用ImageNet的数据集训练SSD(Single Shot MultiBox Detector)
来源：互联网 发布：吉林求实软件视频教程 编辑：程序博客网 时间：2024/06/11 14:27
- 简介
- 资源
- Code: https://github.com/weiliu89/caffe
- Paper: http://arxiv.org/abs/1512.02325
- 和Fast(er) R-CNN(python版)的代码结构不是一种风格，SSD构建的工作环境使得的文件组织略显复杂
- 介绍主要文件夹的用途
- examples/ssd/，存放了训练，测试的脚本，是主要操作的脚本
- data/，存放了训练、验证和测试所需的数据和代码
- python/caffe/model_libs.py，存放了生成网络主体的代码
- models/，存放了Proto文件，是由 examples/ssd/中的脚本生成
- jobs/，存放了训练、测试文件，是由 examples/ssd/中的脚本生成
- 按照源码中的安装教程，设备合适的情况下，是可以很顺利的安装并运行的
- 资源
- 按照源码中的安装教程，先用明白VOC数据集
- 开始
更换数据集篇(请看更新)- 阅读data/ILSVRC2016中的README.md
- 按照SSD的data/ILSVRC2016中的create_list.py和create_data.sh代码来组织数据结构
- SSD不支持单通道图片的训练(ILSVRC2014的训练集中存在单通道图片)
- 方案一：通过代码修改(matlab仅供参考)或删除。
- 方案二：配置force_color(推荐)。
- 更换网络篇，以AlexNet为例
- 方案一：
- 按照SSD工程的组织结构，需要在python/caffe/model_libs.py中将AlexNet的Body用Python语言写好。
- 删除example/ssd/ssd_pascal.py中的AddExtraLayers的函数。
- 修改example/ssd/ssd_pascal.py中的参数。
- 方案二：（本人采用的）
- 将example/ssd/ssd_pascal.py中，生成train.prototxt、test.prototxt、solver.prototxt的代码片全部删除。只留下对/models文件的复制逻辑
shutil.copy(test_net_file, job_dir) - 调整、删除或添加example/ssd/ssd_pascal.py中相关参数或变量
- 在/models中创建AlexNet的路径以存放train.prototxt、test.prototxt、solver.prototxt
- train.prototxt仅供参考
- test.prototxt仅供参考
- solver.prototxt参考VGGNet文件夹中的
- 将example/ssd/ssd_pascal.py中，生成train.prototxt、test.prototxt、solver.prototxt的代码片全部删除。只留下对/models文件的复制逻辑
- 方案一：
2016年12月5日
Github上的SSD更新了ssd_ilsvrc.py，所以直接用ILSVRC（2016）的数据集啦。
function [ x ] = gray2rgb( path ) I = imread(path); if(length(size(I))==2) III(:,:,1) = I; III(:,:,2) = I; III(:,:,3) = I; imwrite(III,path); x = '1'; else x = '0'; endend
name: "AlexNet"layer{ name: "data" type: "AnnotatedData" top: "data" top: "label" include{ phase: TRAIN } transform_param{ mirror: true mean_value: 104 mean_value: 117 mean_value: 123 resize_param{ prob: 1 resize_mode: WARP height: 300 width: 300 interp_mode: LINEAR interp_mode: AREA interp_mode: NEAREST interp_mode: CUBIC interp_mode: LANCZOS4 } emit_constraint{ emit_type: CENTER } } data_param{ source: "data/VOCdevkit/VOC0712/lmdb/VOC0712_trainval_lmdb" batch_size: 16 backend: LMDB } annotated_data_param{ batch_sampler{ max_sample: 1 max_trials: 1 } batch_sampler{ sampler{ min_scale: 0.3 max_scale: 1.0 min_aspect_ratio: 0.5 max_aspect_ratio: 2.0 } sample_constraint{ min_jaccard_overlap: 0.1 } max_sample: 1 max_trials: 50 } batch_sampler{ sampler{ min_scale: 0.3 max_scale: 1.0 min_aspect_ratio: 0.5 max_aspect_ratio: 2.0 } sample_constraint{ min_jaccard_overlap: 0.3 } max_sample: 1 max_trials: 50 } batch_sampler{ sampler{ min_scale: 0.3 max_scale: 1.0 min_aspect_ratio: 0.5 max_aspect_ratio: 2.0 } sample_constraint{ min_jaccard_overlap: 0.5 } max_sample: 1 max_trials: 50 } batch_sampler{ sampler{ min_scale: 0.3 max_scale: 1.0 min_aspect_ratio: 0.5 max_aspect_ratio: 2.0 } sample_constraint{ min_jaccard_overlap: 0.7 } max_sample: 1 max_trials: 50 } batch_sampler{ sampler{ min_scale: 0.3 max_scale: 1.0 min_aspect_ratio: 0.5 max_aspect_ratio: 2.0 } sample_constraint{ min_jaccard_overlap: 0.9 } max_sample: 1 max_trials: 50 } batch_sampler{ sampler{ min_scale: 0.3 max_scale: 1.0 min_aspect_ratio: 0.5 max_aspect_ratio: 2.0 } sample_constraint{ max_jaccard_overlap: 1.0 } max_sample: 1 max_trials: 50 } label_map_file: "caffe-ssd/data/VOC0712/labelmap_voc.prototxt" }}layer{ name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 96 kernel_size: 11 stride: 4 pad: 5 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0 } }}layer{ name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"}layer{ name: "norm1" type: "LRN" bottom: "conv1" top: "norm1" lrn_param{ local_size: 5 alpha: 0.0001 beta: 0.75 }}layer{ name: "pool1" type: "Pooling" bottom: "norm1" top: "pool1" pooling_param{ pool: MAX kernel_size: 3 stride: 2 pad: 1 }}layer{ name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 256 pad: 2 group: 2 kernel_size: 5 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2"}layer{ name: "norm2" type: "LRN" bottom: "conv2" top: "norm2" lrn_param{ local_size: 5 alpha: 0.0001 beta: 0.75 }}layer{ name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" 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: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0 } }}layer{ name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3"}layer{ name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4_3" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 384 pad: 1 kernel_size: 3 group: 2 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu4" type: "ReLU" bottom: "conv4_3" top: "conv4_3"}layer{ name: "pool2" type: "Pooling" bottom: "conv4_3" top: "pool2" pooling_param{ pool: MAX kernel_size: 3 stride: 2 }}layer{ name: "conv5" type: "Convolution" bottom: "pool2" top: "conv5" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 256 pad: 1 kernel_size: 3 group: 2 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5"}layer{ name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param{ pool: MAX kernel_size: 3 stride: 1 pad: 1 }}layer{ name: "fc6-conv" 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 kernel_size: 3 #dilation: 3 pad: 6 dilation: 6 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6"}layer{ name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param{ dropout_ratio: 0.5 }}layer{ name: "fc7-conv" 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: "gaussian" std: 0.005 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7"}layer{ name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param{ dropout_ratio: 0.5 }}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: "Normalize" bottom: "conv4_3" top: "conv4_3_norm" norm_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_loss" type: "MultiBoxLoss" bottom: "mbox_loc" bottom: "mbox_conf" bottom: "mbox_priorbox" bottom: "label" top: "mbox_loss" include{ phase: TRAIN } propagate_down: true propagate_down: true propagate_down: false propagate_down: false loss_param{ normalization: VALID } multibox_loss_param{ loc_loss_type: SMOOTH_L1 conf_loss_type: SOFTMAX loc_weight: 1.0 num_classes: 21 share_location: true match_type: PER_PREDICTION overlap_threshold: 0.5 use_prior_for_matching: true background_label_id: 0 use_difficult_gt: true do_neg_mining: true neg_pos_ratio: 3.0 neg_overlap: 0.5 code_type: CENTER_SIZE }}
name: "AlexNet"layer{ name: "data" type: "AnnotatedData" top: "data" top: "label" include{ phase: TEST } transform_param{ mean_value: 104 mean_value: 117 mean_value: 123 resize_param{ prob: 1 resize_mode: WARP height: 300 width: 300 interp_mode: LINEAR } } data_param{ source: "data/VOCdevkit/VOC0712/lmdb/VOC0712_test_lmdb" batch_size: 1 backend: LMDB } annotated_data_param{ batch_sampler{ } label_map_file: "caffe-ssd/data/coco/labelmap_coco.prototxt" }}layer{ name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 96 kernel_size: 11 stride: 4 pad: 5 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0 } }}layer{ name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1"}layer{ name: "norm1" type: "LRN" bottom: "conv1" top: "norm1" lrn_param{ local_size: 5 alpha: 0.0001 beta: 0.75 }}layer{ name: "pool1" type: "Pooling" bottom: "norm1" top: "pool1" pooling_param{ pool: MAX kernel_size: 3 stride: 2 pad: 1 }}layer{ name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 256 pad: 2 group: 2 kernel_size: 5 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2"}layer{ name: "norm2" type: "LRN" bottom: "conv2" top: "norm2" lrn_param{ local_size: 5 alpha: 0.0001 beta: 0.75 }}layer{ name: "conv3" type: "Convolution" bottom: "norm2" top: "conv3" 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: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0 } }}layer{ name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3"}layer{ name: "conv4" type: "Convolution" bottom: "conv3" top: "conv4_3" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 384 pad: 1 group: 2 kernel_size: 3 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu4" type: "ReLU" bottom: "conv4_3" top: "conv4_3"}layer{ name: "pool2" type: "Pooling" bottom: "conv4_3" top: "pool2" pooling_param{ pool: MAX kernel_size: 3 stride: 2 }}layer{ name: "conv5" type: "Convolution" bottom: "pool2" top: "conv5" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } convolution_param{ num_output: 256 pad: 1 kernel_size: 3 group: 2 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu5" type: "ReLU" bottom: "conv5" top: "conv5"}layer{ name: "pool5" type: "Pooling" bottom: "conv5" top: "pool5" pooling_param{ pool: MAX kernel_size: 3 stride: 1 pad: 1 }}layer{ name: "fc6-conv" 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 kernel_size: 3 dilation: 6 pad: 6 weight_filler{ type: "gaussian" std: 0.01 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6"}layer{ name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param{ dropout_ratio: 0.5 }}layer{ name: "fc7-conv" 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: "gaussian" std: 0.005 } bias_filler{ type: "constant" value: 0.1 } }}layer{ name: "relu7" type: "ReLU" bottom: "fc7" top: "fc7"}layer{ name: "drop7" type: "Dropout" bottom: "fc7" top: "fc7" dropout_param{ dropout_ratio: 0.5 }}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: "Normalize" bottom: "conv4_3" top: "conv4_3_norm" norm_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_param{ nms_threshold: 0.45 top_k: 400 } save_output_param{ output_directory: "data/VOCdevkit/results/VOC2007/SSD_300x300/Main" output_name_prefix: "comp4_det_test_" output_format: "VOC" label_map_file: "caffe-ssd/data/VOC0712/labelmap_voc.prototxt" name_size_file: "caffe-ssd/data/VOC0712/test_name_size.txt" num_test_image: 4952 } code_type: CENTER_SIZE keep_top_k: 200 confidence_threshold: 0.01 }}layer{ name: "detection_eval" type: "DetectionEvaluate" bottom: "detection_out" bottom: "label" top: "detection_eval" include{ phase: TEST } detection_evaluate_param{ num_classes: 21 background_label_id: 0 overlap_threshold: 0.5 evaluate_difficult_gt: false name_size_file: "caffe-ssd/data/VOC0712/test_name_size.txt" }} 2 0
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