Thanks for reading this issue.
I am trying to organize ImageNet into tripet formation using the provided scripts, but I am a little confused about how to write the relevant LISTFILE. Among "Pic1.jpg Pic2.jpg Pic3.jpg", which one is the anchor image and which one is the positive sample?

Thank you!

Hi, I use keras to realize this paper. but I do not know whether it has problem or not.
`#!/usr/bin/env python3

-- coding: utf-8 --

"""
Created on Thu Jul 5 10:26:49 2018

@author: dolphin
"""

from future import absolute_import
from future import print_function
import keras
from keras import backend as K
import tensorflow as tf

#define slice function
def slice_f(x,c1,c2):
return x[c1:c2,:]

#define a slice layer using Lamda layer
def slice_hash_layer(inputX,arguemnts):
slice_s=keras.layers.Lambda(slice_f,arguments=arguemnts)(inputX)
hash_s=keras.layers.Dense(1,activation='sigmoid',kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.1))(slice_s)
return hash_s

def deep_hash_model_():
inputX=keras.Input(shape=(224,224,3))
#conv1
conv1_1=keras.layers.Conv2D(96,kernel_size=(11,11),strides=(4,4),activation='relu',
kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.01))(inputX)
conv1_2=keras.layers.Conv2D(96,kernel_size=(1,1),striders=(1,1),activation='relu',
kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.05))(conv1_1)
pool1=keras.layers.MaxPool2D(pool_size=(3,3),strides = (2,2),padding='valid')(conv1_2)
pool1=keras.layers.Dropout(0.5)(pool1)

#conv2
conv2_1=keras.layers.Conv2D(256,kernel_size=(5,5),strides=(1,1),activation='relu',
                      kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.01),
                      padding='same')(pool1)
conv2_2=keras.layers.Conv2D(256,kernel_size=(1,1),strides=(1,1),activation='relu',
                      kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.05),
                      padding='same')(conv2_1)
pool2=keras.layers.MaxPool2D(pool_size=(3,3),strides = (2,2),padding='valid')(conv2_2)

#conv3
conv3_1=keras.layers.Conv2D(384,kernel_size=(3,3),strides=(1,1),activation='relu',
                      kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.01),
                      padding='valid')(pool2)
conv3_2=keras.layers.Conv2D(384,kernel_size=(1,1),strides=(1,1),activation='relu',
                            kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.05),
                            padding='valid')(conv3_1)
pool3=keras.layers.MaxPool2D(pool_size=(3,3),strides = (2,2),padding='valid')(conv3_2)

#conv4
conv4_1=keras.layers.Conv2D(1024,kernel_size=(3,3),strides=(1,1),activation='relu',
                      kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.01),
                      padding='valid')(pool3)
conv4_2=keras.layers.Conv2D(1200,kernel_size=(1,1),strides=(1,1),activation='relu',
                            kernel_initializer=keras.initializers.TruncatedNormal(stddev=0.05),
                            padding='valid')(conv4_1)
pool4=keras.layers.AvgPool2D(kernel_size=(6,6),strides=(1,1),padding='valid')(conv4_2)

#divide and encode module
#use 24 hash-code-bit
slice_hash=[]
for i in range(24):
    arguments={'c1': i*50, 'c2': (i+1)*50}
    slice_hash.append(slice_hash_layer(pool4,arguments))
merge_one=keras.layers.concatenate(slice_hash)  
final_model = keras.models.Model(inputs=inputX, outputs=merge_one)
return final_model

deep_hash_model = deep_hash_model_()

#triplt loss function
#read a line from text,each line include a triple,including three images:
#query_image,positive_image,negtivate_image
batch_size=24
def triplt_loss(y_true,y_pred):
loss=tf.convert_to_tensor(0,dtype=tf.float32)
total_loss=tf.convert_to_tensor(0,dtype=tf.float32)
g=tf.constant(1.0,shape=[1],dtype=tf.float32)
zero=tf.constant(0.0,shape=[1],dtype=tf.float32)
for i in range(0,batch_size,3):
q_embedding=y_pred[i]
p_embedding=y_pred[i+1]
n_embedding=y_pred[i+2]
D_q_p=K.sqrt(K.sum((q_embedding-p_embedding)**2))
D_q_n=K.sqrt(K.sum((q_embedding-n_embedding)**2))
loss=tf.maximum(g+D_q_p-D_q_n,zero)
total_loss=total_loss+loss
total_loss=total_loss/(batch_size/3)
return total_loss `

I run "run_test12.sh". I find a file "pool_hashcode.txt" but miss "query_hashcode.txt".

It causes a problem when run evaluate_map.cpp.
Please help

Hello
Thanks for your hard work.
After downloading your source code and datasets and deployed it in runtime/cifar_hash_dataset, I ran./run_train.sh in runtime/12bit and after serveral hours I got

.......
I0913 18:39:05.471935 10028 solver.cpp:311] Iteration 100000, loss = 0.000143271
I0913 18:39:05.471941 10028 solver.cpp:316] Optimization Done.

But when I ran ./run_test.sh after training, I got

......
I0913 19:03:20.272930 12613 caffe.cpp:319] Running for iteration#589...
I0913 19:03:20.296061 12613 caffe.cpp:354] Loss: 0
Reading images's hashcode from files...
Starting querying images in 58999 images' pool...
Finish querying 1 images in 58999 pool images set within the top 10000 returned neighbors!
Mean Average Precision(MAP): 0
MAP of label#1: 0
MAP of label#2: 0
MAP of label#3: 0
MAP of label#4: 0
MAP of label#5: 0
MAP of label#6: 0
MAP of label#7: 0
MAP of label#8: 0
MAP of label#9: 0
MAP of label#10: 0

I have not modified any code and don't know what I did wrong. Could you please give me some advices?
Thank you.

hello, I'm asking a details of the partition of the NUS-WIDE data set: how do you randomly select 100 samples in each classes for the query set (and so does the training set)?
I mean, as it's a multi-label data set, a same very sample might be selected several times during the sampling of each classes.
thanks

Please, share this dataset on some more available sources like google drive or smth. I just cannot create an baidu account for downloading your dataset. I would much appreciate your help

Hello, I am compiling the "convert_triplet_imageset.cpp", and the error is io.h: No such file or directory. Even though there is a "io.h" in "/usr/include/x86_64-linux-gnu/sys", it cannot solve my problem. When I copy the file to "/usr/include", the result just likes the result when I simply comment the "#include <io.h>", in "io.cpp". i.e. "error: ‘_open’ was not declared in this scope","error: ‘_close’ was not declared in this scope" and so on. So what should I do to compile the file "convert_triplet_imageset.cpp"