Caffe

Question

我正在尝试在 caffe 中创建单个多class 和多标签网络配置。

假设class狗的化：狗是小的还是大的？ (class) 它是什么颜色？ (class) 它有衣领吗？（标签）

这东西可以用caffe吗？这样做的正确方法是什么？构建 lmdb 文件的正确方法是什么？

关于多标签class化的所有出版物都是2015年左右的，从那以后这个主题发生了一些变化？

谢谢。

Answer 1

Caffe 的 LMDB 接口的问题是它只允许。
如果您希望每个图像有多个标签，则必须使用不同的输入层。
我建议使用 "HDF5Data" 图层：
这允许更灵活地设置输入数据，您可以为该层设置任意数量的 "top"。每个输入图像可能有多个标签，并且您的网络有多个损失可供训练。

请参阅了解如何为 caffe 创建 hdf5 数据。

Answer 2

感谢,

只是想了解实用的方法.. 创建包含所有图像标签的 2 个 .text 文件（一个用于训练，一个用于验证）后，例如：

/train/img/1.png 0 4 18
/train/img/2.png 1 7 17 33
/train/img/3.png 0 4 17

运行 py脚本：

import h5py, os
import caffe
import numpy as np

SIZE = 227 # fixed size to all images
with open( 'train.txt', 'r' ) as T :
    lines = T.readlines()
# If you do not have enough memory split data into
# multiple batches and generate multiple separate h5 files
X = np.zeros( (len(lines), 3, SIZE, SIZE), dtype='f4' ) 
y = np.zeros( (len(lines),1), dtype='f4' )
for i,l in enumerate(lines):
    sp = l.split(' ')
    img = caffe.io.load_image( sp[0] )
    img = caffe.io.resize( img, (SIZE, SIZE, 3) ) # resize to fixed size
    # you may apply other input transformations here...
    # Note that the transformation should take img from size-by-size-by-3 and transpose it to 3-by-size-by-size
    # for example
    transposed_img = img.transpose((2,0,1))[::-1,:,:] # RGB->BGR
    X[i] = transposed_img
    y[i] = float(sp[1])
with h5py.File('train.h5','w') as H:
    H.create_dataset( 'X', data=X ) # note the name X given to the dataset!
    H.create_dataset( 'y', data=y ) # note the name y given to the dataset!
with open('train_h5_list.txt','w') as L:
    L.write( 'train.h5' ) # list all h5 files you are going to use

并创建 train.h5 和 val.h5（X 数据集包含图像，Y 数据集包含标签吗？）。

替换我的网络输入层：

layers { 
 name: "data" 
 type: DATA 
 top:  "data" 
 top:  "label" 
 data_param { 
   source: "/home/gal/digits/digits/jobs/20181010-191058-21ab/train_db" 
   backend: LMDB 
   batch_size: 64 
 } 
 transform_param { 
    crop_size: 227 
    mean_file: "/home/gal/digits/digits/jobs/20181010-191058-21ab/mean.binaryproto" 
    mirror: true 
  } 
  include: { phase: TRAIN } 
} 
layers { 
 name: "data" 
 type: DATA 
 top:  "data" 
 top:  "label" 
 data_param { 
   source: "/home/gal/digits/digits/jobs/20181010-191058-21ab/val_db"  
   backend: LMDB 
   batch_size: 64
 } 
 transform_param { 
    crop_size: 227 
    mean_file: "/home/gal/digits/digits/jobs/20181010-191058-21ab/mean.binaryproto" 
    mirror: true 
  } 
  include: { phase: TEST } 
}

到

layer {
  type: "HDF5Data"
  top: "X" # same name as given in create_dataset!
  top: "y"
  hdf5_data_param {
    source: "train_h5_list.txt" # do not give the h5 files directly, but the list.
    batch_size: 32
  }
  include { phase:TRAIN }
}

layer {
  type: "HDF5Data"
  top: "X" # same name as given in create_dataset!
  top: "y"
  hdf5_data_param {
    source: "val_h5_list.txt" # do not give the h5 files directly, but the list.
    batch_size: 32
  }
  include { phase:TEST }
}

我猜 HDF5 不需要 mean.binaryproto?

接下来，输出层应该如何变化才能输出多个标签概率？我想我需要交叉熵层而不是 softmax？这是当前的输出层：

layers {
  bottom: "prob"
  bottom: "label"
  top: "loss"
  name: "loss"
  type: SOFTMAX_LOSS
  loss_weight: 1
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "prob"
  bottom: "label"
  top: "accuracy"
  include: { phase: TEST }
}

Caffe - 多class和多标签图像class化

Caffe - multi-class and multi-label image classification

multilabel-classification

deep-learning