Created
April 22, 2020 02:13
-
-
Save siahuat0727/ccd6641c3791649527686f2fda4f7bb8 to your computer and use it in GitHub Desktop.
traffic light recognition (vertical)
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: "traffic light recognition (vertical)" | |
| layer{ | |
| name: "input" | |
| type: "Input" | |
| top: "data_org" | |
| input_param{ | |
| shape { | |
| dim:1 | |
| dim:96 | |
| dim:32 | |
| dim:3 | |
| } | |
| } | |
| } | |
| layer { | |
| type: "Permute" | |
| name: "permute" | |
| bottom: "data_org" | |
| top: "data" | |
| permute_param{ | |
| order: 0 | |
| order: 3 | |
| order: 1 | |
| order: 2 | |
| } | |
| } | |
| #layer { | |
| # name: "distort" | |
| # type: "ImageDistort" | |
| # bottom: "data_org" | |
| # top: "data" | |
| # image_distort_param { | |
| # new_scale: 0.01 | |
| # new_mean_value: 69.06 | |
| # new_mean_value: 66.58 | |
| # new_mean_value: 66.56 | |
| # } | |
| #} | |
| layer{ | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param{ | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| dilation: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer{ | |
| type: "BatchNorm" | |
| name: "conv1_bn" | |
| bottom: "conv1" | |
| top: "conv1" | |
| batch_norm_param{ | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| type: "Scale" | |
| name: "conv1_bn_scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| scale_param { | |
| axis: 1 | |
| num_axes: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer{ | |
| type: "ReLU" | |
| name: "conv1_relu" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_w: 3 | |
| kernel_h: 3 | |
| stride_w: 2 | |
| stride_h: 2 | |
| pad_w: 1 | |
| pad_h: 1 | |
| round_mode: 1 | |
| } | |
| } | |
| layer{ | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param{ | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| dilation: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer{ | |
| type: "BatchNorm" | |
| name: "conv2_bn" | |
| bottom: "conv2" | |
| top: "conv2" | |
| batch_norm_param{ | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| type: "Scale" | |
| name: "conv2_bn_scale" | |
| bottom: "conv2" | |
| top: "conv2" | |
| scale_param { | |
| axis: 1 | |
| num_axes: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer{ | |
| type: "ReLU" | |
| name: "conv2_relu" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_w: 3 | |
| kernel_h: 3 | |
| stride_w: 2 | |
| stride_h: 2 | |
| pad_w: 1 | |
| pad_h: 1 | |
| round_mode: 1 | |
| } | |
| } | |
| layer{ | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param{ | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| dilation: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer{ | |
| type: "BatchNorm" | |
| name: "conv3_bn" | |
| bottom: "conv3" | |
| top: "conv3" | |
| batch_norm_param{ | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| type: "Scale" | |
| name: "conv3_bn_scale" | |
| bottom: "conv3" | |
| top: "conv3" | |
| scale_param { | |
| axis: 1 | |
| num_axes: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer{ | |
| type: "ReLU" | |
| name: "conv3_relu" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_w: 3 | |
| kernel_h: 3 | |
| stride_w: 2 | |
| stride_h: 2 | |
| pad_w: 1 | |
| pad_h: 1 | |
| round_mode: 1 | |
| } | |
| } | |
| layer{ | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4" | |
| param{ | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| dilation: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer{ | |
| type: "BatchNorm" | |
| name: "conv4_bn" | |
| bottom: "conv4" | |
| top: "conv4" | |
| batch_norm_param{ | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| type: "Scale" | |
| name: "conv4_bn_scale" | |
| bottom: "conv4" | |
| top: "conv4" | |
| scale_param { | |
| axis: 1 | |
| num_axes: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer{ | |
| type: "ReLU" | |
| name: "conv4_relu" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_w: 3 | |
| kernel_h: 3 | |
| stride_w: 2 | |
| stride_h: 2 | |
| pad_w: 1 | |
| pad_h: 1 | |
| round_mode: 1 | |
| } | |
| } | |
| layer{ | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "pool4" | |
| top: "conv5" | |
| param{ | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| pad: 1 | |
| stride: 1 | |
| dilation: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer{ | |
| type: "BatchNorm" | |
| name: "conv5_bn" | |
| bottom: "conv5" | |
| top: "conv5" | |
| batch_norm_param{ | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| type: "Scale" | |
| name: "conv5_bn_scale" | |
| bottom: "conv5" | |
| top: "conv5" | |
| scale_param { | |
| axis: 1 | |
| num_axes: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer{ | |
| type: "ReLU" | |
| name: "conv5_relu" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "pool5" | |
| type: "Pooling" | |
| bottom: "conv5" | |
| top: "pool5" | |
| pooling_param { | |
| pool: AVE | |
| kernel_w: 2 | |
| kernel_h: 6 | |
| stride_w: 2 | |
| stride_h: 6 | |
| round_mode: 1 | |
| } | |
| } | |
| layer { | |
| name: "ft" | |
| type: "InnerProduct" | |
| bottom: "pool5" | |
| top: "ft" | |
| param { | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| inner_product_param { | |
| num_output: 128 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer{ | |
| type: "BatchNorm" | |
| name: "ft_bn" | |
| bottom: "ft" | |
| top: "ft" | |
| batch_norm_param{ | |
| use_global_stats: true | |
| } | |
| } | |
| layer { | |
| type: "Scale" | |
| name: "ft_bn_scale" | |
| bottom: "ft" | |
| top: "ft" | |
| scale_param { | |
| axis: 1 | |
| num_axes: 1 | |
| bias_term: false | |
| } | |
| } | |
| layer{ | |
| type: "ReLU" | |
| name: "ft_relu" | |
| bottom: "ft" | |
| top: "ft" | |
| } | |
| layer { | |
| name: "logits" | |
| type: "InnerProduct" | |
| bottom: "ft" | |
| top: "logits" | |
| param { | |
| lr_mult: 1.000000 | |
| decay_mult: 1.000000 | |
| } | |
| param { | |
| lr_mult: 2.000000 | |
| decay_mult: 0.000000 | |
| } | |
| inner_product_param { | |
| num_output: 4 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.000000 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "prob" | |
| type: "Softmax" | |
| bottom: "logits" | |
| top: "prob" | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment