name: "inception_3a/3x3_reduce"
- type: "Convolution"
- bottom: "pool2/3x3_s2"
- top: "inception_3a/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 96
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3a/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_3a/3x3_reduce"
- top: "inception_3a/3x3_reduce"
-}
-layer {
- name: "inception_3a/3x3"
- type: "Convolution"
- bottom: "inception_3a/3x3_reduce"
- top: "inception_3a/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3a/relu_3x3"
- type: "ReLU"
- bottom: "inception_3a/3x3"
- top: "inception_3a/3x3"
-}
-layer {
- name: "inception_3a/5x5_reduce"
- type: "Convolution"
- bottom: "pool2/3x3_s2"
- top: "inception_3a/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 16
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3a/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_3a/5x5_reduce"
- top: "inception_3a/5x5_reduce"
-}
-layer {
- name: "inception_3a/5x5"
- type: "Convolution"
- bottom: "inception_3a/5x5_reduce"
- top: "inception_3a/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 32
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3a/relu_5x5"
- type: "ReLU"
- bottom: "inception_3a/5x5"
- top: "inception_3a/5x5"
-}
-layer {
- name: "inception_3a/pool"
- type: "Pooling"
- bottom: "pool2/3x3_s2"
- top: "inception_3a/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_3a/pool_proj"
- type: "Convolution"
- bottom: "inception_3a/pool"
- top: "inception_3a/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 32
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3a/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_3a/pool_proj"
- top: "inception_3a/pool_proj"
-}
-layer {
- name: "inception_3a/output"
- type: "Concat"
- bottom: "inception_3a/1x1"
- bottom: "inception_3a/3x3"
- bottom: "inception_3a/5x5"
- bottom: "inception_3a/pool_proj"
- top: "inception_3a/output"
-}
-layer {
- name: "inception_3b/1x1"
- type: "Convolution"
- bottom: "inception_3a/output"
- top: "inception_3b/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3b/relu_1x1"
- type: "ReLU"
- bottom: "inception_3b/1x1"
- top: "inception_3b/1x1"
-}
-layer {
- name: "inception_3b/3x3_reduce"
- type: "Convolution"
- bottom: "inception_3a/output"
- top: "inception_3b/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3b/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_3b/3x3_reduce"
- top: "inception_3b/3x3_reduce"
-}
-layer {
- name: "inception_3b/3x3"
- type: "Convolution"
- bottom: "inception_3b/3x3_reduce"
- top: "inception_3b/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 192
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3b/relu_3x3"
- type: "ReLU"
- bottom: "inception_3b/3x3"
- top: "inception_3b/3x3"
-}
-layer {
- name: "inception_3b/5x5_reduce"
- type: "Convolution"
- bottom: "inception_3a/output"
- top: "inception_3b/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 32
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3b/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_3b/5x5_reduce"
- top: "inception_3b/5x5_reduce"
-}
-layer {
- name: "inception_3b/5x5"
- type: "Convolution"
- bottom: "inception_3b/5x5_reduce"
- top: "inception_3b/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 96
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3b/relu_5x5"
- type: "ReLU"
- bottom: "inception_3b/5x5"
- top: "inception_3b/5x5"
-}
-layer {
- name: "inception_3b/pool"
- type: "Pooling"
- bottom: "inception_3a/output"
- top: "inception_3b/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_3b/pool_proj"
- type: "Convolution"
- bottom: "inception_3b/pool"
- top: "inception_3b/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_3b/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_3b/pool_proj"
- top: "inception_3b/pool_proj"
-}
-layer {
- name: "inception_3b/output"
- type: "Concat"
- bottom: "inception_3b/1x1"
- bottom: "inception_3b/3x3"
- bottom: "inception_3b/5x5"
- bottom: "inception_3b/pool_proj"
- top: "inception_3b/output"
-}
-layer {
- name: "pool3/3x3_s2"
- type: "Pooling"
- bottom: "inception_3b/output"
- top: "pool3/3x3_s2"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 2
- }
-}
-layer {
- name: "inception_4a/1x1"
- type: "Convolution"
- bottom: "pool3/3x3_s2"
- top: "inception_4a/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 192
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4a/relu_1x1"
- type: "ReLU"
- bottom: "inception_4a/1x1"
- top: "inception_4a/1x1"
-}
-layer {
- name: "inception_4a/3x3_reduce"
- type: "Convolution"
- bottom: "pool3/3x3_s2"
- top: "inception_4a/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 96
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4a/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_4a/3x3_reduce"
- top: "inception_4a/3x3_reduce"
-}
-layer {
- name: "inception_4a/3x3"
- type: "Convolution"
- bottom: "inception_4a/3x3_reduce"
- top: "inception_4a/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 208
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4a/relu_3x3"
- type: "ReLU"
- bottom: "inception_4a/3x3"
- top: "inception_4a/3x3"
-}
-layer {
- name: "inception_4a/5x5_reduce"
- type: "Convolution"
- bottom: "pool3/3x3_s2"
- top: "inception_4a/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 16
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4a/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_4a/5x5_reduce"
- top: "inception_4a/5x5_reduce"
-}
-layer {
- name: "inception_4a/5x5"
- type: "Convolution"
- bottom: "inception_4a/5x5_reduce"
- top: "inception_4a/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 48
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4a/relu_5x5"
- type: "ReLU"
- bottom: "inception_4a/5x5"
- top: "inception_4a/5x5"
-}
-layer {
- name: "inception_4a/pool"
- type: "Pooling"
- bottom: "pool3/3x3_s2"
- top: "inception_4a/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_4a/pool_proj"
- type: "Convolution"
- bottom: "inception_4a/pool"
- top: "inception_4a/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4a/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_4a/pool_proj"
- top: "inception_4a/pool_proj"
-}
-layer {
- name: "inception_4a/output"
- type: "Concat"
- bottom: "inception_4a/1x1"
- bottom: "inception_4a/3x3"
- bottom: "inception_4a/5x5"
- bottom: "inception_4a/pool_proj"
- top: "inception_4a/output"
-}
-layer {
- name: "inception_4b/1x1"
- type: "Convolution"
- bottom: "inception_4a/output"
- top: "inception_4b/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 160
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4b/relu_1x1"
- type: "ReLU"
- bottom: "inception_4b/1x1"
- top: "inception_4b/1x1"
-}
-layer {
- name: "inception_4b/3x3_reduce"
- type: "Convolution"
- bottom: "inception_4a/output"
- top: "inception_4b/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 112
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4b/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_4b/3x3_reduce"
- top: "inception_4b/3x3_reduce"
-}
-layer {
- name: "inception_4b/3x3"
- type: "Convolution"
- bottom: "inception_4b/3x3_reduce"
- top: "inception_4b/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 224
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4b/relu_3x3"
- type: "ReLU"
- bottom: "inception_4b/3x3"
- top: "inception_4b/3x3"
-}
-layer {
- name: "inception_4b/5x5_reduce"
- type: "Convolution"
- bottom: "inception_4a/output"
- top: "inception_4b/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4b/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_4b/5x5_reduce"
- top: "inception_4b/5x5_reduce"
-}
-layer {
- name: "inception_4b/5x5"
- type: "Convolution"
- bottom: "inception_4b/5x5_reduce"
- top: "inception_4b/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4b/relu_5x5"
- type: "ReLU"
- bottom: "inception_4b/5x5"
- top: "inception_4b/5x5"
-}
-layer {
- name: "inception_4b/pool"
- type: "Pooling"
- bottom: "inception_4a/output"
- top: "inception_4b/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_4b/pool_proj"
- type: "Convolution"
- bottom: "inception_4b/pool"
- top: "inception_4b/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4b/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_4b/pool_proj"
- top: "inception_4b/pool_proj"
-}
-layer {
- name: "inception_4b/output"
- type: "Concat"
- bottom: "inception_4b/1x1"
- bottom: "inception_4b/3x3"
- bottom: "inception_4b/5x5"
- bottom: "inception_4b/pool_proj"
- top: "inception_4b/output"
-}
-layer {
- name: "inception_4c/1x1"
- type: "Convolution"
- bottom: "inception_4b/output"
- top: "inception_4c/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4c/relu_1x1"
- type: "ReLU"
- bottom: "inception_4c/1x1"
- top: "inception_4c/1x1"
-}
-layer {
- name: "inception_4c/3x3_reduce"
- type: "Convolution"
- bottom: "inception_4b/output"
- top: "inception_4c/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4c/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_4c/3x3_reduce"
- top: "inception_4c/3x3_reduce"
-}
-layer {
- name: "inception_4c/3x3"
- type: "Convolution"
- bottom: "inception_4c/3x3_reduce"
- top: "inception_4c/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4c/relu_3x3"
- type: "ReLU"
- bottom: "inception_4c/3x3"
- top: "inception_4c/3x3"
-}
-layer {
- name: "inception_4c/5x5_reduce"
- type: "Convolution"
- bottom: "inception_4b/output"
- top: "inception_4c/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 24
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4c/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_4c/5x5_reduce"
- top: "inception_4c/5x5_reduce"
-}
-layer {
- name: "inception_4c/5x5"
- type: "Convolution"
- bottom: "inception_4c/5x5_reduce"
- top: "inception_4c/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4c/relu_5x5"
- type: "ReLU"
- bottom: "inception_4c/5x5"
- top: "inception_4c/5x5"
-}
-layer {
- name: "inception_4c/pool"
- type: "Pooling"
- bottom: "inception_4b/output"
- top: "inception_4c/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_4c/pool_proj"
- type: "Convolution"
- bottom: "inception_4c/pool"
- top: "inception_4c/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4c/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_4c/pool_proj"
- top: "inception_4c/pool_proj"
-}
-layer {
- name: "inception_4c/output"
- type: "Concat"
- bottom: "inception_4c/1x1"
- bottom: "inception_4c/3x3"
- bottom: "inception_4c/5x5"
- bottom: "inception_4c/pool_proj"
- top: "inception_4c/output"
-}
-layer {
- name: "inception_4d/1x1"
- type: "Convolution"
- bottom: "inception_4c/output"
- top: "inception_4d/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 112
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4d/relu_1x1"
- type: "ReLU"
- bottom: "inception_4d/1x1"
- top: "inception_4d/1x1"
-}
-layer {
- name: "inception_4d/3x3_reduce"
- type: "Convolution"
- bottom: "inception_4c/output"
- top: "inception_4d/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 144
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4d/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_4d/3x3_reduce"
- top: "inception_4d/3x3_reduce"
-}
-layer {
- name: "inception_4d/3x3"
- type: "Convolution"
- bottom: "inception_4d/3x3_reduce"
- top: "inception_4d/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 288
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4d/relu_3x3"
- type: "ReLU"
- bottom: "inception_4d/3x3"
- top: "inception_4d/3x3"
-}
-layer {
- name: "inception_4d/5x5_reduce"
- type: "Convolution"
- bottom: "inception_4c/output"
- top: "inception_4d/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 32
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4d/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_4d/5x5_reduce"
- top: "inception_4d/5x5_reduce"
-}
-layer {
- name: "inception_4d/5x5"
- type: "Convolution"
- bottom: "inception_4d/5x5_reduce"
- top: "inception_4d/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4d/relu_5x5"
- type: "ReLU"
- bottom: "inception_4d/5x5"
- top: "inception_4d/5x5"
-}
-layer {
- name: "inception_4d/pool"
- type: "Pooling"
- bottom: "inception_4c/output"
- top: "inception_4d/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_4d/pool_proj"
- type: "Convolution"
- bottom: "inception_4d/pool"
- top: "inception_4d/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4d/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_4d/pool_proj"
- top: "inception_4d/pool_proj"
-}
-layer {
- name: "inception_4d/output"
- type: "Concat"
- bottom: "inception_4d/1x1"
- bottom: "inception_4d/3x3"
- bottom: "inception_4d/5x5"
- bottom: "inception_4d/pool_proj"
- top: "inception_4d/output"
-}
-layer {
- name: "inception_4e/1x1"
- type: "Convolution"
- bottom: "inception_4d/output"
- top: "inception_4e/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4e/relu_1x1"
- type: "ReLU"
- bottom: "inception_4e/1x1"
- top: "inception_4e/1x1"
-}
-layer {
- name: "inception_4e/3x3_reduce"
- type: "Convolution"
- bottom: "inception_4d/output"
- top: "inception_4e/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 160
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4e/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_4e/3x3_reduce"
- top: "inception_4e/3x3_reduce"
-}
-layer {
- name: "inception_4e/3x3"
- type: "Convolution"
- bottom: "inception_4e/3x3_reduce"
- top: "inception_4e/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 320
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4e/relu_3x3"
- type: "ReLU"
- bottom: "inception_4e/3x3"
- top: "inception_4e/3x3"
-}
-layer {
- name: "inception_4e/5x5_reduce"
- type: "Convolution"
- bottom: "inception_4d/output"
- top: "inception_4e/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 32
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4e/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_4e/5x5_reduce"
- top: "inception_4e/5x5_reduce"
-}
-layer {
- name: "inception_4e/5x5"
- type: "Convolution"
- bottom: "inception_4e/5x5_reduce"
- top: "inception_4e/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4e/relu_5x5"
- type: "ReLU"
- bottom: "inception_4e/5x5"
- top: "inception_4e/5x5"
-}
-layer {
- name: "inception_4e/pool"
- type: "Pooling"
- bottom: "inception_4d/output"
- top: "inception_4e/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_4e/pool_proj"
- type: "Convolution"
- bottom: "inception_4e/pool"
- top: "inception_4e/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_4e/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_4e/pool_proj"
- top: "inception_4e/pool_proj"
-}
-layer {
- name: "inception_4e/output"
- type: "Concat"
- bottom: "inception_4e/1x1"
- bottom: "inception_4e/3x3"
- bottom: "inception_4e/5x5"
- bottom: "inception_4e/pool_proj"
- top: "inception_4e/output"
-}
-layer {
- name: "pool4/3x3_s2"
- type: "Pooling"
- bottom: "inception_4e/output"
- top: "pool4/3x3_s2"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 2
- }
-}
-layer {
- name: "inception_5a/1x1"
- type: "Convolution"
- bottom: "pool4/3x3_s2"
- top: "inception_5a/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5a/relu_1x1"
- type: "ReLU"
- bottom: "inception_5a/1x1"
- top: "inception_5a/1x1"
-}
-layer {
- name: "inception_5a/3x3_reduce"
- type: "Convolution"
- bottom: "pool4/3x3_s2"
- top: "inception_5a/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 160
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5a/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_5a/3x3_reduce"
- top: "inception_5a/3x3_reduce"
-}
-layer {
- name: "inception_5a/3x3"
- type: "Convolution"
- bottom: "inception_5a/3x3_reduce"
- top: "inception_5a/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 320
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5a/relu_3x3"
- type: "ReLU"
- bottom: "inception_5a/3x3"
- top: "inception_5a/3x3"
-}
-layer {
- name: "inception_5a/5x5_reduce"
- type: "Convolution"
- bottom: "pool4/3x3_s2"
- top: "inception_5a/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 32
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5a/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_5a/5x5_reduce"
- top: "inception_5a/5x5_reduce"
-}
-layer {
- name: "inception_5a/5x5"
- type: "Convolution"
- bottom: "inception_5a/5x5_reduce"
- top: "inception_5a/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5a/relu_5x5"
- type: "ReLU"
- bottom: "inception_5a/5x5"
- top: "inception_5a/5x5"
-}
-layer {
- name: "inception_5a/pool"
- type: "Pooling"
- bottom: "pool4/3x3_s2"
- top: "inception_5a/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_5a/pool_proj"
- type: "Convolution"
- bottom: "inception_5a/pool"
- top: "inception_5a/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5a/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_5a/pool_proj"
- top: "inception_5a/pool_proj"
-}
-layer {
- name: "inception_5a/output"
- type: "Concat"
- bottom: "inception_5a/1x1"
- bottom: "inception_5a/3x3"
- bottom: "inception_5a/5x5"
- bottom: "inception_5a/pool_proj"
- top: "inception_5a/output"
-}
-layer {
- name: "inception_5b/1x1"
- type: "Convolution"
- bottom: "inception_5a/output"
- top: "inception_5b/1x1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 384
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5b/relu_1x1"
- type: "ReLU"
- bottom: "inception_5b/1x1"
- top: "inception_5b/1x1"
-}
-layer {
- name: "inception_5b/3x3_reduce"
- type: "Convolution"
- bottom: "inception_5a/output"
- top: "inception_5b/3x3_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 192
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.09
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5b/relu_3x3_reduce"
- type: "ReLU"
- bottom: "inception_5b/3x3_reduce"
- top: "inception_5b/3x3_reduce"
-}
-layer {
- name: "inception_5b/3x3"
- type: "Convolution"
- bottom: "inception_5b/3x3_reduce"
- top: "inception_5b/3x3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 384
- pad: 1
- kernel_size: 3
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5b/relu_3x3"
- type: "ReLU"
- bottom: "inception_5b/3x3"
- top: "inception_5b/3x3"
-}
-layer {
- name: "inception_5b/5x5_reduce"
- type: "Convolution"
- bottom: "inception_5a/output"
- top: "inception_5b/5x5_reduce"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 48
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.2
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5b/relu_5x5_reduce"
- type: "ReLU"
- bottom: "inception_5b/5x5_reduce"
- top: "inception_5b/5x5_reduce"
-}
-layer {
- name: "inception_5b/5x5"
- type: "Convolution"
- bottom: "inception_5b/5x5_reduce"
- top: "inception_5b/5x5"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 2
- kernel_size: 5
- weight_filler {
- type: "xavier"
- std: 0.03
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5b/relu_5x5"
- type: "ReLU"
- bottom: "inception_5b/5x5"
- top: "inception_5b/5x5"
-}
-layer {
- name: "inception_5b/pool"
- type: "Pooling"
- bottom: "inception_5a/output"
- top: "inception_5b/pool"
- pooling_param {
- pool: MAX
- kernel_size: 3
- stride: 1
- pad: 1
- }
-}
-layer {
- name: "inception_5b/pool_proj"
- type: "Convolution"
- bottom: "inception_5b/pool"
- top: "inception_5b/pool_proj"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- kernel_size: 1
- weight_filler {
- type: "xavier"
- std: 0.1
- }
- bias_filler {
- type: "constant"
- value: 0.2
- }
- }
-}
-layer {
- name: "inception_5b/relu_pool_proj"
- type: "ReLU"
- bottom: "inception_5b/pool_proj"
- top: "inception_5b/pool_proj"
-}
-layer {
- name: "inception_5b/output"
- type: "Concat"
- bottom: "inception_5b/1x1"
- bottom: "inception_5b/3x3"
- bottom: "inception_5b/5x5"
- bottom: "inception_5b/pool_proj"
- top: "inception_5b/output"
-}
-layer {
- name: "pool5/7x7_s1"
- type: "Pooling"
- bottom: "inception_5b/output"
- top: "pool5/7x7_s1"
- pooling_param {
- pool: AVE
- kernel_size: 7
- stride: 1
- }
-}
-layer {
- name: "pool5/drop_7x7_s1"
- type: "Dropout"
- bottom: "pool5/7x7_s1"
- top: "pool5/7x7_s1"
- dropout_param {
- dropout_ratio: 0.4
- }
-}
-layer {
- name: "loss3/classifier"
- type: "InnerProduct"
- bottom: "pool5/7x7_s1"
- top: "loss3/classifier"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- inner_product_param {
- num_output: 1000
- weight_filler {
- type: "xavier"
- }
- bias_filler {
- type: "constant"
- value: 0
- }
- }
-}
-layer {
- name: "prob"
- type: "Softmax"
- bottom: "loss3/classifier"
- top: "prob"
-}
+++ /dev/null
-Unlabeled 0 0 0
-Road 128 64 128
-Sidewalk 244 35 232
-Building 70 70 70
-Wall 102 102 156
-Fence 190 153 153
-Pole 153 153 153
-TrafficLight 250 170 30
-TrafficSign 220 220 0
-Vegetation 107 142 35
-Terrain 152 251 152
-Sky 70 130 180
-Person 220 20 60
-Rider 255 0 0
-Car 0 0 142
-Truck 0 0 70
-Bus 0 60 100
-Train 0 80 100
-Motorcycle 0 0 230
-Bicycle 119 11 32
\ No newline at end of file
+++ /dev/null
-#
-# This prototxt is based on voc-fcn32s/val.prototxt file from
-# https://github.com/shelhamer/fcn.berkeleyvision.org, which is distributed under
-# Caffe (BSD) license:
-# http://caffe.berkeleyvision.org/model_zoo.html#bvlc-model-license
-#
-name: "voc-fcn32s"
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 500
-input_dim: 500
-layer {
- name: "conv1_1"
- type: "Convolution"
- bottom: "data"
- top: "conv1_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 100
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu1_1"
- type: "ReLU"
- bottom: "conv1_1"
- top: "conv1_1"
-}
-layer {
- name: "conv1_2"
- type: "Convolution"
- bottom: "conv1_1"
- top: "conv1_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu1_2"
- type: "ReLU"
- bottom: "conv1_2"
- top: "conv1_2"
-}
-layer {
- name: "pool1"
- type: "Pooling"
- bottom: "conv1_2"
- top: "pool1"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv2_1"
- type: "Convolution"
- bottom: "pool1"
- top: "conv2_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu2_1"
- type: "ReLU"
- bottom: "conv2_1"
- top: "conv2_1"
-}
-layer {
- name: "conv2_2"
- type: "Convolution"
- bottom: "conv2_1"
- top: "conv2_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu2_2"
- type: "ReLU"
- bottom: "conv2_2"
- top: "conv2_2"
-}
-layer {
- name: "pool2"
- type: "Pooling"
- bottom: "conv2_2"
- top: "pool2"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv3_1"
- type: "Convolution"
- bottom: "pool2"
- top: "conv3_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu3_1"
- type: "ReLU"
- bottom: "conv3_1"
- top: "conv3_1"
-}
-layer {
- name: "conv3_2"
- type: "Convolution"
- bottom: "conv3_1"
- top: "conv3_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu3_2"
- type: "ReLU"
- bottom: "conv3_2"
- top: "conv3_2"
-}
-layer {
- name: "conv3_3"
- type: "Convolution"
- bottom: "conv3_2"
- top: "conv3_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu3_3"
- type: "ReLU"
- bottom: "conv3_3"
- top: "conv3_3"
-}
-layer {
- name: "pool3"
- type: "Pooling"
- bottom: "conv3_3"
- top: "pool3"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv4_1"
- type: "Convolution"
- bottom: "pool3"
- top: "conv4_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu4_1"
- type: "ReLU"
- bottom: "conv4_1"
- top: "conv4_1"
-}
-layer {
- name: "conv4_2"
- type: "Convolution"
- bottom: "conv4_1"
- top: "conv4_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu4_2"
- type: "ReLU"
- bottom: "conv4_2"
- top: "conv4_2"
-}
-layer {
- name: "conv4_3"
- type: "Convolution"
- bottom: "conv4_2"
- top: "conv4_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu4_3"
- type: "ReLU"
- bottom: "conv4_3"
- top: "conv4_3"
-}
-layer {
- name: "pool4"
- type: "Pooling"
- bottom: "conv4_3"
- top: "pool4"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv5_1"
- type: "Convolution"
- bottom: "pool4"
- top: "conv5_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu5_1"
- type: "ReLU"
- bottom: "conv5_1"
- top: "conv5_1"
-}
-layer {
- name: "conv5_2"
- type: "Convolution"
- bottom: "conv5_1"
- top: "conv5_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu5_2"
- type: "ReLU"
- bottom: "conv5_2"
- top: "conv5_2"
-}
-layer {
- name: "conv5_3"
- type: "Convolution"
- bottom: "conv5_2"
- top: "conv5_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu5_3"
- type: "ReLU"
- bottom: "conv5_3"
- top: "conv5_3"
-}
-layer {
- name: "pool5"
- type: "Pooling"
- bottom: "conv5_3"
- top: "pool5"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "fc6"
- type: "Convolution"
- bottom: "pool5"
- top: "fc6"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 4096
- pad: 0
- kernel_size: 7
- stride: 1
- }
-}
-layer {
- name: "relu6"
- type: "ReLU"
- bottom: "fc6"
- top: "fc6"
-}
-layer {
- name: "fc7"
- type: "Convolution"
- bottom: "fc6"
- top: "fc7"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 4096
- pad: 0
- kernel_size: 1
- stride: 1
- }
-}
-layer {
- name: "relu7"
- type: "ReLU"
- bottom: "fc7"
- top: "fc7"
-}
-layer {
- name: "score_fr"
- type: "Convolution"
- bottom: "fc7"
- top: "score_fr"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 21
- pad: 0
- kernel_size: 1
- }
-}
-layer {
- name: "upscore"
- type: "Deconvolution"
- bottom: "score_fr"
- top: "upscore"
- param {
- lr_mult: 0
- }
- convolution_param {
- num_output: 21
- bias_term: false
- kernel_size: 64
- stride: 32
- }
-}
-layer {
- name: "score"
- type: "Crop"
- bottom: "upscore"
- bottom: "data"
- top: "score"
- crop_param {
- axis: 2
- offset: 19
- }
-}
+++ /dev/null
-#
-# This prototxt is based on voc-fcn8s/val.prototxt file from
-# https://github.com/shelhamer/fcn.berkeleyvision.org, which is distributed under
-# Caffe (BSD) license:
-# http://caffe.berkeleyvision.org/model_zoo.html#bvlc-model-license
-#
-name: "voc-fcn8s"
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 500
-input_dim: 500
-layer {
- name: "conv1_1"
- type: "Convolution"
- bottom: "data"
- top: "conv1_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 100
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu1_1"
- type: "ReLU"
- bottom: "conv1_1"
- top: "conv1_1"
-}
-layer {
- name: "conv1_2"
- type: "Convolution"
- bottom: "conv1_1"
- top: "conv1_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 64
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu1_2"
- type: "ReLU"
- bottom: "conv1_2"
- top: "conv1_2"
-}
-layer {
- name: "pool1"
- type: "Pooling"
- bottom: "conv1_2"
- top: "pool1"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv2_1"
- type: "Convolution"
- bottom: "pool1"
- top: "conv2_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu2_1"
- type: "ReLU"
- bottom: "conv2_1"
- top: "conv2_1"
-}
-layer {
- name: "conv2_2"
- type: "Convolution"
- bottom: "conv2_1"
- top: "conv2_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 128
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu2_2"
- type: "ReLU"
- bottom: "conv2_2"
- top: "conv2_2"
-}
-layer {
- name: "pool2"
- type: "Pooling"
- bottom: "conv2_2"
- top: "pool2"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv3_1"
- type: "Convolution"
- bottom: "pool2"
- top: "conv3_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu3_1"
- type: "ReLU"
- bottom: "conv3_1"
- top: "conv3_1"
-}
-layer {
- name: "conv3_2"
- type: "Convolution"
- bottom: "conv3_1"
- top: "conv3_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu3_2"
- type: "ReLU"
- bottom: "conv3_2"
- top: "conv3_2"
-}
-layer {
- name: "conv3_3"
- type: "Convolution"
- bottom: "conv3_2"
- top: "conv3_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 256
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu3_3"
- type: "ReLU"
- bottom: "conv3_3"
- top: "conv3_3"
-}
-layer {
- name: "pool3"
- type: "Pooling"
- bottom: "conv3_3"
- top: "pool3"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv4_1"
- type: "Convolution"
- bottom: "pool3"
- top: "conv4_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu4_1"
- type: "ReLU"
- bottom: "conv4_1"
- top: "conv4_1"
-}
-layer {
- name: "conv4_2"
- type: "Convolution"
- bottom: "conv4_1"
- top: "conv4_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu4_2"
- type: "ReLU"
- bottom: "conv4_2"
- top: "conv4_2"
-}
-layer {
- name: "conv4_3"
- type: "Convolution"
- bottom: "conv4_2"
- top: "conv4_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu4_3"
- type: "ReLU"
- bottom: "conv4_3"
- top: "conv4_3"
-}
-layer {
- name: "pool4"
- type: "Pooling"
- bottom: "conv4_3"
- top: "pool4"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "conv5_1"
- type: "Convolution"
- bottom: "pool4"
- top: "conv5_1"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu5_1"
- type: "ReLU"
- bottom: "conv5_1"
- top: "conv5_1"
-}
-layer {
- name: "conv5_2"
- type: "Convolution"
- bottom: "conv5_1"
- top: "conv5_2"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu5_2"
- type: "ReLU"
- bottom: "conv5_2"
- top: "conv5_2"
-}
-layer {
- name: "conv5_3"
- type: "Convolution"
- bottom: "conv5_2"
- top: "conv5_3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 512
- pad: 1
- kernel_size: 3
- stride: 1
- }
-}
-layer {
- name: "relu5_3"
- type: "ReLU"
- bottom: "conv5_3"
- top: "conv5_3"
-}
-layer {
- name: "pool5"
- type: "Pooling"
- bottom: "conv5_3"
- top: "pool5"
- pooling_param {
- pool: MAX
- kernel_size: 2
- stride: 2
- }
-}
-layer {
- name: "fc6"
- type: "Convolution"
- bottom: "pool5"
- top: "fc6"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 4096
- pad: 0
- kernel_size: 7
- stride: 1
- }
-}
-layer {
- name: "relu6"
- type: "ReLU"
- bottom: "fc6"
- top: "fc6"
-}
-layer {
- name: "fc7"
- type: "Convolution"
- bottom: "fc6"
- top: "fc7"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 4096
- pad: 0
- kernel_size: 1
- stride: 1
- }
-}
-layer {
- name: "relu7"
- type: "ReLU"
- bottom: "fc7"
- top: "fc7"
-}
-layer {
- name: "score_fr"
- type: "Convolution"
- bottom: "fc7"
- top: "score_fr"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 21
- pad: 0
- kernel_size: 1
- }
-}
-layer {
- name: "upscore2"
- type: "Deconvolution"
- bottom: "score_fr"
- top: "upscore2"
- param {
- lr_mult: 0
- }
- convolution_param {
- num_output: 21
- bias_term: false
- kernel_size: 4
- stride: 2
- }
-}
-layer {
- name: "score_pool4"
- type: "Convolution"
- bottom: "pool4"
- top: "score_pool4"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 21
- pad: 0
- kernel_size: 1
- }
-}
-layer {
- name: "score_pool4c"
- type: "Crop"
- bottom: "score_pool4"
- bottom: "upscore2"
- top: "score_pool4c"
- crop_param {
- axis: 2
- offset: 5
- }
-}
-layer {
- name: "fuse_pool4"
- type: "Eltwise"
- bottom: "upscore2"
- bottom: "score_pool4c"
- top: "fuse_pool4"
- eltwise_param {
- operation: SUM
- }
-}
-layer {
- name: "upscore_pool4"
- type: "Deconvolution"
- bottom: "fuse_pool4"
- top: "upscore_pool4"
- param {
- lr_mult: 0
- }
- convolution_param {
- num_output: 21
- bias_term: false
- kernel_size: 4
- stride: 2
- }
-}
-layer {
- name: "score_pool3"
- type: "Convolution"
- bottom: "pool3"
- top: "score_pool3"
- param {
- lr_mult: 1
- decay_mult: 1
- }
- param {
- lr_mult: 2
- decay_mult: 0
- }
- convolution_param {
- num_output: 21
- pad: 0
- kernel_size: 1
- }
-}
-layer {
- name: "score_pool3c"
- type: "Crop"
- bottom: "score_pool3"
- bottom: "upscore_pool4"
- top: "score_pool3c"
- crop_param {
- axis: 2
- offset: 9
- }
-}
-layer {
- name: "fuse_pool3"
- type: "Eltwise"
- bottom: "upscore_pool4"
- bottom: "score_pool3c"
- top: "fuse_pool3"
- eltwise_param {
- operation: SUM
- }
-}
-layer {
- name: "upscore8"
- type: "Deconvolution"
- bottom: "fuse_pool3"
- top: "upscore8"
- param {
- lr_mult: 0
- }
- convolution_param {
- num_output: 21
- bias_term: false
- kernel_size: 16
- stride: 8
- }
-}
-layer {
- name: "score"
- type: "Crop"
- bottom: "upscore8"
- bottom: "data"
- top: "score"
- crop_param {
- axis: 2
- offset: 31
- }
-}
+++ /dev/null
-background 0 0 0
-aeroplane 128 0 0
-bicycle 0 128 0
-bird 128 128 0
-boat 0 0 128
-bottle 128 0 128
-bus 0 128 128
-car 128 128 128
-cat 64 0 0
-chair 192 0 0
-cow 64 128 0
-diningtable 192 128 0
-dog 64 0 128
-horse 192 0 128
-motorbike 64 128 128
-person 192 128 128
-pottedplant 0 64 0
-sheep 128 64 0
-sofa 0 192 0
-train 128 192 0
-tvmonitor 0 64 128
+++ /dev/null
-n01440764 tench, Tinca tinca
-n01443537 goldfish, Carassius auratus
-n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
-n01491361 tiger shark, Galeocerdo cuvieri
-n01494475 hammerhead, hammerhead shark
-n01496331 electric ray, crampfish, numbfish, torpedo
-n01498041 stingray
-n01514668 cock
-n01514859 hen
-n01518878 ostrich, Struthio camelus
-n01530575 brambling, Fringilla montifringilla
-n01531178 goldfinch, Carduelis carduelis
-n01532829 house finch, linnet, Carpodacus mexicanus
-n01534433 junco, snowbird
-n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea
-n01558993 robin, American robin, Turdus migratorius
-n01560419 bulbul
-n01580077 jay
-n01582220 magpie
-n01592084 chickadee
-n01601694 water ouzel, dipper
-n01608432 kite
-n01614925 bald eagle, American eagle, Haliaeetus leucocephalus
-n01616318 vulture
-n01622779 great grey owl, great gray owl, Strix nebulosa
-n01629819 European fire salamander, Salamandra salamandra
-n01630670 common newt, Triturus vulgaris
-n01631663 eft
-n01632458 spotted salamander, Ambystoma maculatum
-n01632777 axolotl, mud puppy, Ambystoma mexicanum
-n01641577 bullfrog, Rana catesbeiana
-n01644373 tree frog, tree-frog
-n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
-n01664065 loggerhead, loggerhead turtle, Caretta caretta
-n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
-n01667114 mud turtle
-n01667778 terrapin
-n01669191 box turtle, box tortoise
-n01675722 banded gecko
-n01677366 common iguana, iguana, Iguana iguana
-n01682714 American chameleon, anole, Anolis carolinensis
-n01685808 whiptail, whiptail lizard
-n01687978 agama
-n01688243 frilled lizard, Chlamydosaurus kingi
-n01689811 alligator lizard
-n01692333 Gila monster, Heloderma suspectum
-n01693334 green lizard, Lacerta viridis
-n01694178 African chameleon, Chamaeleo chamaeleon
-n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
-n01697457 African crocodile, Nile crocodile, Crocodylus niloticus
-n01698640 American alligator, Alligator mississipiensis
-n01704323 triceratops
-n01728572 thunder snake, worm snake, Carphophis amoenus
-n01728920 ringneck snake, ring-necked snake, ring snake
-n01729322 hognose snake, puff adder, sand viper
-n01729977 green snake, grass snake
-n01734418 king snake, kingsnake
-n01735189 garter snake, grass snake
-n01737021 water snake
-n01739381 vine snake
-n01740131 night snake, Hypsiglena torquata
-n01742172 boa constrictor, Constrictor constrictor
-n01744401 rock python, rock snake, Python sebae
-n01748264 Indian cobra, Naja naja
-n01749939 green mamba
-n01751748 sea snake
-n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
-n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus
-n01756291 sidewinder, horned rattlesnake, Crotalus cerastes
-n01768244 trilobite
-n01770081 harvestman, daddy longlegs, Phalangium opilio
-n01770393 scorpion
-n01773157 black and gold garden spider, Argiope aurantia
-n01773549 barn spider, Araneus cavaticus
-n01773797 garden spider, Aranea diademata
-n01774384 black widow, Latrodectus mactans
-n01774750 tarantula
-n01775062 wolf spider, hunting spider
-n01776313 tick
-n01784675 centipede
-n01795545 black grouse
-n01796340 ptarmigan
-n01797886 ruffed grouse, partridge, Bonasa umbellus
-n01798484 prairie chicken, prairie grouse, prairie fowl
-n01806143 peacock
-n01806567 quail
-n01807496 partridge
-n01817953 African grey, African gray, Psittacus erithacus
-n01818515 macaw
-n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
-n01820546 lorikeet
-n01824575 coucal
-n01828970 bee eater
-n01829413 hornbill
-n01833805 hummingbird
-n01843065 jacamar
-n01843383 toucan
-n01847000 drake
-n01855032 red-breasted merganser, Mergus serrator
-n01855672 goose
-n01860187 black swan, Cygnus atratus
-n01871265 tusker
-n01872401 echidna, spiny anteater, anteater
-n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
-n01877812 wallaby, brush kangaroo
-n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
-n01883070 wombat
-n01910747 jellyfish
-n01914609 sea anemone, anemone
-n01917289 brain coral
-n01924916 flatworm, platyhelminth
-n01930112 nematode, nematode worm, roundworm
-n01943899 conch
-n01944390 snail
-n01945685 slug
-n01950731 sea slug, nudibranch
-n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore
-n01968897 chambered nautilus, pearly nautilus, nautilus
-n01978287 Dungeness crab, Cancer magister
-n01978455 rock crab, Cancer irroratus
-n01980166 fiddler crab
-n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
-n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus
-n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
-n01985128 crayfish, crawfish, crawdad, crawdaddy
-n01986214 hermit crab
-n01990800 isopod
-n02002556 white stork, Ciconia ciconia
-n02002724 black stork, Ciconia nigra
-n02006656 spoonbill
-n02007558 flamingo
-n02009229 little blue heron, Egretta caerulea
-n02009912 American egret, great white heron, Egretta albus
-n02011460 bittern
-n02012849 crane
-n02013706 limpkin, Aramus pictus
-n02017213 European gallinule, Porphyrio porphyrio
-n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana
-n02018795 bustard
-n02025239 ruddy turnstone, Arenaria interpres
-n02027492 red-backed sandpiper, dunlin, Erolia alpina
-n02028035 redshank, Tringa totanus
-n02033041 dowitcher
-n02037110 oystercatcher, oyster catcher
-n02051845 pelican
-n02056570 king penguin, Aptenodytes patagonica
-n02058221 albatross, mollymawk
-n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
-n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
-n02074367 dugong, Dugong dugon
-n02077923 sea lion
-n02085620 Chihuahua
-n02085782 Japanese spaniel
-n02085936 Maltese dog, Maltese terrier, Maltese
-n02086079 Pekinese, Pekingese, Peke
-n02086240 Shih-Tzu
-n02086646 Blenheim spaniel
-n02086910 papillon
-n02087046 toy terrier
-n02087394 Rhodesian ridgeback
-n02088094 Afghan hound, Afghan
-n02088238 basset, basset hound
-n02088364 beagle
-n02088466 bloodhound, sleuthhound
-n02088632 bluetick
-n02089078 black-and-tan coonhound
-n02089867 Walker hound, Walker foxhound
-n02089973 English foxhound
-n02090379 redbone
-n02090622 borzoi, Russian wolfhound
-n02090721 Irish wolfhound
-n02091032 Italian greyhound
-n02091134 whippet
-n02091244 Ibizan hound, Ibizan Podenco
-n02091467 Norwegian elkhound, elkhound
-n02091635 otterhound, otter hound
-n02091831 Saluki, gazelle hound
-n02092002 Scottish deerhound, deerhound
-n02092339 Weimaraner
-n02093256 Staffordshire bullterrier, Staffordshire bull terrier
-n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
-n02093647 Bedlington terrier
-n02093754 Border terrier
-n02093859 Kerry blue terrier
-n02093991 Irish terrier
-n02094114 Norfolk terrier
-n02094258 Norwich terrier
-n02094433 Yorkshire terrier
-n02095314 wire-haired fox terrier
-n02095570 Lakeland terrier
-n02095889 Sealyham terrier, Sealyham
-n02096051 Airedale, Airedale terrier
-n02096177 cairn, cairn terrier
-n02096294 Australian terrier
-n02096437 Dandie Dinmont, Dandie Dinmont terrier
-n02096585 Boston bull, Boston terrier
-n02097047 miniature schnauzer
-n02097130 giant schnauzer
-n02097209 standard schnauzer
-n02097298 Scotch terrier, Scottish terrier, Scottie
-n02097474 Tibetan terrier, chrysanthemum dog
-n02097658 silky terrier, Sydney silky
-n02098105 soft-coated wheaten terrier
-n02098286 West Highland white terrier
-n02098413 Lhasa, Lhasa apso
-n02099267 flat-coated retriever
-n02099429 curly-coated retriever
-n02099601 golden retriever
-n02099712 Labrador retriever
-n02099849 Chesapeake Bay retriever
-n02100236 German short-haired pointer
-n02100583 vizsla, Hungarian pointer
-n02100735 English setter
-n02100877 Irish setter, red setter
-n02101006 Gordon setter
-n02101388 Brittany spaniel
-n02101556 clumber, clumber spaniel
-n02102040 English springer, English springer spaniel
-n02102177 Welsh springer spaniel
-n02102318 cocker spaniel, English cocker spaniel, cocker
-n02102480 Sussex spaniel
-n02102973 Irish water spaniel
-n02104029 kuvasz
-n02104365 schipperke
-n02105056 groenendael
-n02105162 malinois
-n02105251 briard
-n02105412 kelpie
-n02105505 komondor
-n02105641 Old English sheepdog, bobtail
-n02105855 Shetland sheepdog, Shetland sheep dog, Shetland
-n02106030 collie
-n02106166 Border collie
-n02106382 Bouvier des Flandres, Bouviers des Flandres
-n02106550 Rottweiler
-n02106662 German shepherd, German shepherd dog, German police dog, alsatian
-n02107142 Doberman, Doberman pinscher
-n02107312 miniature pinscher
-n02107574 Greater Swiss Mountain dog
-n02107683 Bernese mountain dog
-n02107908 Appenzeller
-n02108000 EntleBucher
-n02108089 boxer
-n02108422 bull mastiff
-n02108551 Tibetan mastiff
-n02108915 French bulldog
-n02109047 Great Dane
-n02109525 Saint Bernard, St Bernard
-n02109961 Eskimo dog, husky
-n02110063 malamute, malemute, Alaskan malamute
-n02110185 Siberian husky
-n02110341 dalmatian, coach dog, carriage dog
-n02110627 affenpinscher, monkey pinscher, monkey dog
-n02110806 basenji
-n02110958 pug, pug-dog
-n02111129 Leonberg
-n02111277 Newfoundland, Newfoundland dog
-n02111500 Great Pyrenees
-n02111889 Samoyed, Samoyede
-n02112018 Pomeranian
-n02112137 chow, chow chow
-n02112350 keeshond
-n02112706 Brabancon griffon
-n02113023 Pembroke, Pembroke Welsh corgi
-n02113186 Cardigan, Cardigan Welsh corgi
-n02113624 toy poodle
-n02113712 miniature poodle
-n02113799 standard poodle
-n02113978 Mexican hairless
-n02114367 timber wolf, grey wolf, gray wolf, Canis lupus
-n02114548 white wolf, Arctic wolf, Canis lupus tundrarum
-n02114712 red wolf, maned wolf, Canis rufus, Canis niger
-n02114855 coyote, prairie wolf, brush wolf, Canis latrans
-n02115641 dingo, warrigal, warragal, Canis dingo
-n02115913 dhole, Cuon alpinus
-n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
-n02117135 hyena, hyaena
-n02119022 red fox, Vulpes vulpes
-n02119789 kit fox, Vulpes macrotis
-n02120079 Arctic fox, white fox, Alopex lagopus
-n02120505 grey fox, gray fox, Urocyon cinereoargenteus
-n02123045 tabby, tabby cat
-n02123159 tiger cat
-n02123394 Persian cat
-n02123597 Siamese cat, Siamese
-n02124075 Egyptian cat
-n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
-n02127052 lynx, catamount
-n02128385 leopard, Panthera pardus
-n02128757 snow leopard, ounce, Panthera uncia
-n02128925 jaguar, panther, Panthera onca, Felis onca
-n02129165 lion, king of beasts, Panthera leo
-n02129604 tiger, Panthera tigris
-n02130308 cheetah, chetah, Acinonyx jubatus
-n02132136 brown bear, bruin, Ursus arctos
-n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus
-n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
-n02134418 sloth bear, Melursus ursinus, Ursus ursinus
-n02137549 mongoose
-n02138441 meerkat, mierkat
-n02165105 tiger beetle
-n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
-n02167151 ground beetle, carabid beetle
-n02168699 long-horned beetle, longicorn, longicorn beetle
-n02169497 leaf beetle, chrysomelid
-n02172182 dung beetle
-n02174001 rhinoceros beetle
-n02177972 weevil
-n02190166 fly
-n02206856 bee
-n02219486 ant, emmet, pismire
-n02226429 grasshopper, hopper
-n02229544 cricket
-n02231487 walking stick, walkingstick, stick insect
-n02233338 cockroach, roach
-n02236044 mantis, mantid
-n02256656 cicada, cicala
-n02259212 leafhopper
-n02264363 lacewing, lacewing fly
-n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
-n02268853 damselfly
-n02276258 admiral
-n02277742 ringlet, ringlet butterfly
-n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
-n02280649 cabbage butterfly
-n02281406 sulphur butterfly, sulfur butterfly
-n02281787 lycaenid, lycaenid butterfly
-n02317335 starfish, sea star
-n02319095 sea urchin
-n02321529 sea cucumber, holothurian
-n02325366 wood rabbit, cottontail, cottontail rabbit
-n02326432 hare
-n02328150 Angora, Angora rabbit
-n02342885 hamster
-n02346627 porcupine, hedgehog
-n02356798 fox squirrel, eastern fox squirrel, Sciurus niger
-n02361337 marmot
-n02363005 beaver
-n02364673 guinea pig, Cavia cobaya
-n02389026 sorrel
-n02391049 zebra
-n02395406 hog, pig, grunter, squealer, Sus scrofa
-n02396427 wild boar, boar, Sus scrofa
-n02397096 warthog
-n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius
-n02403003 ox
-n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
-n02410509 bison
-n02412080 ram, tup
-n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
-n02417914 ibex, Capra ibex
-n02422106 hartebeest
-n02422699 impala, Aepyceros melampus
-n02423022 gazelle
-n02437312 Arabian camel, dromedary, Camelus dromedarius
-n02437616 llama
-n02441942 weasel
-n02442845 mink
-n02443114 polecat, fitch, foulmart, foumart, Mustela putorius
-n02443484 black-footed ferret, ferret, Mustela nigripes
-n02444819 otter
-n02445715 skunk, polecat, wood pussy
-n02447366 badger
-n02454379 armadillo
-n02457408 three-toed sloth, ai, Bradypus tridactylus
-n02480495 orangutan, orang, orangutang, Pongo pygmaeus
-n02480855 gorilla, Gorilla gorilla
-n02481823 chimpanzee, chimp, Pan troglodytes
-n02483362 gibbon, Hylobates lar
-n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus
-n02484975 guenon, guenon monkey
-n02486261 patas, hussar monkey, Erythrocebus patas
-n02486410 baboon
-n02487347 macaque
-n02488291 langur
-n02488702 colobus, colobus monkey
-n02489166 proboscis monkey, Nasalis larvatus
-n02490219 marmoset
-n02492035 capuchin, ringtail, Cebus capucinus
-n02492660 howler monkey, howler
-n02493509 titi, titi monkey
-n02493793 spider monkey, Ateles geoffroyi
-n02494079 squirrel monkey, Saimiri sciureus
-n02497673 Madagascar cat, ring-tailed lemur, Lemur catta
-n02500267 indri, indris, Indri indri, Indri brevicaudatus
-n02504013 Indian elephant, Elephas maximus
-n02504458 African elephant, Loxodonta africana
-n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
-n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
-n02514041 barracouta, snoek
-n02526121 eel
-n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
-n02606052 rock beauty, Holocanthus tricolor
-n02607072 anemone fish
-n02640242 sturgeon
-n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus
-n02643566 lionfish
-n02655020 puffer, pufferfish, blowfish, globefish
-n02666196 abacus
-n02667093 abaya
-n02669723 academic gown, academic robe, judge's robe
-n02672831 accordion, piano accordion, squeeze box
-n02676566 acoustic guitar
-n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier
-n02690373 airliner
-n02692877 airship, dirigible
-n02699494 altar
-n02701002 ambulance
-n02704792 amphibian, amphibious vehicle
-n02708093 analog clock
-n02727426 apiary, bee house
-n02730930 apron
-n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
-n02749479 assault rifle, assault gun
-n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack
-n02776631 bakery, bakeshop, bakehouse
-n02777292 balance beam, beam
-n02782093 balloon
-n02783161 ballpoint, ballpoint pen, ballpen, Biro
-n02786058 Band Aid
-n02787622 banjo
-n02788148 bannister, banister, balustrade, balusters, handrail
-n02790996 barbell
-n02791124 barber chair
-n02791270 barbershop
-n02793495 barn
-n02794156 barometer
-n02795169 barrel, cask
-n02797295 barrow, garden cart, lawn cart, wheelbarrow
-n02799071 baseball
-n02802426 basketball
-n02804414 bassinet
-n02804610 bassoon
-n02807133 bathing cap, swimming cap
-n02808304 bath towel
-n02808440 bathtub, bathing tub, bath, tub
-n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
-n02814860 beacon, lighthouse, beacon light, pharos
-n02815834 beaker
-n02817516 bearskin, busby, shako
-n02823428 beer bottle
-n02823750 beer glass
-n02825657 bell cote, bell cot
-n02834397 bib
-n02835271 bicycle-built-for-two, tandem bicycle, tandem
-n02837789 bikini, two-piece
-n02840245 binder, ring-binder
-n02841315 binoculars, field glasses, opera glasses
-n02843684 birdhouse
-n02859443 boathouse
-n02860847 bobsled, bobsleigh, bob
-n02865351 bolo tie, bolo, bola tie, bola
-n02869837 bonnet, poke bonnet
-n02870880 bookcase
-n02871525 bookshop, bookstore, bookstall
-n02877765 bottlecap
-n02879718 bow
-n02883205 bow tie, bow-tie, bowtie
-n02892201 brass, memorial tablet, plaque
-n02892767 brassiere, bra, bandeau
-n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty
-n02895154 breastplate, aegis, egis
-n02906734 broom
-n02909870 bucket, pail
-n02910353 buckle
-n02916936 bulletproof vest
-n02917067 bullet train, bullet
-n02927161 butcher shop, meat market
-n02930766 cab, hack, taxi, taxicab
-n02939185 caldron, cauldron
-n02948072 candle, taper, wax light
-n02950826 cannon
-n02951358 canoe
-n02951585 can opener, tin opener
-n02963159 cardigan
-n02965783 car mirror
-n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig
-n02966687 carpenter's kit, tool kit
-n02971356 carton
-n02974003 car wheel
-n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
-n02978881 cassette
-n02979186 cassette player
-n02980441 castle
-n02981792 catamaran
-n02988304 CD player
-n02992211 cello, violoncello
-n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone
-n02999410 chain
-n03000134 chainlink fence
-n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
-n03000684 chain saw, chainsaw
-n03014705 chest
-n03016953 chiffonier, commode
-n03017168 chime, bell, gong
-n03018349 china cabinet, china closet
-n03026506 Christmas stocking
-n03028079 church, church building
-n03032252 cinema, movie theater, movie theatre, movie house, picture palace
-n03041632 cleaver, meat cleaver, chopper
-n03042490 cliff dwelling
-n03045698 cloak
-n03047690 clog, geta, patten, sabot
-n03062245 cocktail shaker
-n03063599 coffee mug
-n03063689 coffeepot
-n03065424 coil, spiral, volute, whorl, helix
-n03075370 combination lock
-n03085013 computer keyboard, keypad
-n03089624 confectionery, confectionary, candy store
-n03095699 container ship, containership, container vessel
-n03100240 convertible
-n03109150 corkscrew, bottle screw
-n03110669 cornet, horn, trumpet, trump
-n03124043 cowboy boot
-n03124170 cowboy hat, ten-gallon hat
-n03125729 cradle
-n03126707 crane
-n03127747 crash helmet
-n03127925 crate
-n03131574 crib, cot
-n03133878 Crock Pot
-n03134739 croquet ball
-n03141823 crutch
-n03146219 cuirass
-n03160309 dam, dike, dyke
-n03179701 desk
-n03180011 desktop computer
-n03187595 dial telephone, dial phone
-n03188531 diaper, nappy, napkin
-n03196217 digital clock
-n03197337 digital watch
-n03201208 dining table, board
-n03207743 dishrag, dishcloth
-n03207941 dishwasher, dish washer, dishwashing machine
-n03208938 disk brake, disc brake
-n03216828 dock, dockage, docking facility
-n03218198 dogsled, dog sled, dog sleigh
-n03220513 dome
-n03223299 doormat, welcome mat
-n03240683 drilling platform, offshore rig
-n03249569 drum, membranophone, tympan
-n03250847 drumstick
-n03255030 dumbbell
-n03259280 Dutch oven
-n03271574 electric fan, blower
-n03272010 electric guitar
-n03272562 electric locomotive
-n03290653 entertainment center
-n03291819 envelope
-n03297495 espresso maker
-n03314780 face powder
-n03325584 feather boa, boa
-n03337140 file, file cabinet, filing cabinet
-n03344393 fireboat
-n03345487 fire engine, fire truck
-n03347037 fire screen, fireguard
-n03355925 flagpole, flagstaff
-n03372029 flute, transverse flute
-n03376595 folding chair
-n03379051 football helmet
-n03384352 forklift
-n03388043 fountain
-n03388183 fountain pen
-n03388549 four-poster
-n03393912 freight car
-n03394916 French horn, horn
-n03400231 frying pan, frypan, skillet
-n03404251 fur coat
-n03417042 garbage truck, dustcart
-n03424325 gasmask, respirator, gas helmet
-n03425413 gas pump, gasoline pump, petrol pump, island dispenser
-n03443371 goblet
-n03444034 go-kart
-n03445777 golf ball
-n03445924 golfcart, golf cart
-n03447447 gondola
-n03447721 gong, tam-tam
-n03450230 gown
-n03452741 grand piano, grand
-n03457902 greenhouse, nursery, glasshouse
-n03459775 grille, radiator grille
-n03461385 grocery store, grocery, food market, market
-n03467068 guillotine
-n03476684 hair slide
-n03476991 hair spray
-n03478589 half track
-n03481172 hammer
-n03482405 hamper
-n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier
-n03485407 hand-held computer, hand-held microcomputer
-n03485794 handkerchief, hankie, hanky, hankey
-n03492542 hard disc, hard disk, fixed disk
-n03494278 harmonica, mouth organ, harp, mouth harp
-n03495258 harp
-n03496892 harvester, reaper
-n03498962 hatchet
-n03527444 holster
-n03529860 home theater, home theatre
-n03530642 honeycomb
-n03532672 hook, claw
-n03534580 hoopskirt, crinoline
-n03535780 horizontal bar, high bar
-n03538406 horse cart, horse-cart
-n03544143 hourglass
-n03584254 iPod
-n03584829 iron, smoothing iron
-n03590841 jack-o'-lantern
-n03594734 jean, blue jean, denim
-n03594945 jeep, landrover
-n03595614 jersey, T-shirt, tee shirt
-n03598930 jigsaw puzzle
-n03599486 jinrikisha, ricksha, rickshaw
-n03602883 joystick
-n03617480 kimono
-n03623198 knee pad
-n03627232 knot
-n03630383 lab coat, laboratory coat
-n03633091 ladle
-n03637318 lampshade, lamp shade
-n03642806 laptop, laptop computer
-n03649909 lawn mower, mower
-n03657121 lens cap, lens cover
-n03658185 letter opener, paper knife, paperknife
-n03661043 library
-n03662601 lifeboat
-n03666591 lighter, light, igniter, ignitor
-n03670208 limousine, limo
-n03673027 liner, ocean liner
-n03676483 lipstick, lip rouge
-n03680355 Loafer
-n03690938 lotion
-n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
-n03692522 loupe, jeweler's loupe
-n03697007 lumbermill, sawmill
-n03706229 magnetic compass
-n03709823 mailbag, postbag
-n03710193 mailbox, letter box
-n03710637 maillot
-n03710721 maillot, tank suit
-n03717622 manhole cover
-n03720891 maraca
-n03721384 marimba, xylophone
-n03724870 mask
-n03729826 matchstick
-n03733131 maypole
-n03733281 maze, labyrinth
-n03733805 measuring cup
-n03742115 medicine chest, medicine cabinet
-n03743016 megalith, megalithic structure
-n03759954 microphone, mike
-n03761084 microwave, microwave oven
-n03763968 military uniform
-n03764736 milk can
-n03769881 minibus
-n03770439 miniskirt, mini
-n03770679 minivan
-n03773504 missile
-n03775071 mitten
-n03775546 mixing bowl
-n03776460 mobile home, manufactured home
-n03777568 Model T
-n03777754 modem
-n03781244 monastery
-n03782006 monitor
-n03785016 moped
-n03786901 mortar
-n03787032 mortarboard
-n03788195 mosque
-n03788365 mosquito net
-n03791053 motor scooter, scooter
-n03792782 mountain bike, all-terrain bike, off-roader
-n03792972 mountain tent
-n03793489 mouse, computer mouse
-n03794056 mousetrap
-n03796401 moving van
-n03803284 muzzle
-n03804744 nail
-n03814639 neck brace
-n03814906 necklace
-n03825788 nipple
-n03832673 notebook, notebook computer
-n03837869 obelisk
-n03838899 oboe, hautboy, hautbois
-n03840681 ocarina, sweet potato
-n03841143 odometer, hodometer, mileometer, milometer
-n03843555 oil filter
-n03854065 organ, pipe organ
-n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO
-n03866082 overskirt
-n03868242 oxcart
-n03868863 oxygen mask
-n03871628 packet
-n03873416 paddle, boat paddle
-n03874293 paddlewheel, paddle wheel
-n03874599 padlock
-n03876231 paintbrush
-n03877472 pajama, pyjama, pj's, jammies
-n03877845 palace
-n03884397 panpipe, pandean pipe, syrinx
-n03887697 paper towel
-n03888257 parachute, chute
-n03888605 parallel bars, bars
-n03891251 park bench
-n03891332 parking meter
-n03895866 passenger car, coach, carriage
-n03899768 patio, terrace
-n03902125 pay-phone, pay-station
-n03903868 pedestal, plinth, footstall
-n03908618 pencil box, pencil case
-n03908714 pencil sharpener
-n03916031 perfume, essence
-n03920288 Petri dish
-n03924679 photocopier
-n03929660 pick, plectrum, plectron
-n03929855 pickelhaube
-n03930313 picket fence, paling
-n03930630 pickup, pickup truck
-n03933933 pier
-n03935335 piggy bank, penny bank
-n03937543 pill bottle
-n03938244 pillow
-n03942813 ping-pong ball
-n03944341 pinwheel
-n03947888 pirate, pirate ship
-n03950228 pitcher, ewer
-n03954731 plane, carpenter's plane, woodworking plane
-n03956157 planetarium
-n03958227 plastic bag
-n03961711 plate rack
-n03967562 plow, plough
-n03970156 plunger, plumber's helper
-n03976467 Polaroid camera, Polaroid Land camera
-n03976657 pole
-n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
-n03980874 poncho
-n03982430 pool table, billiard table, snooker table
-n03983396 pop bottle, soda bottle
-n03991062 pot, flowerpot
-n03992509 potter's wheel
-n03995372 power drill
-n03998194 prayer rug, prayer mat
-n04004767 printer
-n04005630 prison, prison house
-n04008634 projectile, missile
-n04009552 projector
-n04019541 puck, hockey puck
-n04023962 punching bag, punch bag, punching ball, punchball
-n04026417 purse
-n04033901 quill, quill pen
-n04033995 quilt, comforter, comfort, puff
-n04037443 racer, race car, racing car
-n04039381 racket, racquet
-n04040759 radiator
-n04041544 radio, wireless
-n04044716 radio telescope, radio reflector
-n04049303 rain barrel
-n04065272 recreational vehicle, RV, R.V.
-n04067472 reel
-n04069434 reflex camera
-n04070727 refrigerator, icebox
-n04074963 remote control, remote
-n04081281 restaurant, eating house, eating place, eatery
-n04086273 revolver, six-gun, six-shooter
-n04090263 rifle
-n04099969 rocking chair, rocker
-n04111531 rotisserie
-n04116512 rubber eraser, rubber, pencil eraser
-n04118538 rugby ball
-n04118776 rule, ruler
-n04120489 running shoe
-n04125021 safe
-n04127249 safety pin
-n04131690 saltshaker, salt shaker
-n04133789 sandal
-n04136333 sarong
-n04141076 sax, saxophone
-n04141327 scabbard
-n04141975 scale, weighing machine
-n04146614 school bus
-n04147183 schooner
-n04149813 scoreboard
-n04152593 screen, CRT screen
-n04153751 screw
-n04154565 screwdriver
-n04162706 seat belt, seatbelt
-n04179913 sewing machine
-n04192698 shield, buckler
-n04200800 shoe shop, shoe-shop, shoe store
-n04201297 shoji
-n04204238 shopping basket
-n04204347 shopping cart
-n04208210 shovel
-n04209133 shower cap
-n04209239 shower curtain
-n04228054 ski
-n04229816 ski mask
-n04235860 sleeping bag
-n04238763 slide rule, slipstick
-n04239074 sliding door
-n04243546 slot, one-armed bandit
-n04251144 snorkel
-n04252077 snowmobile
-n04252225 snowplow, snowplough
-n04254120 soap dispenser
-n04254680 soccer ball
-n04254777 sock
-n04258138 solar dish, solar collector, solar furnace
-n04259630 sombrero
-n04263257 soup bowl
-n04264628 space bar
-n04265275 space heater
-n04266014 space shuttle
-n04270147 spatula
-n04273569 speedboat
-n04275548 spider web, spider's web
-n04277352 spindle
-n04285008 sports car, sport car
-n04286575 spotlight, spot
-n04296562 stage
-n04310018 steam locomotive
-n04311004 steel arch bridge
-n04311174 steel drum
-n04317175 stethoscope
-n04325704 stole
-n04326547 stone wall
-n04328186 stopwatch, stop watch
-n04330267 stove
-n04332243 strainer
-n04335435 streetcar, tram, tramcar, trolley, trolley car
-n04336792 stretcher
-n04344873 studio couch, day bed
-n04346328 stupa, tope
-n04347754 submarine, pigboat, sub, U-boat
-n04350905 suit, suit of clothes
-n04355338 sundial
-n04355933 sunglass
-n04356056 sunglasses, dark glasses, shades
-n04357314 sunscreen, sunblock, sun blocker
-n04366367 suspension bridge
-n04367480 swab, swob, mop
-n04370456 sweatshirt
-n04371430 swimming trunks, bathing trunks
-n04371774 swing
-n04372370 switch, electric switch, electrical switch
-n04376876 syringe
-n04380533 table lamp
-n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle
-n04392985 tape player
-n04398044 teapot
-n04399382 teddy, teddy bear
-n04404412 television, television system
-n04409515 tennis ball
-n04417672 thatch, thatched roof
-n04418357 theater curtain, theatre curtain
-n04423845 thimble
-n04428191 thresher, thrasher, threshing machine
-n04429376 throne
-n04435653 tile roof
-n04442312 toaster
-n04443257 tobacco shop, tobacconist shop, tobacconist
-n04447861 toilet seat
-n04456115 torch
-n04458633 totem pole
-n04461696 tow truck, tow car, wrecker
-n04462240 toyshop
-n04465501 tractor
-n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
-n04476259 tray
-n04479046 trench coat
-n04482393 tricycle, trike, velocipede
-n04483307 trimaran
-n04485082 tripod
-n04486054 triumphal arch
-n04487081 trolleybus, trolley coach, trackless trolley
-n04487394 trombone
-n04493381 tub, vat
-n04501370 turnstile
-n04505470 typewriter keyboard
-n04507155 umbrella
-n04509417 unicycle, monocycle
-n04515003 upright, upright piano
-n04517823 vacuum, vacuum cleaner
-n04522168 vase
-n04523525 vault
-n04525038 velvet
-n04525305 vending machine
-n04532106 vestment
-n04532670 viaduct
-n04536866 violin, fiddle
-n04540053 volleyball
-n04542943 waffle iron
-n04548280 wall clock
-n04548362 wallet, billfold, notecase, pocketbook
-n04550184 wardrobe, closet, press
-n04552348 warplane, military plane
-n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin
-n04554684 washer, automatic washer, washing machine
-n04557648 water bottle
-n04560804 water jug
-n04562935 water tower
-n04579145 whiskey jug
-n04579432 whistle
-n04584207 wig
-n04589890 window screen
-n04590129 window shade
-n04591157 Windsor tie
-n04591713 wine bottle
-n04592741 wing
-n04596742 wok
-n04597913 wooden spoon
-n04599235 wool, woolen, woollen
-n04604644 worm fence, snake fence, snake-rail fence, Virginia fence
-n04606251 wreck
-n04612504 yawl
-n04613696 yurt
-n06359193 web site, website, internet site, site
-n06596364 comic book
-n06785654 crossword puzzle, crossword
-n06794110 street sign
-n06874185 traffic light, traffic signal, stoplight
-n07248320 book jacket, dust cover, dust jacket, dust wrapper
-n07565083 menu
-n07579787 plate
-n07583066 guacamole
-n07584110 consomme
-n07590611 hot pot, hotpot
-n07613480 trifle
-n07614500 ice cream, icecream
-n07615774 ice lolly, lolly, lollipop, popsicle
-n07684084 French loaf
-n07693725 bagel, beigel
-n07695742 pretzel
-n07697313 cheeseburger
-n07697537 hotdog, hot dog, red hot
-n07711569 mashed potato
-n07714571 head cabbage
-n07714990 broccoli
-n07715103 cauliflower
-n07716358 zucchini, courgette
-n07716906 spaghetti squash
-n07717410 acorn squash
-n07717556 butternut squash
-n07718472 cucumber, cuke
-n07718747 artichoke, globe artichoke
-n07720875 bell pepper
-n07730033 cardoon
-n07734744 mushroom
-n07742313 Granny Smith
-n07745940 strawberry
-n07747607 orange
-n07749582 lemon
-n07753113 fig
-n07753275 pineapple, ananas
-n07753592 banana
-n07754684 jackfruit, jak, jack
-n07760859 custard apple
-n07768694 pomegranate
-n07802026 hay
-n07831146 carbonara
-n07836838 chocolate sauce, chocolate syrup
-n07860988 dough
-n07871810 meat loaf, meatloaf
-n07873807 pizza, pizza pie
-n07875152 potpie
-n07880968 burrito
-n07892512 red wine
-n07920052 espresso
-n07930864 cup
-n07932039 eggnog
-n09193705 alp
-n09229709 bubble
-n09246464 cliff, drop, drop-off
-n09256479 coral reef
-n09288635 geyser
-n09332890 lakeside, lakeshore
-n09399592 promontory, headland, head, foreland
-n09421951 sandbar, sand bar
-n09428293 seashore, coast, seacoast, sea-coast
-n09468604 valley, vale
-n09472597 volcano
-n09835506 ballplayer, baseball player
-n10148035 groom, bridegroom
-n10565667 scuba diver
-n11879895 rapeseed
-n11939491 daisy
-n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
-n12144580 corn
-n12267677 acorn
-n12620546 hip, rose hip, rosehip
-n12768682 buckeye, horse chestnut, conker
-n12985857 coral fungus
-n12998815 agaric
-n13037406 gyromitra
-n13040303 stinkhorn, carrion fungus
-n13044778 earthstar
-n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
-n13054560 bolete
-n13133613 ear, spike, capitulum
-n15075141 toilet tissue, toilet paper, bathroom tissue
| [Faster-RCNN](https://github.com/rbgirshick/py-faster-rcnn) | `1.0` | `800x600` | `102.9801, 115.9465, 122.7717` | BGR |
| [R-FCN](https://github.com/YuwenXiong/py-R-FCN) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR |
+
+### Classification
+| Model | Scale | Size WxH| Mean subtraction | Channels order |
+|---------------|-------|-----------|--------------------|-------|
+| GoogLeNet | `1.0` | `224x224` | `104 117 123` | BGR |
+| [SqueezeNet](https://github.com/DeepScale/SqueezeNet) | `1.0` | `227x227` | `0 0 0` | BGR |
+
+
## References
* [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py)
* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
+++ /dev/null
-/**M///////////////////////////////////////////////////////////////////////////////////////
-//
-// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-// By downloading, copying, installing or using the software you agree to this license.
-// If you do not agree to this license, do not download, install,
-// copy or use the software.
-//
-//
-// License Agreement
-// For Open Source Computer Vision Library
-//
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-// * Redistribution's of source code must retain the above copyright notice,
-// this list of conditions and the following disclaimer.
-//
-// * Redistribution's in binary form must reproduce the above copyright notice,
-// this list of conditions and the following disclaimer in the documentation
-// and/or other materials provided with the distribution.
-//
-// * The name of the copyright holders may not be used to endorse or promote products
-// derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-#include <opencv2/core/utils/trace.hpp>
-using namespace cv;
-using namespace cv::dnn;
-
-#include <fstream>
-#include <iostream>
-#include <cstdlib>
-using namespace std;
-
-/* Find best class for the blob (i. e. class with maximal probability) */
-static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
-{
- Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
- Point classNumber;
-
- minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
- *classId = classNumber.x;
-}
-
-static std::vector<String> readClassNames(const char *filename )
-{
- std::vector<String> classNames;
-
- std::ifstream fp(filename);
- if (!fp.is_open())
- {
- std::cerr << "File with classes labels not found: " << filename << std::endl;
- exit(-1);
- }
-
- std::string name;
- while (!fp.eof())
- {
- std::getline(fp, name);
- if (name.length())
- classNames.push_back( name.substr(name.find(' ')+1) );
- }
-
- fp.close();
- return classNames;
-}
-
-const char* params
- = "{ help | false | Sample app for loading googlenet model }"
- "{ proto | bvlc_googlenet.prototxt | model configuration }"
- "{ model | bvlc_googlenet.caffemodel | model weights }"
- "{ label | synset_words.txt | names of ILSVRC2012 classes }"
- "{ image | space_shuttle.jpg | path to image file }"
- "{ opencl | false | enable OpenCL }"
-;
-
-int main(int argc, char **argv)
-{
- CV_TRACE_FUNCTION();
-
- CommandLineParser parser(argc, argv, params);
-
- if (parser.get<bool>("help"))
- {
- parser.printMessage();
- return 0;
- }
-
- String modelTxt = parser.get<string>("proto");
- String modelBin = parser.get<string>("model");
- String imageFile = parser.get<String>("image");
- String classNameFile = parser.get<String>("label");
-
- Net net;
- try {
- //! [Read and initialize network]
- net = dnn::readNetFromCaffe(modelTxt, modelBin);
- //! [Read and initialize network]
- }
- catch (const cv::Exception& e) {
- std::cerr << "Exception: " << e.what() << std::endl;
- //! [Check that network was read successfully]
- if (net.empty())
- {
- std::cerr << "Can't load network by using the following files: " << std::endl;
- std::cerr << "prototxt: " << modelTxt << std::endl;
- std::cerr << "caffemodel: " << modelBin << std::endl;
- std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
- std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
- exit(-1);
- }
- //! [Check that network was read successfully]
- }
-
- if (parser.get<bool>("opencl"))
- {
- net.setPreferableTarget(DNN_TARGET_OPENCL);
- }
-
- //! [Prepare blob]
- Mat img = imread(imageFile);
- if (img.empty())
- {
- std::cerr << "Can't read image from the file: " << imageFile << std::endl;
- exit(-1);
- }
-
- //GoogLeNet accepts only 224x224 BGR-images
- Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224),
- Scalar(104, 117, 123), false); //Convert Mat to batch of images
- //! [Prepare blob]
- net.setInput(inputBlob, "data"); //set the network input
- Mat prob = net.forward("prob"); //compute output
-
- cv::TickMeter t;
- for (int i = 0; i < 10; i++)
- {
- CV_TRACE_REGION("forward");
- //! [Set input blob]
- net.setInput(inputBlob, "data"); //set the network input
- //! [Set input blob]
- t.start();
- //! [Make forward pass]
- prob = net.forward("prob"); //compute output
- //! [Make forward pass]
- t.stop();
- }
-
- //! [Gather output]
- int classId;
- double classProb;
- getMaxClass(prob, &classId, &classProb);//find the best class
- //! [Gather output]
-
- //! [Print results]
- std::vector<String> classNames = readClassNames(classNameFile.c_str());
- std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
- std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
- //! [Print results]
- std::cout << "Time: " << (double)t.getTimeMilli() / t.getCounter() << " ms (average from " << t.getCounter() << " iterations)" << std::endl;
-
- return 0;
-} //main
--- /dev/null
+#include <fstream>
+#include <iostream>
+#include <sstream>
+
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+
+const char* keys =
+ "{ help h | | Print help message. }"
+ "{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+ "{ model m | | Path to a binary file of model contains trained weights. "
+ "It could be a file with extensions .caffemodel (Caffe), "
+ ".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet) }"
+ "{ config c | | Path to a text file of model contains network configuration. "
+ "It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet) }"
+ "{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
+ "{ classes | | Optional path to a text file with names of classes. }"
+ "{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
+ "{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
+ "{ width | -1 | Preprocess input image by resizing to a specific width. }"
+ "{ height | -1 | Preprocess input image by resizing to a specific height. }"
+ "{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
+ "{ backend | 0 | Choose one of computation backends: "
+ "0: default C++ backend, "
+ "1: Halide language (http://halide-lang.org/), "
+ "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
+ "{ target | 0 | Choose one of target computation devices: "
+ "0: CPU target (by default),"
+ "1: OpenCL }";
+
+using namespace cv;
+using namespace dnn;
+
+std::vector<std::string> classes;
+
+Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = "");
+
+int main(int argc, char** argv)
+{
+ CommandLineParser parser(argc, argv, keys);
+ parser.about("Use this script to run classification deep learning networks using OpenCV.");
+ if (argc == 1 || parser.has("help"))
+ {
+ parser.printMessage();
+ return 0;
+ }
+
+ float scale = parser.get<float>("scale");
+ bool swapRB = parser.get<bool>("rgb");
+ int inpWidth = parser.get<int>("width");
+ int inpHeight = parser.get<int>("height");
+
+ // Parse mean values.
+ Scalar mean;
+ if (parser.has("mean"))
+ {
+ std::istringstream meanStr(parser.get<String>("mean"));
+ std::vector<float> meanValues;
+ float val;
+ while (meanStr >> val)
+ meanValues.push_back(val);
+ CV_Assert(meanValues.size() == 3);
+ mean = Scalar(meanValues[0], meanValues[1], meanValues[2]);
+ }
+
+ // Open file with classes names.
+ if (parser.has("classes"))
+ {
+ std::string file = parser.get<String>("classes");
+ std::ifstream ifs(file.c_str());
+ if (!ifs.is_open())
+ CV_Error(Error::StsError, "File " + file + " not found");
+ std::string line;
+ while (std::getline(ifs, line))
+ {
+ classes.push_back(line);
+ }
+ }
+
+ // Load a model.
+ CV_Assert(parser.has("model"));
+ Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
+ net.setPreferableBackend(parser.get<int>("backend"));
+ net.setPreferableTarget(parser.get<int>("target"));
+
+ // Create a window
+ static const std::string kWinName = "Deep learning image classification in OpenCV";
+ namedWindow(kWinName, WINDOW_NORMAL);
+
+ // Open a video file or an image file or a camera stream.
+ VideoCapture cap;
+ if (parser.has("input"))
+ cap.open(parser.get<String>("input"));
+ else
+ cap.open(0);
+
+ // Process frames.
+ Mat frame, blob;
+ while (waitKey(1) < 0)
+ {
+ cap >> frame;
+ if (frame.empty())
+ {
+ waitKey();
+ break;
+ }
+
+ // Create a 4D blob from a frame.
+ blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
+
+ // Run a model.
+ net.setInput(blob);
+ Mat out = net.forward();
+ out = out.reshape(1, 1);
+
+ // Get a class with a highest score.
+ Point classIdPoint;
+ double confidence;
+ minMaxLoc(out, 0, &confidence, 0, &classIdPoint);
+ int classId = classIdPoint.x;
+
+ // Put efficiency information.
+ std::vector<double> layersTimes;
+ double t = net.getPerfProfile(layersTimes);
+ std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency());
+ putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+
+ // Print predicted class.
+ label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() :
+ classes[classId].c_str()),
+ confidence);
+ putText(frame, label, Point(0, 40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
+
+ imshow(kWinName, frame);
+ }
+ return 0;
+}
+
+Net readNet(const std::string& model, const std::string& config, const std::string& framework)
+{
+ std::string modelExt = model.substr(model.rfind('.'));
+ if (framework == "caffe" || modelExt == ".caffemodel")
+ return readNetFromCaffe(config, model);
+ else if (framework == "tensorflow" || modelExt == ".pb")
+ return readNetFromTensorflow(model, config);
+ else if (framework == "torch" || modelExt == ".t7" || modelExt == ".net")
+ return readNetFromTorch(model);
+ else if (framework == "darknet" || modelExt == ".weights")
+ return readNetFromDarknet(config, model);
+ else
+ CV_Error(Error::StsError, "Cannot determine an origin framework of model from file " + model);
+ return Net();
+}
--- /dev/null
+import cv2 as cv
+import argparse
+import numpy as np
+import sys
+
+backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
+targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL)
+
+parser = argparse.ArgumentParser(description='Use this script to run classification deep learning networks using OpenCV.')
+parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
+parser.add_argument('--model', required=True,
+ help='Path to a binary file of model contains trained weights. '
+ 'It could be a file with extensions .caffemodel (Caffe), '
+ '.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet)')
+parser.add_argument('--config',
+ help='Path to a text file of model contains network configuration. '
+ 'It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet)')
+parser.add_argument('--framework', choices=['caffe', 'tensorflow', 'torch', 'darknet'],
+ help='Optional name of an origin framework of the model. '
+ 'Detect it automatically if it does not set.')
+parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
+parser.add_argument('--mean', nargs='+', type=float, default=[0, 0, 0],
+ help='Preprocess input image by subtracting mean values. '
+ 'Mean values should be in BGR order.')
+parser.add_argument('--scale', type=float, default=1.0,
+ help='Preprocess input image by multiplying on a scale factor.')
+parser.add_argument('--width', type=int, required=True,
+ help='Preprocess input image by resizing to a specific width.')
+parser.add_argument('--height', type=int, required=True,
+ help='Preprocess input image by resizing to a specific height.')
+parser.add_argument('--rgb', action='store_true',
+ help='Indicate that model works with RGB input images instead BGR ones.')
+parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
+ help="Choose one of computation backends: "
+ "%d: default C++ backend, "
+ "%d: Halide language (http://halide-lang.org/), "
+ "%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends)
+parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
+ help='Choose one of target computation devices: '
+ '%d: CPU target (by default), '
+ '%d: OpenCL' % targets)
+args = parser.parse_args()
+
+# Load names of classes
+classes = None
+if args.classes:
+ with open(args.classes, 'rt') as f:
+ classes = f.read().rstrip('\n').split('\n')
+
+# Load a network
+modelExt = args.model[args.model.rfind('.'):]
+if args.framework == 'caffe' or modelExt == '.caffemodel':
+ net = cv.dnn.readNetFromCaffe(args.config, args.model)
+elif args.framework == 'tensorflow' or modelExt == '.pb':
+ net = cv.dnn.readNetFromTensorflow(args.model, args.config)
+elif args.framework == 'torch' or modelExt in ['.t7', '.net']:
+ net = cv.dnn.readNetFromTorch(args.model)
+elif args.framework == 'darknet' or modelExt == '.weights':
+ net = cv.dnn.readNetFromDarknet(args.config, args.model)
+else:
+ print('Cannot determine an origin framework of model from file %s' % args.model)
+ sys.exit(0)
+
+net.setPreferableBackend(args.backend)
+net.setPreferableTarget(args.target)
+
+winName = 'Deep learning image classification in OpenCV'
+cv.namedWindow(winName, cv.WINDOW_NORMAL)
+
+cap = cv.VideoCapture(args.input if args.input else 0)
+while cv.waitKey(1) < 0:
+ hasFrame, frame = cap.read()
+ if not hasFrame:
+ cv.waitKey()
+ break
+
+ # Create a 4D blob from a frame.
+ blob = cv.dnn.blobFromImage(frame, args.scale, (args.width, args.height), args.mean, args.rgb, crop=False)
+
+ # Run a model
+ net.setInput(blob)
+ out = net.forward()
+
+ # Get a class with a highest score.
+ out = out.flatten()
+ classId = np.argmax(out)
+ confidence = out[classId]
+
+ # Put efficiency information.
+ t, _ = net.getPerfProfile()
+ label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
+ cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
+
+ # Print predicted class.
+ label = '%s: %.4f' % (classes[classId] if classes else 'Class #%d' % classId, confidence)
+ cv.putText(frame, label, (0, 40), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
+
+ cv.imshow(winName, frame)
--- /dev/null
+tench, Tinca tinca
+goldfish, Carassius auratus
+great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+tiger shark, Galeocerdo cuvieri
+hammerhead, hammerhead shark
+electric ray, crampfish, numbfish, torpedo
+stingray
+cock
+hen
+ostrich, Struthio camelus
+brambling, Fringilla montifringilla
+goldfinch, Carduelis carduelis
+house finch, linnet, Carpodacus mexicanus
+junco, snowbird
+indigo bunting, indigo finch, indigo bird, Passerina cyanea
+robin, American robin, Turdus migratorius
+bulbul
+jay
+magpie
+chickadee
+water ouzel, dipper
+kite
+bald eagle, American eagle, Haliaeetus leucocephalus
+vulture
+great grey owl, great gray owl, Strix nebulosa
+European fire salamander, Salamandra salamandra
+common newt, Triturus vulgaris
+eft
+spotted salamander, Ambystoma maculatum
+axolotl, mud puppy, Ambystoma mexicanum
+bullfrog, Rana catesbeiana
+tree frog, tree-frog
+tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+loggerhead, loggerhead turtle, Caretta caretta
+leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+mud turtle
+terrapin
+box turtle, box tortoise
+banded gecko
+common iguana, iguana, Iguana iguana
+American chameleon, anole, Anolis carolinensis
+whiptail, whiptail lizard
+agama
+frilled lizard, Chlamydosaurus kingi
+alligator lizard
+Gila monster, Heloderma suspectum
+green lizard, Lacerta viridis
+African chameleon, Chamaeleo chamaeleon
+Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+African crocodile, Nile crocodile, Crocodylus niloticus
+American alligator, Alligator mississipiensis
+triceratops
+thunder snake, worm snake, Carphophis amoenus
+ringneck snake, ring-necked snake, ring snake
+hognose snake, puff adder, sand viper
+green snake, grass snake
+king snake, kingsnake
+garter snake, grass snake
+water snake
+vine snake
+night snake, Hypsiglena torquata
+boa constrictor, Constrictor constrictor
+rock python, rock snake, Python sebae
+Indian cobra, Naja naja
+green mamba
+sea snake
+horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+diamondback, diamondback rattlesnake, Crotalus adamanteus
+sidewinder, horned rattlesnake, Crotalus cerastes
+trilobite
+harvestman, daddy longlegs, Phalangium opilio
+scorpion
+black and gold garden spider, Argiope aurantia
+barn spider, Araneus cavaticus
+garden spider, Aranea diademata
+black widow, Latrodectus mactans
+tarantula
+wolf spider, hunting spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse, partridge, Bonasa umbellus
+prairie chicken, prairie grouse, prairie fowl
+peacock
+quail
+partridge
+African grey, African gray, Psittacus erithacus
+macaw
+sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser, Mergus serrator
+goose
+black swan, Cygnus atratus
+tusker
+echidna, spiny anteater, anteater
+platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+wallaby, brush kangaroo
+koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+wombat
+jellyfish
+sea anemone, anemone
+brain coral
+flatworm, platyhelminth
+nematode, nematode worm, roundworm
+conch
+snail
+slug
+sea slug, nudibranch
+chiton, coat-of-mail shell, sea cradle, polyplacophore
+chambered nautilus, pearly nautilus, nautilus
+Dungeness crab, Cancer magister
+rock crab, Cancer irroratus
+fiddler crab
+king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+American lobster, Northern lobster, Maine lobster, Homarus americanus
+spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+crayfish, crawfish, crawdad, crawdaddy
+hermit crab
+isopod
+white stork, Ciconia ciconia
+black stork, Ciconia nigra
+spoonbill
+flamingo
+little blue heron, Egretta caerulea
+American egret, great white heron, Egretta albus
+bittern
+crane
+limpkin, Aramus pictus
+European gallinule, Porphyrio porphyrio
+American coot, marsh hen, mud hen, water hen, Fulica americana
+bustard
+ruddy turnstone, Arenaria interpres
+red-backed sandpiper, dunlin, Erolia alpina
+redshank, Tringa totanus
+dowitcher
+oystercatcher, oyster catcher
+pelican
+king penguin, Aptenodytes patagonica
+albatross, mollymawk
+grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+dugong, Dugong dugon
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog, Maltese terrier, Maltese
+Pekinese, Pekingese, Peke
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound, Afghan
+basset, basset hound
+beagle
+bloodhound, sleuthhound
+bluetick
+black-and-tan coonhound
+Walker hound, Walker foxhound
+English foxhound
+redbone
+borzoi, Russian wolfhound
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound, Ibizan Podenco
+Norwegian elkhound, elkhound
+otterhound, otter hound
+Saluki, gazelle hound
+Scottish deerhound, deerhound
+Weimaraner
+Staffordshire bullterrier, Staffordshire bull terrier
+American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier, Sealyham
+Airedale, Airedale terrier
+cairn, cairn terrier
+Australian terrier
+Dandie Dinmont, Dandie Dinmont terrier
+Boston bull, Boston terrier
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier, Scottish terrier, Scottie
+Tibetan terrier, chrysanthemum dog
+silky terrier, Sydney silky
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa, Lhasa apso
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla, Hungarian pointer
+English setter
+Irish setter, red setter
+Gordon setter
+Brittany spaniel
+clumber, clumber spaniel
+English springer, English springer spaniel
+Welsh springer spaniel
+cocker spaniel, English cocker spaniel, cocker
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog, bobtail
+Shetland sheepdog, Shetland sheep dog, Shetland
+collie
+Border collie
+Bouvier des Flandres, Bouviers des Flandres
+Rottweiler
+German shepherd, German shepherd dog, German police dog, alsatian
+Doberman, Doberman pinscher
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard, St Bernard
+Eskimo dog, husky
+malamute, malemute, Alaskan malamute
+Siberian husky
+dalmatian, coach dog, carriage dog
+affenpinscher, monkey pinscher, monkey dog
+basenji
+pug, pug-dog
+Leonberg
+Newfoundland, Newfoundland dog
+Great Pyrenees
+Samoyed, Samoyede
+Pomeranian
+chow, chow chow
+keeshond
+Brabancon griffon
+Pembroke, Pembroke Welsh corgi
+Cardigan, Cardigan Welsh corgi
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf, grey wolf, gray wolf, Canis lupus
+white wolf, Arctic wolf, Canis lupus tundrarum
+red wolf, maned wolf, Canis rufus, Canis niger
+coyote, prairie wolf, brush wolf, Canis latrans
+dingo, warrigal, warragal, Canis dingo
+dhole, Cuon alpinus
+African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+hyena, hyaena
+red fox, Vulpes vulpes
+kit fox, Vulpes macrotis
+Arctic fox, white fox, Alopex lagopus
+grey fox, gray fox, Urocyon cinereoargenteus
+tabby, tabby cat
+tiger cat
+Persian cat
+Siamese cat, Siamese
+Egyptian cat
+cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+lynx, catamount
+leopard, Panthera pardus
+snow leopard, ounce, Panthera uncia
+jaguar, panther, Panthera onca, Felis onca
+lion, king of beasts, Panthera leo
+tiger, Panthera tigris
+cheetah, chetah, Acinonyx jubatus
+brown bear, bruin, Ursus arctos
+American black bear, black bear, Ursus americanus, Euarctos americanus
+ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+sloth bear, Melursus ursinus, Ursus ursinus
+mongoose
+meerkat, mierkat
+tiger beetle
+ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+ground beetle, carabid beetle
+long-horned beetle, longicorn, longicorn beetle
+leaf beetle, chrysomelid
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant, emmet, pismire
+grasshopper, hopper
+cricket
+walking stick, walkingstick, stick insect
+cockroach, roach
+mantis, mantid
+cicada, cicala
+leafhopper
+lacewing, lacewing fly
+dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+damselfly
+admiral
+ringlet, ringlet butterfly
+monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+cabbage butterfly
+sulphur butterfly, sulfur butterfly
+lycaenid, lycaenid butterfly
+starfish, sea star
+sea urchin
+sea cucumber, holothurian
+wood rabbit, cottontail, cottontail rabbit
+hare
+Angora, Angora rabbit
+hamster
+porcupine, hedgehog
+fox squirrel, eastern fox squirrel, Sciurus niger
+marmot
+beaver
+guinea pig, Cavia cobaya
+sorrel
+zebra
+hog, pig, grunter, squealer, Sus scrofa
+wild boar, boar, Sus scrofa
+warthog
+hippopotamus, hippo, river horse, Hippopotamus amphibius
+ox
+water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+bison
+ram, tup
+bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+ibex, Capra ibex
+hartebeest
+impala, Aepyceros melampus
+gazelle
+Arabian camel, dromedary, Camelus dromedarius
+llama
+weasel
+mink
+polecat, fitch, foulmart, foumart, Mustela putorius
+black-footed ferret, ferret, Mustela nigripes
+otter
+skunk, polecat, wood pussy
+badger
+armadillo
+three-toed sloth, ai, Bradypus tridactylus
+orangutan, orang, orangutang, Pongo pygmaeus
+gorilla, Gorilla gorilla
+chimpanzee, chimp, Pan troglodytes
+gibbon, Hylobates lar
+siamang, Hylobates syndactylus, Symphalangus syndactylus
+guenon, guenon monkey
+patas, hussar monkey, Erythrocebus patas
+baboon
+macaque
+langur
+colobus, colobus monkey
+proboscis monkey, Nasalis larvatus
+marmoset
+capuchin, ringtail, Cebus capucinus
+howler monkey, howler
+titi, titi monkey
+spider monkey, Ateles geoffroyi
+squirrel monkey, Saimiri sciureus
+Madagascar cat, ring-tailed lemur, Lemur catta
+indri, indris, Indri indri, Indri brevicaudatus
+Indian elephant, Elephas maximus
+African elephant, Loxodonta africana
+lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+barracouta, snoek
+eel
+coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+rock beauty, Holocanthus tricolor
+anemone fish
+sturgeon
+gar, garfish, garpike, billfish, Lepisosteus osseus
+lionfish
+puffer, pufferfish, blowfish, globefish
+abacus
+abaya
+academic gown, academic robe, judge's robe
+accordion, piano accordion, squeeze box
+acoustic guitar
+aircraft carrier, carrier, flattop, attack aircraft carrier
+airliner
+airship, dirigible
+altar
+ambulance
+amphibian, amphibious vehicle
+analog clock
+apiary, bee house
+apron
+ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+assault rifle, assault gun
+backpack, back pack, knapsack, packsack, rucksack, haversack
+bakery, bakeshop, bakehouse
+balance beam, beam
+balloon
+ballpoint, ballpoint pen, ballpen, Biro
+Band Aid
+banjo
+bannister, banister, balustrade, balusters, handrail
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel, cask
+barrow, garden cart, lawn cart, wheelbarrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap, swimming cap
+bath towel
+bathtub, bathing tub, bath, tub
+beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+beacon, lighthouse, beacon light, pharos
+beaker
+bearskin, busby, shako
+beer bottle
+beer glass
+bell cote, bell cot
+bib
+bicycle-built-for-two, tandem bicycle, tandem
+bikini, two-piece
+binder, ring-binder
+binoculars, field glasses, opera glasses
+birdhouse
+boathouse
+bobsled, bobsleigh, bob
+bolo tie, bolo, bola tie, bola
+bonnet, poke bonnet
+bookcase
+bookshop, bookstore, bookstall
+bottlecap
+bow
+bow tie, bow-tie, bowtie
+brass, memorial tablet, plaque
+brassiere, bra, bandeau
+breakwater, groin, groyne, mole, bulwark, seawall, jetty
+breastplate, aegis, egis
+broom
+bucket, pail
+buckle
+bulletproof vest
+bullet train, bullet
+butcher shop, meat market
+cab, hack, taxi, taxicab
+caldron, cauldron
+candle, taper, wax light
+cannon
+canoe
+can opener, tin opener
+cardigan
+car mirror
+carousel, carrousel, merry-go-round, roundabout, whirligig
+carpenter's kit, tool kit
+carton
+car wheel
+cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello, violoncello
+cellular telephone, cellular phone, cellphone, cell, mobile phone
+chain
+chainlink fence
+chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+chain saw, chainsaw
+chest
+chiffonier, commode
+chime, bell, gong
+china cabinet, china closet
+Christmas stocking
+church, church building
+cinema, movie theater, movie theatre, movie house, picture palace
+cleaver, meat cleaver, chopper
+cliff dwelling
+cloak
+clog, geta, patten, sabot
+cocktail shaker
+coffee mug
+coffeepot
+coil, spiral, volute, whorl, helix
+combination lock
+computer keyboard, keypad
+confectionery, confectionary, candy store
+container ship, containership, container vessel
+convertible
+corkscrew, bottle screw
+cornet, horn, trumpet, trump
+cowboy boot
+cowboy hat, ten-gallon hat
+cradle
+crane
+crash helmet
+crate
+crib, cot
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam, dike, dyke
+desk
+desktop computer
+dial telephone, dial phone
+diaper, nappy, napkin
+digital clock
+digital watch
+dining table, board
+dishrag, dishcloth
+dishwasher, dish washer, dishwashing machine
+disk brake, disc brake
+dock, dockage, docking facility
+dogsled, dog sled, dog sleigh
+dome
+doormat, welcome mat
+drilling platform, offshore rig
+drum, membranophone, tympan
+drumstick
+dumbbell
+Dutch oven
+electric fan, blower
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa, boa
+file, file cabinet, filing cabinet
+fireboat
+fire engine, fire truck
+fire screen, fireguard
+flagpole, flagstaff
+flute, transverse flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn, horn
+frying pan, frypan, skillet
+fur coat
+garbage truck, dustcart
+gasmask, respirator, gas helmet
+gas pump, gasoline pump, petrol pump, island dispenser
+goblet
+go-kart
+golf ball
+golfcart, golf cart
+gondola
+gong, tam-tam
+gown
+grand piano, grand
+greenhouse, nursery, glasshouse
+grille, radiator grille
+grocery store, grocery, food market, market
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower, blow dryer, blow drier, hair dryer, hair drier
+hand-held computer, hand-held microcomputer
+handkerchief, hankie, hanky, hankey
+hard disc, hard disk, fixed disk
+harmonica, mouth organ, harp, mouth harp
+harp
+harvester, reaper
+hatchet
+holster
+home theater, home theatre
+honeycomb
+hook, claw
+hoopskirt, crinoline
+horizontal bar, high bar
+horse cart, horse-cart
+hourglass
+iPod
+iron, smoothing iron
+jack-o'-lantern
+jean, blue jean, denim
+jeep, landrover
+jersey, T-shirt, tee shirt
+jigsaw puzzle
+jinrikisha, ricksha, rickshaw
+joystick
+kimono
+knee pad
+knot
+lab coat, laboratory coat
+ladle
+lampshade, lamp shade
+laptop, laptop computer
+lawn mower, mower
+lens cap, lens cover
+letter opener, paper knife, paperknife
+library
+lifeboat
+lighter, light, igniter, ignitor
+limousine, limo
+liner, ocean liner
+lipstick, lip rouge
+Loafer
+lotion
+loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+loupe, jeweler's loupe
+lumbermill, sawmill
+magnetic compass
+mailbag, postbag
+mailbox, letter box
+maillot
+maillot, tank suit
+manhole cover
+maraca
+marimba, xylophone
+mask
+matchstick
+maypole
+maze, labyrinth
+measuring cup
+medicine chest, medicine cabinet
+megalith, megalithic structure
+microphone, mike
+microwave, microwave oven
+military uniform
+milk can
+minibus
+miniskirt, mini
+minivan
+missile
+mitten
+mixing bowl
+mobile home, manufactured home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter, scooter
+mountain bike, all-terrain bike, off-roader
+mountain tent
+mouse, computer mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook, notebook computer
+obelisk
+oboe, hautboy, hautbois
+ocarina, sweet potato
+odometer, hodometer, mileometer, milometer
+oil filter
+organ, pipe organ
+oscilloscope, scope, cathode-ray oscilloscope, CRO
+overskirt
+oxcart
+oxygen mask
+packet
+paddle, boat paddle
+paddlewheel, paddle wheel
+padlock
+paintbrush
+pajama, pyjama, pj's, jammies
+palace
+panpipe, pandean pipe, syrinx
+paper towel
+parachute, chute
+parallel bars, bars
+park bench
+parking meter
+passenger car, coach, carriage
+patio, terrace
+pay-phone, pay-station
+pedestal, plinth, footstall
+pencil box, pencil case
+pencil sharpener
+perfume, essence
+Petri dish
+photocopier
+pick, plectrum, plectron
+pickelhaube
+picket fence, paling
+pickup, pickup truck
+pier
+piggy bank, penny bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate, pirate ship
+pitcher, ewer
+plane, carpenter's plane, woodworking plane
+planetarium
+plastic bag
+plate rack
+plow, plough
+plunger, plumber's helper
+Polaroid camera, Polaroid Land camera
+pole
+police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+poncho
+pool table, billiard table, snooker table
+pop bottle, soda bottle
+pot, flowerpot
+potter's wheel
+power drill
+prayer rug, prayer mat
+printer
+prison, prison house
+projectile, missile
+projector
+puck, hockey puck
+punching bag, punch bag, punching ball, punchball
+purse
+quill, quill pen
+quilt, comforter, comfort, puff
+racer, race car, racing car
+racket, racquet
+radiator
+radio, wireless
+radio telescope, radio reflector
+rain barrel
+recreational vehicle, RV, R.V.
+reel
+reflex camera
+refrigerator, icebox
+remote control, remote
+restaurant, eating house, eating place, eatery
+revolver, six-gun, six-shooter
+rifle
+rocking chair, rocker
+rotisserie
+rubber eraser, rubber, pencil eraser
+rugby ball
+rule, ruler
+running shoe
+safe
+safety pin
+saltshaker, salt shaker
+sandal
+sarong
+sax, saxophone
+scabbard
+scale, weighing machine
+school bus
+schooner
+scoreboard
+screen, CRT screen
+screw
+screwdriver
+seat belt, seatbelt
+sewing machine
+shield, buckler
+shoe shop, shoe-shop, shoe store
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule, slipstick
+sliding door
+slot, one-armed bandit
+snorkel
+snowmobile
+snowplow, snowplough
+soap dispenser
+soccer ball
+sock
+solar dish, solar collector, solar furnace
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web, spider's web
+spindle
+sports car, sport car
+spotlight, spot
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch, stop watch
+stove
+strainer
+streetcar, tram, tramcar, trolley, trolley car
+stretcher
+studio couch, day bed
+stupa, tope
+submarine, pigboat, sub, U-boat
+suit, suit of clothes
+sundial
+sunglass
+sunglasses, dark glasses, shades
+sunscreen, sunblock, sun blocker
+suspension bridge
+swab, swob, mop
+sweatshirt
+swimming trunks, bathing trunks
+swing
+switch, electric switch, electrical switch
+syringe
+table lamp
+tank, army tank, armored combat vehicle, armoured combat vehicle
+tape player
+teapot
+teddy, teddy bear
+television, television system
+tennis ball
+thatch, thatched roof
+theater curtain, theatre curtain
+thimble
+thresher, thrasher, threshing machine
+throne
+tile roof
+toaster
+tobacco shop, tobacconist shop, tobacconist
+toilet seat
+torch
+totem pole
+tow truck, tow car, wrecker
+toyshop
+tractor
+trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+tray
+trench coat
+tricycle, trike, velocipede
+trimaran
+tripod
+triumphal arch
+trolleybus, trolley coach, trackless trolley
+trombone
+tub, vat
+turnstile
+typewriter keyboard
+umbrella
+unicycle, monocycle
+upright, upright piano
+vacuum, vacuum cleaner
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin, fiddle
+volleyball
+waffle iron
+wall clock
+wallet, billfold, notecase, pocketbook
+wardrobe, closet, press
+warplane, military plane
+washbasin, handbasin, washbowl, lavabo, wash-hand basin
+washer, automatic washer, washing machine
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool, woolen, woollen
+worm fence, snake fence, snake-rail fence, Virginia fence
+wreck
+yawl
+yurt
+web site, website, internet site, site
+comic book
+crossword puzzle, crossword
+street sign
+traffic light, traffic signal, stoplight
+book jacket, dust cover, dust jacket, dust wrapper
+menu
+plate
+guacamole
+consomme
+hot pot, hotpot
+trifle
+ice cream, icecream
+ice lolly, lolly, lollipop, popsicle
+French loaf
+bagel, beigel
+pretzel
+cheeseburger
+hotdog, hot dog, red hot
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini, courgette
+spaghetti squash
+acorn squash
+butternut squash
+cucumber, cuke
+artichoke, globe artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple, ananas
+banana
+jackfruit, jak, jack
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce, chocolate syrup
+dough
+meat loaf, meatloaf
+pizza, pizza pie
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff, drop, drop-off
+coral reef
+geyser
+lakeside, lakeshore
+promontory, headland, head, foreland
+sandbar, sand bar
+seashore, coast, seacoast, sea-coast
+valley, vale
+volcano
+ballplayer, baseball player
+groom, bridegroom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+corn
+acorn
+hip, rose hip, rosehip
+buckeye, horse chestnut, conker
+coral fungus
+agaric
+gyromitra
+stinkhorn, carrion fungus
+earthstar
+hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+bolete
+ear, spike, capitulum
+toilet tissue, toilet paper, bathroom tissue
+++ /dev/null
-from __future__ import print_function
-import numpy as np
-import cv2 as cv
-from cv2 import dnn
-import timeit
-
-def timeit_forward(net):
- print("Runtime:", timeit.timeit(lambda: net.forward(), number=10))
-
-def get_class_list():
- with open('synset_words.txt', 'rt') as f:
- return [x[x.find(" ") + 1:] for x in f]
-
-blob = dnn.blobFromImage(cv.imread('space_shuttle.jpg'), 1, (224, 224), (104, 117, 123), False)
-print("Input:", blob.shape, blob.dtype)
-
-net = dnn.readNetFromCaffe('bvlc_googlenet.prototxt', 'bvlc_googlenet.caffemodel')
-net.setInput(blob)
-prob = net.forward()
-#timeit_forward(net) #Uncomment to check performance
-
-print("Output:", prob.shape, prob.dtype)
-classes = get_class_list()
-print("Best match", classes[prob.argmax()])
\ No newline at end of file
-#include <opencv2/opencv.hpp>
#include <fstream>
#include <iostream>
#include <sstream>
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+
const char* keys =
"{ help h | | Print help message. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ height | -1 | Preprocess input image by resizing to a specific height. }"
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
"{ thr | .5 | Confidence threshold. }"
- "{ opencl | | Enable OpenCL }";
+ "{ backend | 0 | Choose one of computation backends: "
+ "0: default C++ backend, "
+ "1: Halide language (http://halide-lang.org/), "
+ "2: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)}"
+ "{ target | 0 | Choose one of target computation devices: "
+ "0: CPU target (by default),"
+ "1: OpenCL }";
using namespace cv;
using namespace dnn;
float confThreshold;
std::vector<std::string> classes;
-void loadClasses(const std::string& file);
-
Net readNet(const std::string& model, const std::string& config = "", const std::string& framework = "");
void postprocess(Mat& frame, const Mat& out, Net& net);
if (!ifs.is_open())
CV_Error(Error::StsError, "File " + file + " not found");
std::string line;
- while (ifs >> line)
+ while (std::getline(ifs, line))
{
classes.push_back(line);
}
// Load a model.
CV_Assert(parser.has("model"));
Net net = readNet(parser.get<String>("model"), parser.get<String>("config"), parser.get<String>("framework"));
-
- if (parser.get<bool>("opencl"))
- {
- net.setPreferableTarget(DNN_TARGET_OPENCL);
- }
+ net.setPreferableBackend(parser.get<int>("backend"));
+ net.setPreferableTarget(parser.get<int>("target"));
// Create a window
static const std::string kWinName = "Deep learning object detection in OpenCV";
namedWindow(kWinName, WINDOW_NORMAL);
int initialConf = confThreshold * 100;
- createTrackbar("Confidence threshold", kWinName, &initialConf, 99, callback);
+ createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
// Open a video file or an image file or a camera stream.
VideoCapture cap;
std::vector<double> layersTimes;
double t = net.getPerfProfile(layersTimes);
std::string label = format("Inference time: %.2f", t * 1000 / getTickFrequency());
- putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
+ putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
imshow(kWinName, frame);
}
Net readNet(const std::string& model, const std::string& config, const std::string& framework)
{
- std::string modelExt = model.substr(model.find('.'));
+ std::string modelExt = model.substr(model.rfind('.'));
if (framework == "caffe" || modelExt == ".caffemodel")
return readNetFromCaffe(config, model);
else if (framework == "tensorflow" || modelExt == ".pb")
import sys
import numpy as np
+backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
+targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL)
+
parser = argparse.ArgumentParser(description='Use this script to run object detection deep learning networks using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
parser.add_argument('--model', required=True,
parser.add_argument('--rgb', action='store_true',
help='Indicate that model works with RGB input images instead BGR ones.')
parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
+parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
+ help="Choose one of computation backends: "
+ "%d: default C++ backend, "
+ "%d: Halide language (http://halide-lang.org/), "
+ "%d: Intel's Deep Learning Inference Engine (https://software.seek.intel.com/deep-learning-deployment)" % backends)
+parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
+ help='Choose one of target computation devices: '
+ '%d: CPU target (by default), '
+ '%d: OpenCL' % targets)
args = parser.parse_args()
# Load names of classes
classes = f.read().rstrip('\n').split('\n')
# Load a network
-modelExt = args.model[args.model.find('.'):]
+modelExt = args.model[args.model.rfind('.'):]
if args.framework == 'caffe' or modelExt == '.caffemodel':
net = cv.dnn.readNetFromCaffe(args.config, args.model)
elif args.framework == 'tensorflow' or modelExt == '.pb':
print('Cannot determine an origin framework of model from file %s' % args.model)
sys.exit(0)
+net.setPreferableBackend(args.backend)
+net.setPreferableTarget(args.target)
+
confThreshold = args.thr
def postprocess(frame, out):
# Put efficiency information.
t, _ = net.getPerfProfile()
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
- cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
+ cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
cv.imshow(winName, frame)
+++ /dev/null
-// Sample of using Halide backend in OpenCV deep learning module.
-// Based on caffe_googlenet.cpp.
-
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-using namespace cv;
-using namespace cv::dnn;
-
-#include <fstream>
-#include <iostream>
-#include <cstdlib>
-
-/* Find best class for the blob (i. e. class with maximal probability) */
-static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
-{
- Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
- Point classNumber;
-
- minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
- *classId = classNumber.x;
-}
-
-static std::vector<std::string> readClassNames(const char *filename = "synset_words.txt")
-{
- std::vector<std::string> classNames;
-
- std::ifstream fp(filename);
- if (!fp.is_open())
- {
- std::cerr << "File with classes labels not found: " << filename << std::endl;
- exit(-1);
- }
-
- std::string name;
- while (!fp.eof())
- {
- std::getline(fp, name);
- if (name.length())
- classNames.push_back( name.substr(name.find(' ')+1) );
- }
-
- fp.close();
- return classNames;
-}
-
-int main(int argc, char **argv)
-{
- std::string modelTxt = "train_val.prototxt";
- std::string modelBin = "squeezenet_v1.1.caffemodel";
- std::string imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
-
- //! [Read and initialize network]
- Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
- //! [Read and initialize network]
-
- //! [Check that network was read successfully]
- if (net.empty())
- {
- std::cerr << "Can't load network by using the following files: " << std::endl;
- std::cerr << "prototxt: " << modelTxt << std::endl;
- std::cerr << "caffemodel: " << modelBin << std::endl;
- std::cerr << "SqueezeNet v1.1 can be downloaded from:" << std::endl;
- std::cerr << "https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1" << std::endl;
- exit(-1);
- }
- //! [Check that network was read successfully]
-
- //! [Prepare blob]
- Mat img = imread(imageFile);
- if (img.empty())
- {
- std::cerr << "Can't read image from the file: " << imageFile << std::endl;
- exit(-1);
- }
- if (img.channels() != 3)
- {
- std::cerr << "Image " << imageFile << " isn't 3-channel" << std::endl;
- exit(-1);
- }
-
- Mat inputBlob = blobFromImage(img, 1.0, Size(227, 227), Scalar(), false, false); // Convert Mat to 4-dimensional batch.
- //! [Prepare blob]
-
- //! [Set input blob]
- net.setInput(inputBlob); // Set the network input.
- //! [Set input blob]
-
- //! [Enable Halide backend]
- net.setPreferableBackend(DNN_BACKEND_HALIDE); // Tell engine to use Halide where it possible.
- //! [Enable Halide backend]
-
- //! [Make forward pass]
- Mat prob = net.forward("prob"); // Compute output.
- //! [Make forward pass]
-
- //! [Determine the best class]
- int classId;
- double classProb;
- getMaxClass(prob, &classId, &classProb); // Find the best class.
- //! [Determine the best class]
-
- //! [Print results]
- std::vector<std::string> classNames = readClassNames();
- std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
- std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
- //! [Print results]
-
- return 0;
-} //main
+++ /dev/null
-// This file is part of OpenCV project.
-// It is subject to the license terms in the LICENSE file found in the top-level directory
-// of this distribution and at http://opencv.org/license.html.
-
-// Copyright (C) 2016, Intel Corporation, all rights reserved.
-// Third party copyrights are property of their respective owners.
-
-/*
-Sample of using OpenCV dnn module with Tensorflow Inception model.
-*/
-
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/highgui.hpp>
-using namespace cv;
-using namespace cv::dnn;
-
-#include <fstream>
-#include <iostream>
-#include <cstdlib>
-using namespace std;
-
-const String keys =
- "{help h || Sample app for loading Inception TensorFlow model. "
- "The model and class names list can be downloaded here: "
- "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip }"
- "{model m |tensorflow_inception_graph.pb| path to TensorFlow .pb model file }"
- "{image i || path to image file }"
- "{i_blob | input | input blob name) }"
- "{o_blob | softmax2 | output blob name) }"
- "{c_names c | imagenet_comp_graph_label_strings.txt | path to file with classnames for class id }"
- "{result r || path to save output blob (optional, binary format, NCHW order) }"
- ;
-
-void getMaxClass(const Mat &probBlob, int *classId, double *classProb);
-std::vector<String> readClassNames(const char *filename);
-
-int main(int argc, char **argv)
-{
- cv::CommandLineParser parser(argc, argv, keys);
-
- if (parser.has("help"))
- {
- parser.printMessage();
- return 0;
- }
-
- String modelFile = parser.get<String>("model");
- String imageFile = parser.get<String>("image");
- String inBlobName = parser.get<String>("i_blob");
- String outBlobName = parser.get<String>("o_blob");
-
- if (!parser.check())
- {
- parser.printErrors();
- return 0;
- }
-
- String classNamesFile = parser.get<String>("c_names");
- String resultFile = parser.get<String>("result");
-
- //! [Initialize network]
- dnn::Net net = readNetFromTensorflow(modelFile);
- //! [Initialize network]
-
- if (net.empty())
- {
- std::cerr << "Can't load network by using the mode file: " << std::endl;
- std::cerr << modelFile << std::endl;
- exit(-1);
- }
-
- //! [Prepare blob]
- Mat img = imread(imageFile);
- if (img.empty())
- {
- std::cerr << "Can't read image from the file: " << imageFile << std::endl;
- exit(-1);
- }
-
- Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224), Scalar(), true, false); //Convert Mat to batch of images
- //! [Prepare blob]
- inputBlob -= 117.0;
- //! [Set input blob]
- net.setInput(inputBlob, inBlobName); //set the network input
- //! [Set input blob]
-
- cv::TickMeter tm;
- tm.start();
-
- //! [Make forward pass]
- Mat result = net.forward(outBlobName); //compute output
- //! [Make forward pass]
-
- tm.stop();
-
- if (!resultFile.empty()) {
- CV_Assert(result.isContinuous());
-
- ofstream fout(resultFile.c_str(), ios::out | ios::binary);
- fout.write((char*)result.data, result.total() * sizeof(float));
- fout.close();
- }
-
- std::cout << "Output blob shape " << result.size[0] << " x " << result.size[1] << " x " << result.size[2] << " x " << result.size[3] << std::endl;
- std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl;
-
- if (!classNamesFile.empty()) {
- std::vector<String> classNames = readClassNames(classNamesFile.c_str());
-
- int classId;
- double classProb;
- getMaxClass(result, &classId, &classProb);//find the best class
-
- //! [Print results]
- std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
- std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
- }
- return 0;
-} //main
-
-
-/* Find best class for the blob (i. e. class with maximal probability) */
-void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
-{
- Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
- Point classNumber;
-
- minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
- *classId = classNumber.x;
-}
-
-std::vector<String> readClassNames(const char *filename)
-{
- std::vector<String> classNames;
-
- std::ifstream fp(filename);
- if (!fp.is_open())
- {
- std::cerr << "File with classes labels not found: " << filename << std::endl;
- exit(-1);
- }
-
- std::string name;
- while (!fp.eof())
- {
- std::getline(fp, name);
- if (name.length())
- classNames.push_back( name );
- }
-
- fp.close();
- return classNames;
-}
projects / platform / upstream / opencv.git / commitdiff
