These code follow the data providers in papers: and
Usage:
name: "GoogleNet"
layer {
name: "data_mb"
type: "MultibatchData"
top: "data_mb"
top: "label_type_mb"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
multi_batch_data_param {
root_folder: "xxxxxxxx"
source: "./train.txt.exist"
batch_size: 120
shuffle: true
new_height: 224
new_width: 224
# how many identities in one batch
identity_num_per_batch: 60
# how many images belong to one identity
img_num_per_identity: 2
# Sampling strategy
# TRUE FOR each identity has the same sampling probability
# FALSE FOR each image has the same sampling probability
rand_identity: true
}
}