文件列表:

caffe.proto (78498, 2019-03-18)
center_loss.png (324547, 2019-03-18)
center_loss_layer.cpp (4351, 2019-03-18)
center_loss_layer.cu (2756, 2019-03-18)
center_loss_layer.hpp (1464, 2019-03-18)
center_loss_test.prototxt (20593, 2019-03-18)
center_loss_train.prototxt (25966, 2019-03-18)
multibox_center_loss_layer.cpp (23603, 2019-03-18)
multibox_center_loss_layer.hpp (4059, 2019-03-18)
multibox_focal_loss_layer.cpp (15649, 2019-03-18)
multibox_focal_loss_layer.hpp (3492, 2019-03-18)

# SSD-Centerloss This is an unofficial trial applying Centerloss to SSD multibox_loss function Technical details are in the paper: **A Discriminative Feature Learning Approach for Deep Face Recognition** https://pan.baidu.com/s/1up_PWpR85HqVe10yhFzHoQ SSD(Single Shot MultiBox Detector) implements the multibox_loss function in the https://github.com/weiliu89/caffe/tree/ssd. We can read the loss function through the coding multibox_loss_layer.h/multibox_loss_layer.cpp # Motivation: When detecting objects on the image, we often employ, including SSD, softmax function to classify the object and L1 regression to localize the object. $$ L(x,c,l,g) = \frac{1}{N}(L_{conf}(x,c)+ \alpha L_{loc}(x,l,g))$$ In the equation above, $L_{conf}$ represents the function that classifies each object, $L_{loc}$ stands for the localization function. $N$ is the number of default boxes. This equation means that averaging the sum of all of default boxes loss. Each default box will contribute to the final loss. However, for some objects that are similar to each other, learning the location information may be easy. The softmax function is hard to work due to the similarity of feature of foreground samples. Center loss can effectively decrease the feature difference between the same object.

 # How to use SSD-Centerloss ### (1) add center loss layer: Notation ~~ is the root path of your caffe-ssd dir ``` cp center_loss_layer.cpp ~~/caffe-ssd/src/caffe/layers/ cp center_loss_layer.h ~~/caffe-ssd/include/caffe/layers/ cp multibox_center_loss_layer.cpp ~~/caffe-ssd/src/caffe/layers/ cp multibox_center_loss_layer.hpp ~~/caffe-ssd/include/caffe/layers/ ``` ### (2)Then adding the following code in the caffe.proto ``` message CenterLossParameter { optional uint32 num_output = 1; // The number of outputs for the layer optional FillerParameter center_filler = 2; // The filler for the centers // The first axis to be lumped into a single inner product computation; // all preceding axes are retained in the output. // May be negative to index from the end (e.g., -1 for the last axis). optional int32 axis = 3 [default = 1]; } message MultiBoxCenterLossParameter{ //center_features represents the length of features that is equal to the length of object centers in each default box. optional uint32 center_features = 1; } ``` Adding in the message LayerParameter ``` optional MultiBoxCenterLossParameter multibox_center_loss_param = 211;//this value should be the only in this message optional CenterLossParameter center_loss_param = 149; ``` ### (3) Getting the center_features of each default box For an instance, fc7_norm layer has 4 anchors, including aspect ratio = sqrt(2),1,1/2,2. Each anchor has 16 center_features. Therefore the num_output is 64. ``` layer { name: "fc7_norm_center_mbox_conf_new" type: "Convolution" bottom: "fc7_norm" top: "fc7_norm_center_mbox_conf" 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 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "fc7_norm_center_mbox_conf_perm" type: "Permute" bottom: "fc7_norm_center_mbox_conf" top: "fc7_norm_center_mbox_conf_perm" permute_param { order: 0 order: 2 order: 3 order: 1 } } layer { name: "fc7_norm_mbox_center_conf_flat" type: "Flatten" bottom: "fc7_norm_center_mbox_conf_perm" top: "fc7_norm_mbox_center_conf_flat" flatten_param { axis: 1 } } ``` ### (4) Changing the function type in train.prototxt ``` layer { name: "mbox_loss" type: "MultiBoxCenterLoss" # the type was changed bottom: "mbox_loc" bottom: "mbox_conf" bottom: "mbox_priorbox" bottom: "label" bottom: "mbox_center_conf" #mbox_center_conf is the concatenation of all the center_features in all default box. top: "mbox_loss" include { phase: TRAIN } propagate_down: true propagate_down: true propagate_down: false propagate_down: false propagate_down: true #center_features layers need backward. loss_param { normalization: VALID } multibox_loss_param { loc_loss_type: SMOOTH_L1 conf_loss_type: SOFTMAX loc_weight: 1 num_classes: 21 share_location: true match_type: PER_PREDICTION overlap_threshold: 0.2 use_prior_for_matching: true background_label_id: 0 use_difficult_gt: true neg_pos_ratio: 3 neg_overlap: 0.1 code_type: CENTER_SIZE ignore_cross_boundary_bbox: false mining_type: MAX_NEGATIVE } multibox_center_loss_param { center_features: 16 # center_features represents the length of features that is equal to the length of object centers in each default box. } } ```

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