dongzhuoyao

name: "ResNet-50"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224

layer {
	bottom: "data"
	top: "conv1"

dongzhuoyao

# VGG 16-layer network convolutional finetuning
# Network modified to have smaller receptive field (128 pixels)
# nand smaller stride (8 pixels) when run in convolutional mode.
#
# In this model we also change max pooling size in the first 4 layers
# from 2 to 3 while retaining stride = 2
# which makes it easier to exactly align responses at different layers.
#
# For alignment to work, we set (we choose 32x so as to be able to evaluate
# the model for all different subsampling sizes):

dongzhuoyao

name: "RCF"

layer {
  name: "data"
  type: "ImageLabelmapData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }

dongzhuoyao

layer {
  name: "data"
  type: "CPMData"
  top: "data"
  top: "label"
  data_param {
    source: "/home/zhecao/COCO_kpt/lmdb_trainVal"
    batch_size: 10
    backend: LMDB
  }

dongzhuoyao

input: "data"
input_dim: 1
input_dim: 3
input_dim: 368
input_dim: 368
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"

dongzhuoyao

name: "RCF"

layer {
  name: "data"
  type: "ImageLabelmapData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }

dongzhuoyao

function [Dictionary,output] = KSVD(...
    Data,... % an nXN matrix that contins N signals (Y), each of dimension n.
    param)
% =========================================================================
%                          K-SVD algorithm
% =========================================================================
% The K-SVD algorithm finds a dictionary for linear representation of
% signals. Given a set of signals, it searches for the best dictionary that
% can sparsely represent each signal. Detailed discussion on the algorithm
% and possible applications can be found in "The K-SVD: An Algorithm for 

dongzhuoyao

name: "xception"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: false
    crop_size: 224
    mean_value: 104.0

dongzhuoyao

name: "shufflenet"
#  transform_param {
#    scale: 0.017
#    mirror: false
#    crop_size: 224
#    mean_value: [103.94,116.78,123.68]
#  }
input: "data"
input_shape { 
  dim: 1

dongzhuoyao

name: "xception"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  transform_param {
    mirror: false
    crop_size: 224
    mean_value: 104.0