caffe学习笔记12 -- R-CNN detection

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原创最新推荐文章于 2026-08-04 13:12:54 发布 1.5w 阅读 AI 写同款· GEO 优化 ›

[Caffe]:关于caffe新手入门 Caffe的几个重要文件用了这么久Caffe都没好好写过一篇新手入门的博客，最近应实验室小师妹要求，打算写一篇简单、快熟入门的科普文。利用Caffe进行深度神经网络训练第一步需要搞懂几个重要文件： solver.prototxt train_val.prototxt train.sh 接下来我们按顺序一个个说明。solver.prototxtsolver这个文件主要存放模型训练所用到的一些超参数阅读详情

这是caffe文档中Notebook Examples的倒数第二个例子，链接地址http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/detection.ipynb

这个例子用R-CNN做目标检测。

R-CNN是一个先进的目标检测模型，它通过微调caffe模型提供分类区域。对于R-CNN系统和模型的详细介绍，参考

Rich feature hierarchies for accurate object detection and semantic segmentation. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014.Arxiv 2013.

在本例中，预训练模型基于ImageNet数据集，在并在ILSVRC13上进行微调，输出200个检测分类的得分。需要注意的是：本例中一个原始数据对应所有SVM分类的得分，没有概率校准和类间比较。本例中使用现成的模型只是为了简便，并非完整的R-CNN模型

现在，来检测图caffe-master/examples/images/fish-bike.jpg

首先，需要做一些准备工作：

i. 安装matlab, 具体安装过程参考：http://blog.youkuaiyun.com/thystar/article/details/50720691

ii. 添加matlab安装路径，sudo gedit ~/.bashrc,　在文本最后添加：export PATH="/home/sindyz/software/matlab2014/bin":$PATH　(我的安装路径)保存后需要重启电脑，要说明的是，这一步是必要的，否则运行时会出现：OSError: [Errno 2] No such file or directory错误。

iii. 下载Selective Search文件，下载地址：https://github.com/sergeyk/selective_search_ijcv_with_python，用于检测候选框，关于Selective Search的算法介绍，参考：http://koen.me/research/selectivesearch/，下载完成后，解压，在matlab下运行demo.m, 无报错信息关闭即可，需要注意的是，如果这个文件不在$CAFFE-ROOT/python目录下,需要将其添加到PYTHONPATH路径中，我的是：export PYTHONPATH=/home/sindyz/code/matlabCode/selective_search_ijcv_with_python/:$PYTHONPATH。（按自己的情况添加）

完成上述步骤后，还有几处需要注意和修改的地方：

在Selective Search文件目录下运行python selective_search.py，看看是否由报错信息，一般来说，如果你添加好了matlab路径，这里不会出什么问题。
修改$CAFFE-ROOT/python/caffe/detector.py中第86行左右：
predictions = out[self.outputs[0]].squeeze(axis=(2, 3))　改为　

predictions = out[self.outputs[0]].squeeze()，否则会报出ValueError: 'axis' entry 2 is out of bounds (-2, 2)错误
修改$CAFFE-ROOT/python/caffe/detector.py中114行左右
import selective_search_ijcv_with_python as selective_search
改为
import selective_search，因为在Selective Search文件目录下，只有selective_search.py模块,否则会出现模块找不到的错误

OK，现在可以开心的运行R-CNN这个例子了。

1. 更改目录，导入相应的包

import os
caffe_root = '/home/sindyz/caffe-master/'
os.chdir(caffe_root)
import sys
sys.path.insert(0,'./python')

2. 创建临时目录，导入检测样本

! mkdir -p _temp
! echo examples/images/fish-bike.jpg > _temp/det_input.txt

3. 运行selective_search提取候选框，调用Caffe进行分类预测。运行GPU模式

! python/detect.py --crop_mode=selective_search --pretrained_model=models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5

输出如下内容：

GPU mode
WARNING: Logging before InitGoogleLogging() is written to STDERR
I0608 10:32:38.067106  6131 net.cpp:42] Initializing net from parameters: 
name: "R-CNN-ilsvrc13"
input: "data"
input_dim: 10
input_dim: 3
input_dim: 227
input_dim: 227
state {
  phase: TEST
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc-rcnn"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc-rcnn"
  inner_product_param {
    num_output: 200
  }
}
I0608 10:32:38.067556  6131 net.cpp:370] Input 0 -> data
I0608 10:32:38.067576  6131 layer_factory.hpp:74] Creating layer conv1
I0608 10:32:38.067585  6131 net.cpp:90] Creating Layer conv1
I0608 10:32:38.067589  6131 net.cpp:410] conv1 <- data
I0608 10:32:38.067595  6131 net.cpp:368] conv1 -> conv1
I0608 10:32:38.067603  6131 net.cpp:120] Setting up conv1
I0608 10:32:38.108999  6131 net.cpp:127] Top shape: 10 96 55 55 (2904000)
I0608 10:32:38.109035  6131 layer_factory.hpp:74] Creating layer relu1
I0608 10:32:38.109048  6131 net.cpp:90] Creating Layer relu1
I0608 10:32:38.109055  6131 net.cpp:410] relu1 <- conv1
I0608 10:32:38.109063  6131 net.cpp:357] relu1 -> conv1 (in-place)
I0608 10:32:38.109076  6131 net.cpp:120] Setting up relu1
I0608 10:32:38.109233  6131 net.cpp:127] Top shape: 10 96 55 55 (2904000)
I0608 10:32:38.109244  6131 layer_factory.hpp:74] Creating layer pool1
I0608 10:32:38.109257  6131 net.cpp:90] Creating Layer pool1
I0608 10:32:38.109263  6131 net.cpp:410] pool1 <- conv1
I0608 10:32:38.109269  6131 net.cpp:368] pool1 -> pool1
I0608 10:32:38.109277  6131 net.cpp:120] Setting up pool1
I0608 10:32:38.109311  6131 net.cpp:127] Top shape: 10 96 27 27 (699840)
I0608 10:32:38.109318  6131 layer_factory.hpp:74] Creating layer norm1
I0608 10:32:38.109325  6131 net.cpp:90] Creating Layer norm1
I0608 10:32:38.109329  6131 net.cpp:410] norm1 <- pool1
I0608 10:32:38.109335  6131 net.cpp:368] norm1 -> norm1
I0608 10:32:38.109341  6131 net.cpp:120] Setting up norm1
I0608 10:32:38.109349  6131 net.cpp:127] Top shape: 10 96 27 27 (699840)
I0608 10:32:38.109352  6131 layer_factory.hpp:74] Creating layer conv2
I0608 10:32:38.109360  6131 net.cpp:90] Creating Layer conv2
I0608 10:32:38.109364  6131 net.cpp:410] conv2 <- norm1
I0608 10:32:38.109370  6131 net.cpp:368] conv2 -> conv2
I0608 10:32:38.109376  6131 net.cpp:120] Setting up conv2
I0608 10:32:38.109931  6131 net.cpp:127] Top shape: 10 256 27 27 (1866240)
I0608 10:32:38.109947  6131 layer_factory.hpp:74] Creating layer relu2
I0608 10:32:38.109954  6131 net.cpp:90] Creating Layer relu2
I0608 10:32:38.109959  6131 net.cpp:410] relu2 <- conv2
I0608 10:32:38.109966  6131 net.cpp:357] relu2 -> conv2 (in-place)
I0608 10:32:38.109972  6131 net.cpp:120] Setting up relu2
I0608 10:32:38.110002  6131 net.cpp:127] Top shape: 10 256 27 27 (1866240)
I0608 10:32:38.110008  6131 layer_factory.hpp:74] Creating layer pool2
I0608 10:32:38.110014  6131 net.cpp:90] Creating Layer pool2
I0608 10:32:38.110018  6131 net.cpp:410] pool2 <- conv2
I0608 10:32:38.110024  6131 net.cpp:368] pool2 -> pool2
I0608 10:32:38.110030  6131 net.cpp:120] Setting up pool2
I0608 10:32:38.110136  6131 net.cpp:127] Top shape: 10 256 13 13 (432640)
I0608 10:32:38.110144  6131 layer_factory.hpp:74] Creating layer norm2
I0608 10:32:38.110152  6131 net.cpp:90] Creating Layer norm2
I0608 10:32:38.110157  6131 net.cpp:410] norm2 <- pool2
I0608 10:32:38.110162  6131 net.cpp:368] norm2 -> norm2
I0608 10:32:38.110168  6131 net.cpp:120] Setting up norm2
I0608 10:32:38.110175  6131 net.cpp:127] Top shape: 10 256 13 13 (432640)
I0608 10:32:38.110179  6131 layer_factory.hpp:74] Creating layer conv3
I0608 10:32:38.110187  6131 net.cpp:90] Creating Layer conv3
I0608 10:32:38.110191  6131 net.cpp:410] conv3 <- norm2
I0608 10:32:38.110198  6131 net.cpp:368] conv3 -> conv3
I0608 10:32:38.110203  6131 net.cpp:120] Setting up conv3
I0608 10:32:38.111160  6131 net.cpp:127] Top shape: 10 384 13 13 (648960)
I0608 10:32:38.111176  6131 layer_factory.hpp:74] Creating layer relu3
I0608 10:32:38.111183  6131 net.cpp:90] Creating Layer relu3
I0608 10:32:38.111189  6131 net.cpp:410] relu3 <- conv3
I0608 10:32:38.111194  6131 net.cpp:357] relu3 -> conv3 (in-place)
I0608 10:32:38.111202  6131 net.cpp:120] Setting up relu3
I0608 10:32:38.111232  6131 net.cpp:127] Top shape: 10 384 13 13 (648960)
I0608 10:32:38.111238  6131 layer_factory.hpp:74] Creating layer conv4
I0608 10:32:38.111243  6131 net.cpp:90] Creating Layer conv4
I0608 10:32:38.111248  6131 net.cpp:410] conv4 <- conv3
I0608 10:32:38.111253  6131 net.cpp:368] conv4 -> conv4
I0608 10:32:38.111260  6131 net.cpp:120] Setting up conv4
I0608 10:32:38.112344  6131 net.cpp:127] Top shape: 10 384 13 13 (648960)
I0608 10:32:38.112357  6131 layer_factory.hpp:74] Creating layer relu4
I0608 10:32:38.112365  6131 net.cpp:90] Creating Layer relu4
I0608 10:32:38.112370  6131 net.cpp:410] relu4 <- conv4
I0608 10:32:38.112375  6131 net.cpp:357] relu4 -> conv4 (in-place)
I0608 10:32:38.112381  6131 net.cpp:120] Setting up relu4
I0608 10:32:38.112411  6131 net.cpp:127] Top shape: 10 384 13 13 (648960)
I0608 10:32:38.112416  6131 layer_factory.hpp:74] Creating layer conv5
I0608 10:32:38.112422  6131 net.cpp:90] Creating Layer conv5
I0608 10:32:38.112427  6131 net.cpp:410] conv5 <- conv4
I0608 10:32:38.112432  6131 net.cpp:368] conv5 -> conv5
I0608 10:32:38.112439  6131 net.cpp:120] Setting up conv5
I0608 10:32:38.113263  6131 net.cpp:127] Top shape: 10 256 13 13 (432640)
I0608 10:32:38.113279  6131 layer_factory.hpp:74] Creating layer relu5
I0608 10:32:38.113286  6131 net.cpp:90] Creating Layer relu5
I0608 10:32:38.113291  6131 net.cpp:410] relu5 <- conv5
I0608 10:32:38.113297  6131 net.cpp:357] relu5 -> conv5 (in-place)
I0608 10:32:38.113303  6131 net.cpp:120] Setting up relu5
I0608 10:32:38.113333  6131 net.cpp:127] Top shape: 10 256 13 13 (432640)
I0608 10:32:38.113339  6131 layer_factory.hpp:74] Creating layer pool5
I0608 10:32:38.113347  6131 net.cpp:90] Creating Layer pool5
I0608 10:32:38.113350  6131 net.cpp:410] pool5 <- conv5
I0608 10:32:38.113356  6131 net.cpp:368] pool5 -> pool5
I0608 10:32:38.113363  6131 net.cpp:120] Setting up pool5
I0608 10:32:38.113502  6131 net.cpp:127] Top shape: 10 256 6 6 (92160)
I0608 10:32:38.113520  6131 layer_factory.hpp:74] Creating layer fc6
I0608 10:32:38.113528  6131 net.cpp:90] Creating Layer fc6
I0608 10:32:38.113533  6131 net.cpp:410] fc6 <- pool5
I0608 10:32:38.113538  6131 net.cpp:368] fc6 -> fc6
I0608 10:32:38.113545  6131 net.cpp:120] Setting up fc6
I0608 10:32:38.140440  6131 net.cpp:127] Top shape: 10 4096 (40960)
I0608 10:32:38.140478  6131 layer_factory.hpp:74] Creating layer relu6
I0608 10:32:38.140492  6131 net.cpp:90] Creating Layer relu6
I0608 10:32:38.140498  6131 net.cpp:410] relu6 <- fc6
I0608 10:32:38.140506  6131 net.cpp:357] relu6 -> fc6 (in-place)
I0608 10:32:38.140516  6131 net.cpp:120] Setting up relu6
I0608 10:32:38.140576  6131 net.cpp:127] Top shape: 10 4096 (40960)
I0608 10:32:38.140583  6131 layer_factory.hpp:74] Creating layer drop6
I0608 10:32:38.140589  6131 net.cpp:90] Creating Layer drop6
I0608 10:32:38.140594  6131 net.cpp:410] drop6 <- fc6
I0608 10:32:38.140599  6131 net.cpp:357] drop6 -> fc6 (in-place)
I0608 10:32:38.140605  6131 net.cpp:120] Setting up drop6
I0608 10:32:38.140611  6131 net.cpp:127] Top shape: 10 4096 (40960)
I0608 10:32:38.140616  6131 layer_factory.hpp:74] Creating layer fc7
I0608 10:32:38.140622  6131 net.cpp:90] Creating Layer fc7
I0608 10:32:38.140630  6131 net.cpp:410] fc7 <- fc6
I0608 10:32:38.140636  6131 net.cpp:368] fc7 -> fc7
I0608 10:32:38.140643  6131 net.cpp:120] Setting up fc7
I0608 10:32:38.153045  6131 net.cpp:127] Top shape: 10 4096 (40960)
I0608 10:32:38.153095  6131 layer_factory.hpp:74] Creating layer relu7
I0608 10:32:38.153105  6131 net.cpp:90] Creating Layer relu7
I0608 10:32:38.153112  6131 net.cpp:410] relu7 <- fc7
I0608 10:32:38.153120  6131 net.cpp:357] relu7 -> fc7 (in-place)
I0608 10:32:38.153129  6131 net.cpp:120] Setting up relu7
I0608 10:32:38.153200  6131 net.cpp:127] Top shape: 10 4096 (40960)
I0608 10:32:38.153206  6131 layer_factory.hpp:74] Creating layer drop7
I0608 10:32:38.153214  6131 net.cpp:90] Creating Layer drop7
I0608 10:32:38.153219  6131 net.cpp:410] drop7 <- fc7
I0608 10:32:38.153224  6131 net.cpp:357] drop7 -> fc7 (in-place)
I0608 10:32:38.153231  6131 net.cpp:120] Setting up drop7
I0608 10:32:38.153237  6131 net.cpp:127] Top shape: 10 4096 (40960)
I0608 10:32:38.153242  6131 layer_factory.hpp:74] Creating layer fc-rcnn
I0608 10:32:38.153249  6131 net.cpp:90] Creating Layer fc-rcnn
I0608 10:32:38.153254  6131 net.cpp:410] fc-rcnn <- fc7
I0608 10:32:38.153259  6131 net.cpp:368] fc-rcnn -> fc-rcnn
I0608 10:32:38.153267  6131 net.cpp:120] Setting up fc-rcnn
I0608 10:32:38.154058  6131 net.cpp:127] Top shape: 10 200 (2000)
I0608 10:32:38.154080  6131 net.cpp:194] fc-rcnn does not need backward computation.
I0608 10:32:38.154085  6131 net.cpp:194] drop7 does not need backward computation.
I0608 10:32:38.154090  6131 net.cpp:194] relu7 does not need backward computation.
I0608 10:32:38.154095  6131 net.cpp:194] fc7 does not need backward computation.
I0608 10:32:38.154100  6131 net.cpp:194] drop6 does not need backward computation.
I0608 10:32:38.154105  6131 net.cpp:194] relu6 does not need backward computation.
I0608 10:32:38.154110  6131 net.cpp:194] fc6 does not need backward computation.
I0608 10:32:38.154115  6131 net.cpp:194] pool5 does not need backward computation.
I0608 10:32:38.154129  6131 net.cpp:194] relu5 does not need backward computation.
I0608 10:32:38.154134  6131 net.cpp:194] conv5 does not need backward computation.
I0608 10:32:38.154139  6131 net.cpp:194] relu4 does not need backward computation.
I0608 10:32:38.154145  6131 net.cpp:194] conv4 does not need backward computation.
I0608 10:32:38.154150  6131 net.cpp:194] relu3 does not need backward computation.
I0608 10:32:38.154155  6131 net.cpp:194] conv3 does not need backward computation.
I0608 10:32:38.154160  6131 net.cpp:194] norm2 does not need backward computation.
I0608 10:32:38.154165  6131 net.cpp:194] pool2 does not need backward computation.
I0608 10:32:38.154170  6131 net.cpp:194] relu2 does not need backward computation.
I0608 10:32:38.154175  6131 net.cpp:194] conv2 does not need backward computation.
I0608 10:32:38.154180  6131 net.cpp:194] norm1 does not need backward computation.
I0608 10:32:38.154193  6131 net.cpp:194] pool1 does not need backward computation.
I0608 10:32:38.154198  6131 net.cpp:194] relu1 does not need backward computation.
I0608 10:32:38.154203  6131 net.cpp:194] conv1 does not need backward computation.
I0608 10:32:38.154208  6131 net.cpp:235] This network produces output fc-rcnn
I0608 10:32:38.154220  6131 net.cpp:482] Collecting Learning Rate and Weight Decay.
I0608 10:32:38.154227  6131 net.cpp:247] Network initialization done.
I0608 10:32:38.154232  6131 net.cpp:248] Memory required for data: 62425920
E0608 10:32:38.221285  6131 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel
I0608 10:32:38.324671  6131 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter
Loading input...
selective_search_rcnn({'/home/ouxinyu/caffe-master/examples/images/fish-bike.jpg'}, '/tmp/tmpu85WGa.mat')
Processed 1570 windows in 17.131 s.
/usr/lib/python2.7/dist-packages/pandas/io/pytables.py:2487: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]

  warnings.warn(ws, PerformanceWarning)
Saved to _temp/det_output.h5 in 0.025 s.

4. 运行后输出的文件名，选择的窗口，检测得分存放在~/_temp/det_outpu.h5文件中，查看结果：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

df = pd.read_hdf('_temp/det_output.h5', 'df')
print(df.shape)
print(df.iloc[0])

输出：

(1570, 5)
prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...
ymin                                                     79.846
xmin                                                       9.62
ymax                                                     246.31
xmax                                                    339.624
Name: /home/sindyz/caffe-master/examples/images/fish-bike.jpg, dtype: object

Selective Search选出了1570个区域，作为R-CNN的输入，图与图的候选框的数量随图像的内容和大小不同而改变，也就是说：selective search不是尺度不变的。

通常，detect.py在运行大量图片时是非常高效的：首先，对所有图片提取候选框，用GPU批处理这些窗口，输出结果。只要在images_file中列出图像名，就可以批处理了。

仅管本例中只给出了Imagenet的R-CNN检测，但是detect.py可以适应不同caffe模型的输入维度，批处理规模及输出类别。你可以根据需要选择模型定义和预处理模型，参考detect.py --help根据数据选择参数。

5. 加载ILSVRC13的检测类别名称，做预测的DataFrame, 注意，通过./data/ilsvrc12/get_ilsvrc12_aux.sh获取数据

with open('../data/ilsvrc12/det_synset_words.txt') as f:
    labels_df = pd.DataFrame([
        {
            'synset_id': l.strip().split(' ')[0],
            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]
        }
        for l in f.readlines()
    ])
labels_df.sort('synset_id')
predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])
print(predictions_df.iloc[0])

输出：

name
accordion      -2.622471
airplane       -2.845789
ant            -2.851220
antelope       -3.208377
apple          -1.949950
armadillo      -2.472936
artichoke      -2.201685
axe            -2.327404
baby bed       -2.737924
backpack       -2.176764
bagel          -2.681061
balance beam   -2.722538
banana         -2.390628
band aid       -1.598909
banjo          -2.298197
...
trombone        -2.582361
trumpet         -2.352853
turtle          -2.360860
tv or monitor   -2.761042
unicycle        -2.218468
vacuum          -1.907718
violin          -2.757080
volleyball      -2.723690
waffle iron     -2.418540
washer          -2.408994
water bottle    -2.174899
watercraft      -2.837426
whale           -3.120339
wine bottle     -2.772961
zebra           -2.742914
Name: 0, Length: 200, dtype: float32

6. 查看激活值并可视化

plt.gray()
plt.matshow(predictions_df.values)
plt.xlabel('Classes')
plt.ylabel('Windows')

7.　取得分最大值，并输出

max_s = predictions_df.max(0)
max_s.sort(ascending=False)
print(max_s[:10])

输出

name
person          1.835771
bicycle         0.866109
unicycle        0.057079
motorcycle     -0.006122
banjo          -0.028208
turtle         -0.189833
electric fan   -0.206787
cart           -0.214237
lizard         -0.393519
helmet         -0.477942
dtype: float32

8. 检测结果最高的是人和自行车，检测还需要定位，于是，选择得分最高的人和自行车来定位

# Find, print, and display the top detections: person and bicycle.
i = predictions_df['person'].argmax()
j = predictions_df['bicycle'].argmax()

# Show top predictions for top detection.
f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])
print('Top detection:')
print(f.order(ascending=False)[:5])
print('')

# Show top predictions for second-best detection.
f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])
print('Second-best detection:')
print(f.order(ascending=False)[:5])

# Show top detection in red, second-best top detection in blue.
im = plt.imread('examples/images/fish-bike.jpg')
plt.imshow(im)
currentAxis = plt.gca()

det = df.iloc[i]
coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))

det = df.iloc[j]
coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))

输出

Top detection:
name
person             1.835771
swimming trunks   -1.150371
rubber eraser     -1.231106
turtle            -1.266037
plastic bag       -1.303266
dtype: float32

Second-best detection:
name
bicycle     0.866109
unicycle   -0.359140
scorpion   -0.811621
lobster    -0.982891
lamp       -1.096809
dtype: float32

9. 拿所有的自行车检测，并用NMS避免窗口重叠。

def nms_detections(dets, overlap=0.3):
    """
    Non-maximum suppression: Greedily select high-scoring detections and
    skip detections that are significantly covered by a previously
    selected detection.

    This version is translated from Matlab code by Tomasz Malisiewicz,
    who sped up Pedro Felzenszwalb's code.

    Parameters
    ----------
    dets: ndarray
        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']
    overlap: float
        minimum overlap ratio (0.3 default)

    Output
    ------
    dets: ndarray
        remaining after suppression.
    """
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    ind = np.argsort(dets[:, 4])

    w = x2 - x1
    h = y2 - y1
    area = (w * h).astype(float)

    pick = []
    while len(ind) > 0:
        i = ind[-1]
        pick.append(i)
        ind = ind[:-1]

        xx1 = np.maximum(x1[i], x1[ind])
        yy1 = np.maximum(y1[i], y1[ind])
        xx2 = np.minimum(x2[i], x2[ind])
        yy2 = np.minimum(y2[i], y2[ind])

        w = np.maximum(0., xx2 - xx1)
        h = np.maximum(0., yy2 - yy1)

        wh = w * h
        o = wh / (area[i] + area[ind] - wh)

        ind = ind[np.nonzero(o <= overlap)[0]]

    return dets[pick, :]

scores = predictions_df['bicycle']
windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values
dets = np.hstack((windows, scores[:, np.newaxis]))
nms_dets = nms_detections(dets)

10. 显示排名前3的NMS处理过的自行车，注意得分最高的红色的框与其他框之间的差异

plt.imshow(im)
currentAxis = plt.gca()
colors = ['r', 'b', 'y']
for c, det in zip(colors, nms_dets[:3]):
    currentAxis.add_patch(
        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],
        fill=False, edgecolor=c, linewidth=5)
    )
print 'scores:', nms_dets[:3, 4]