原
Tensorflow下用自己的数据集对Faster RCNN进行训练和测试（二）
对于Tensorflow版本的Faster RCNN网络，网上包括github上都有不同的源码版本，本人之前也在进行不同版本的运行测试，可以说是每个版本都有不同的错误，在解决这些错误时可谓道阻且长。而对于用自己的数据集来训练和测试Faster RCNN网络，本人在之前的博客https://blog.youkuaiyun.com/hitzijiyingcai/article/details/81808091中已经成功实验了一次，之前的那篇博客采用的是github上的此源码https://github.com/endernewton/tf-faster-rcnn。但是本人发现网上很多关于源码的解析都是基于另一个github代码：https://github.com/smallcorgi/Faster-RCNN_TF，对于此代码的demo的基本运行及可能遇到的错误，可以参看本人之前的博客：https://blog.youkuaiyun.com/hitzijiyingcai/article/details/81747148，因此，本人就打算基于此源码进行自己的数据集的训练和测试，基于此源码的过程可谓是困难重重，遇到的各个错误有时并不能完全搜到，在这里记录下整个过程。
一、运行demo
1、获取代码
git clone --recursive https://github.com/smallcorgi/Faster-RCNN_TF.git
NB：在下载了整个文件夹之后，要自己手动新建几个文件夹，否则后面会报错，一个是在根目录下新建一个output文件夹，另一个是在experiments文件夹下新建一个logs文件夹。
2、建立cyphon模块
在下载的Faster RCNN的lib目录下make
-
cd
$FRCN_ROOT/lib
-
make
3、下载预训练模型
在这里提供一个百度云下载地址： 链接：https://pan.baidu.com/s/1zNWzMxBwQ6qVoXXvN89Peg 密码：0rtb
下载完成之后，在tools文件夹下新建一个名称为model的文件夹，把下载好的模型放进去。
4、运行demo
在Faster RCNN根目录下执行：
python ./tools/demo.py --model /share2/home/qingdong/sharedir/Faster-RCNN_TF/tools//model/VGGnet_fast_rcnn_iter_70000.ckpt
至此，可以得到运行demo的结果，在这期间会遇到一些错误，具体可参看在文章开头提供的本人之前博客的地址中的内容。
二、下载数据并预训练
1、下载 VOCdevkit的训练验证和测试集
-
wget
http:/
/host.robots.ox.ac.uk/pascal
/VOC/voc2007/VOCtrainval_06-Nov-
2007.tar
-
wget
http:/
/host.robots.ox.ac.uk/pascal
/VOC/voc2007/VOCtest_06-Nov-
2007.tar
-
wget
http:/
/host.robots.ox.ac.uk/pascal
/VOC/voc2007/VOCdevkit_08-Jun-
2007.tar
2、解压上面下载的三个压缩包
-
tar
xvf
VOCtrainval_06-Nov-2007
.tar
-
tar
xvf
VOCtest_06-Nov-2007
.tar
-
tar
xvf
VOCdevkit_08-Jun-2007
.tar
严格按照命令解压，这样解压后的文件嵌套形式不会变。
三个压缩文件解压后文件存储在一个文件夹VOCdekit下，这个文件夹下包含VOCcode和VOC2007等文件夹，结构如下：
-
$VOCdevkit/
# development kit
-
$VOCdevkit/VOCcode/
# VOC utility code
-
$VOCdevkit/VOC2007
# image sets, annotations, etc.
-
# ... and several other directories ...
3、重命名
手动将下载的数据集文件夹VOCdevkit修改名字为VOCdevkit2007放入faster rcnn目录下的Data文件夹里。也可以通过以下代码实现，改名字是因为代码中是VOCdevkit2007
-
cd
$FRCN_ROOT/data
-
ln -s
$VOCdevkit VOCdevkit2007
4、下载预训练模型
此处下载的目的是将其参数作为我们训练模型的初始化参数：
下载地址：https://drive.google.com/open?id=0ByuDEGFYmWsbNVF5eExySUtMZmM或
https://www.dropbox.com/s/po2kzdhdgl4ix55/VGG_imagenet.npy?dl=0
此处为下载VGG_imagenet.npy，需要翻墙，本人也是费了点力气才下载成功，下载完成之后在faster rcnn目录下的data文件夹下新建文件夹pretrain_model，将下载好的预训练模型VGG_imagenet.npy放进去。
5、训练模型
在根目录下：
./experiments/scripts/faster_rcnn_end2end.sh gpu 0 VGG16 pascal_voc
在这里要注意，源码中的迭代次数为70000次，为了节约时间完成对源码的运行测试，可将experiments/scripts/faster_rcnn_end2end.sh文件、lib/fast_rcnn/config.py文件中的迭代次数此改为小一点数（具体格式可参看后文）。本人将其修改为了200。
NB：训练和测试模型中遇到的错误：
（1）训练完模型后会出现如下错误
这是因为tensorflow问题并没有在/output/faster_rcnn_end2end/voc_2007_trainval 中生成VGGnet_fast_rcnn_iter_70000.ckpt文件。
解决方法：
在FASTER-RCNN_TF/lib/fast_rcnn/train.py 文件中：
将saver = tf.train.Saver(max_to_keep=100)改为：
saver = tf.train.Saver(max_to_keep=100,write_version=saver_pb2.SaverDef.V1)
同时，在文件开头加上：from tensorflow.core.protobuf import saver_pb2
在这里要注意，上述修改后，再次训练时，会出现如下提示：
这是在保存迭代文件时提示V1版本过低建议使用V2，此时千万不要改为V2，否则在修改完之后会持续提示：
Waiting for ../output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_200.ckpt to exist...
这是没法生成ckpt文件，即使在output文件夹下可以看到生成了一些结果文件，但是没法生成ckpt文件，因此上述不要改成V2。
（2）在训练测试时出现如下错误：
这个错误真的是费了九牛二虎之力，在百度后发现根本没有切实可行的方法，最后还是使用Google解决了问题，发现在运行代码时候在lib文件夹下面执行make操作的时候需要将make.sh文件以及setup.py文件进行修改，将arch参数从sm_37改为sm_61。此处是我在服务器上的参数，在这里附上一张参考图，大家可以根据自己的电脑配置自行查找相应参数进行修改。
重新训练完成后在$Faster-RCNN_TF/output/faster_rcnn_end2end/voc_2007_trainval中生成VGGnet_fast_rcnn_iter_200.ckpt文件。
6、测试模型
在修改完上述错误之后，运行如下代码进行测试：
python ./tools/test_net.py --device gpu --device_id 0 --weights output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_200.ckpt --imdb voc_2007_test --cfg experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_test
测试结果：
可以看到在测试结果中出现了负值，这是完全不对的，这是由于迭代次太少的原因，因此又将迭代次数进行了修改，改为了2000，得到了如下结果：
可以看出，AP仍然非常小，但是作为测试运行代码来说，是正确的结果就可以了。
三、用自己的数据集进行训练、测试
1、制作数据集
此部分内容可以参考本人之前的博客，有详细介绍：https://blog.youkuaiyun.com/hitzijiyingcai/article/details/81636455。
制作完成之后替换掉data下的VOCdevkit2007下相应的数据即可。
2.修改代码
（1）lib/datatsets/pascal_voc.py
修改为自己的类别，由于本人的图片为png格式，因此也将图片读取格式一并修改。
（2）lib/datasets/imdb.py
这里将num_classes改为自己的类别数+1，本人定义了3类，因此是4。
（3）lib/networks/VGGnet_train.py
将类别数改为4。同理，VGGnet_test.py修改同上
（4）tools/demo.py
为了测试demo.py的方便，所以也把tools/demo.py中的类别改成自己的类别。
（5）修改迭代次数等参数
这里大家根据自己的计算，选择合适的迭代次数以及学习率等， 个人认为，初试学习率0.001，如果不收敛再减小一个量级，另外，70000次在gpu（看自己的gpu性能，我的是1080）上跑，也只是需要半天多的时间，所以还是可以接受的迭代次数，至于选择多少迭代次数合适，可以根据不同次数训练好的模型，测试验证。此次为了测试运行代码且由前为了得到正确的结果，将迭代次数设为2000。
首先在experiments/scripts/faster_rcnn_end2end.sh文件中修改迭代次数：
（在ITERS中修改成自己想要的参数）
然后，我们进入lib/fast_rcnn/config.py，对config.py进行修改：
其中，第一项，就是学习率，STEPSIZE就是你对训练步长的修改，这里一定要小于等于前面训练文件的ITERS参数。其他大家可以选择保持一致，对于动量和伽马参数不用修改，当然，对于训练每隔多少次显示，大家根据自己情况修改，这里是10次一显示。
除此，我们可以修改batch的大小，
第一个参数是每次输入faster-rcnn网络中图片数量，第二个参数就是训练batch的大小。
关于模型保存问题：
这里，第一个参数是训练时，每迭代多少次保存一次模型；第二个参数是保存时模型的名字。
另外，大家要在训练的时候，可以将rpn检测目标设置为True：
NB：在这里需要注意的是，以上在修改每一个py文件的时候，会发现相应的文件夹中也有一个同名的pyc文件，在修改之前要将其删除。在修改完上述py文件后要进入lib文件夹重新进行make。
3、训练数据
进入你的Faster-rcnn文件夹根目录，然后直接输入：
./experiments/scripts/faster_rcnn_end2end.sh gpu 0 VGG16 pascal_voc
训练完成之后，Faster-RCNN-TF会将训练好的模型保存在output文件夹下，具体如下，这里是自己训练的一个模型 ，前面为迭代次数保存的模型，最终的模型为以2000为后缀名的：
若训练时遇到文件夹权限问题，可以参考这篇博客https://blog.youkuaiyun.com/qq_39531954/article/details/78865452。
4、测试数据
对于需要调整参数进行优化的我们来讲，我们最好对于不同迭代次数产生的模型都进行测试，能够让我们很好的找到一个合适的迭代次数。
训练完成后，会产生许多数据的计算值，并保存了下来，但是在单独测试（单独）时，我们就要将这些文件删除了，以免会产生错误，如下图：
另外，在测试前，最好进入自己数据集中的ImageSet/Main文件夹下，重新看一下自己的训练、验证、测试集编号和数量，确认没有问题。
对于测试代码可以根据tool文件夹里的test_net.py文件能够得到我们的测试语句，如下图：
这里，根据自己的情况进行修改，其中default中的值就是默认值，如果你在测试时，在终端输入时对这部分不赋值，那么就会按照default中默认值进行赋值。如果你想压缩在终端时的赋值语句，就完全可以把这部分的default值全部按照自己要输入的值修改好。
本次使用的语句为：
python ./tools/test_net.py --device gpu --device_id 0 --weights output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_2000.ckpt --imdb voc_2007_test --cfg experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_test
这里，采用gpu，weights就是我们训练好的模型位置。cfg就选择你测试的方式，这里使用的就是端到端的方式，即由输入直接得到结果。networks就是你使用的网络结构，当然，如果你有自己的网络结构，可以使用自己的，只是自己需要重新修改定义。
最后，可以运行demo看看实际框出的效果图：
python ./tools/demo.py --model output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_2000.ckpt
在这里附上效果较好的一张图片，可以看出，本次是定义了三类且迭代了2000次，跟源码当中迭代70000此相比效果虽没那么好，但是对于迭代2000次来说，已经能够达到一定的效果，后续会增加次数相信会有更好的结果。
本文为博主原创，转载请注明出处：https://blog.youkuaiyun.com/hitzijiyingcai/article/details/81914200
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<div class="comment-list-box" style="max-height: none;"><ul class="comment-list"><li class="comment-line-box d-flex" data-commentid="8923504" data-replyname="King_key"> <a target="_blank" href="https://me.youkuaiyun.com/King_key"><img src="https://avatar.youkuaiyun.com/E/2/5/3_king_key.jpg" alt="King_key" class="avatar"></a> <div class="right-box "> <div class="info-box"> <a target="_blank" href="https://me.youkuaiyun.com/King_key"><span class="name ">King_key：</span></a> <span class="comment">请问，你的类别数目是直接修改就行吗？为什么我的在直接修改后出现了 Assign requires shapes of both tensors to match. lhs shape= [8] rhs shape= [84]
出现了
UnknownError (see above for traceback): exceptions.NameError: global name ‘gpu_nms’ is not defined
Traceback (most recent call last):
File “/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/script_ops.py”, line 206, in call
ret = func(*args)
File “/root/faster_rcnn1/Faster-RCNN_TF-master/tools/…/lib/rpn_msr/proposal_layer_tf.py”, line 127, in proposal_layer
keep = nms(np.hstack((proposals, scores)), nms_thresh)
File “/root/faster_rcnn1/Faster-RCNN_TF-master/tools/…/lib/fast_rcnn/nms_wrapper.py”, line 19, in nms
return gpu_nms(dets, thresh, device_id=cfg.GPU_ID)
NameError: global name ‘gpu_nms’ is not defined
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</div>windows下使用自己制作的数据集训练faster-rcnn（tensorflow版）用于目标检测
01-10 阅读数 4593
步骤一下载配置windows下tensorflow版faster-rcnn，参考博客：windows10下运行tensorflow版的faster-Rcnn步骤二制作自己的数据集参考博客：http:/... 博文 来自： 把岁月化成歌 留在博客
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hover-show">11-22</span> <span class="read-num hover-hide"> 阅读数 1946</span> </p> </div> </a> <p class="content" style="width: 962px;"> <a href="https://blog.youkuaiyun.com/lk123400/article/details/53282827" target="_blank" title="faster-rcnn训练自己的数据集以及遇到的一些问题"> <span class="desc oneline">入坑DeepLearning,尝试着利用faster-rcnn做目标检测，因此也就入了这个坑了。数据集的准备主要根据这篇文章，步骤很明确，资料也提供的比较全，包括label和目标框的标注工具等。htt...</span> </a> <span class="blog_title_box oneline "> <span class="type-show type-show-blog type-show-after">博文</span> <a target="_blank" href="https://blog.youkuaiyun.com/lk123400">来自： <span class="blog_title"> lk123400的专栏</span></a> </span> </p> </div> </div> <div class="recommend-item-box recommend-box-ident type_blog clearfix" data-track-click="{"mod":"popu_387","con":",https://blog.youkuaiyun.com/hongxingabc/article/details/79039537,BlogCommendFromQuerySearch_20,index_21"}" data-flg="true"> <div class="content" style="width: 962px;"> <a 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镜像：类似虚拟机的镜像、用俗话说就是安装文件。
容器：类似一个轻量…
博文
来自： 我走小路的博客
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//js
…
博文
来自： websites
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组图0-1.分页实现效果图一
//
组图0-2.分页实现效果图二
一、从效果可以看出内容由两部分组成：
1.学生信息
数据库中插入一些记录…
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来自： niaonao
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{
public:
//constructor and destructor
CSTLSolid()
{
m_bInitialized=FALS…
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来自： Augusdi的专栏
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imp…
博文
来自： hunanqi的博客
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孤立的一个点也是一个连通分量
使用tarjan算法 在嵌套的多个环中优先得到最大环( 最小环就是每个孤立点）
定义：
int Ti…
博文
来自： 九野的博客
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每个java开发人员对java.lang.ClassNotFoundExcetpion这个异常肯定都不陌生，这背后就涉及到了java技术体系中的类加载。Java的类加载…
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来自： Jack Zhou的专栏
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65535这个数字的由来，很多人想当然地将它与port最大值联系起来。的确，TCP的…
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来自： 田发江的专栏
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[base-src]
name=CentOS-5.4 - Base src -
baseurl=http://vault.ce…
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来自： linux/unix
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a.Firebug虽然可以抓包…
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来自： 专注、专心
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一开始我按照网上的…
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python ./tools/test_net.py --device gpu --device_id 0 --weights output/faster_rcnn_end2end/voc_2007_trainval/VGGnet_fast_rcnn_iter_200.ckpt --imdb voc_2007_test --cfg experiments/cfgs/faster_rcnn_end2end.yml --network VGGnet_test