Faster RCNN訓練出現問題：ValueError: operands could not be broadcast together with shapes
ss = 0.0154733 (* 1 = 0.0154733 loss)
I1017 02:21:18.817603 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.101269 (* 1 = 0.101269 loss)
I1017 02:21:18.817608 16224 sgd_solver.cpp:106] Iteration 9800, lr = 0.001
I1017 02:21:19.844008 16224 solver.cpp:229] Iteration 9820, loss = 0.605604
I1017 02:21:19.844032 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.258967 (* 1 = 0.258967 loss)
I1017 02:21:19.844036 16224 solver.cpp:245] Train net output #1: loss_cls = 0.179147 (* 1 = 0.179147 loss)
I1017 02:21:19.844041 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.0545008 (* 1 = 0.0545008 loss)
I1017 02:21:19.844060 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.11299 (* 1 = 0.11299 loss)
I1017 02:21:19.844065 16224 sgd_solver.cpp:106] Iteration 9820, lr = 0.001
I1017 02:21:20.869732 16224 solver.cpp:229] Iteration 9840, loss = 0.59657
I1017 02:21:20.869755 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.125047 (* 1 = 0.125047 loss)
I1017 02:21:20.869760 16224 solver.cpp:245] Train net output #1: loss_cls = 0.221004 (* 1 = 0.221004 loss)
I1017 02:21:20.869765 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.0261099 (* 1 = 0.0261099 loss)
I1017 02:21:20.869783 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.22441 (* 1 = 0.22441 loss)
I1017 02:21:20.869787 16224 sgd_solver.cpp:106] Iteration 9840, lr = 0.001
I1017 02:21:21.898542 16224 solver.cpp:229] Iteration 9860, loss = 0.566665
I1017 02:21:21.898564 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.217061 (* 1 = 0.217061 loss)
I1017 02:21:21.898571 16224 solver.cpp:245] Train net output #1: loss_cls = 0.205798 (* 1 = 0.205798 loss)
I1017 02:21:21.898574 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.0281926 (* 1 = 0.0281926 loss)
I1017 02:21:21.898593 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.115613 (* 1 = 0.115613 loss)
I1017 02:21:21.898598 16224 sgd_solver.cpp:106] Iteration 9860, lr = 0.001
I1017 02:21:22.924695 16224 solver.cpp:229] Iteration 9880, loss = 0.596356
I1017 02:21:22.924718 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.280685 (* 1 = 0.280685 loss)
I1017 02:21:22.924724 16224 solver.cpp:245] Train net output #1: loss_cls = 0.163881 (* 1 = 0.163881 loss)
I1017 02:21:22.924729 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.0350983 (* 1 = 0.0350983 loss)
I1017 02:21:22.924734 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.116692 (* 1 = 0.116692 loss)
I1017 02:21:22.924738 16224 sgd_solver.cpp:106] Iteration 9880, lr = 0.001
I1017 02:21:23.953996 16224 solver.cpp:229] Iteration 9900, loss = 0.538847
I1017 02:21:23.954020 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.206076 (* 1 = 0.206076 loss)
I1017 02:21:23.954026 16224 solver.cpp:245] Train net output #1: loss_cls = 0.175002 (* 1 = 0.175002 loss)
I1017 02:21:23.954046 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.057081 (* 1 = 0.057081 loss)
I1017 02:21:23.954049 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.100688 (* 1 = 0.100688 loss)
I1017 02:21:23.954054 16224 sgd_solver.cpp:106] Iteration 9900, lr = 0.001
I1017 02:21:24.981950 16224 solver.cpp:229] Iteration 9920, loss = 0.688178
I1017 02:21:24.981978 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.215188 (* 1 = 0.215188 loss)
I1017 02:21:24.981983 16224 solver.cpp:245] Train net output #1: loss_cls = 0.363008 (* 1 = 0.363008 loss)
I1017 02:21:24.981988 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.0116421 (* 1 = 0.0116421 loss)
I1017 02:21:24.982007 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.0983393 (* 1 = 0.0983393 loss)
I1017 02:21:24.982012 16224 sgd_solver.cpp:106] Iteration 9920, lr = 0.001
I1017 02:21:26.007951 16224 solver.cpp:229] Iteration 9940, loss = 0.532088
I1017 02:21:26.007973 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.270666 (* 1 = 0.270666 loss)
I1017 02:21:26.007979 16224 solver.cpp:245] Train net output #1: loss_cls = 0.0925087 (* 1 = 0.0925087 loss)
I1017 02:21:26.007984 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.00594117 (* 1 = 0.00594117 loss)
I1017 02:21:26.007988 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.162972 (* 1 = 0.162972 loss)
I1017 02:21:26.007993 16224 sgd_solver.cpp:106] Iteration 9940, lr = 0.001
I1017 02:21:27.034415 16224 solver.cpp:229] Iteration 9960, loss = 0.553832
I1017 02:21:27.034436 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.221964 (* 1 = 0.221964 loss)
I1017 02:21:27.034441 16224 solver.cpp:245] Train net output #1: loss_cls = 0.20358 (* 1 = 0.20358 loss)
I1017 02:21:27.034446 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.0315744 (* 1 = 0.0315744 loss)
I1017 02:21:27.034466 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.0967139 (* 1 = 0.0967139 loss)
I1017 02:21:27.034469 16224 sgd_solver.cpp:106] Iteration 9960, lr = 0.001
I1017 02:21:28.060611 16224 solver.cpp:229] Iteration 9980, loss = 0.488194
I1017 02:21:28.060633 16224 solver.cpp:245] Train net output #0: loss_bbox = 0.222793 (* 1 = 0.222793 loss)
I1017 02:21:28.060639 16224 solver.cpp:245] Train net output #1: loss_cls = 0.183032 (* 1 = 0.183032 loss)
I1017 02:21:28.060644 16224 solver.cpp:245] Train net output #2: rpn_cls_loss = 0.00902992 (* 1 = 0.00902992 loss)
I1017 02:21:28.060662 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.0733386 (* 1 = 0.0733386 loss)
I1017 02:21:28.060667 16224 sgd_solver.cpp:106] Iteration 9980, lr = 0.001
speed: 0.051s / iter
Traceback (most recent call last):
File "./tools/train_net.py", line 112, in <module>
max_iters=args.max_iters)
File "/home/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 160, in train_net
model_paths = sw.train_model(max_iters)
File "/home/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 108, in train_model
model_paths.append(self.snapshot())
File "/home/py-faster-rcnn/tools/../lib/fast_rcnn/train.py", line 73, in snapshot
self.bbox_stds[:, np.newaxis])
ValueError: operands could not be broadcast together with shapes (84,1024) (8,1) ./experiments/scripts/faster_rcnn_alt_opt.sh 0 VGG16 pascal_voc
NETS = {'vgg16': ('VGG16',
'VGG16_Caltech_faster_rcnn_final.caffemodel'), #這裡需要修改為訓練後得到的模型的名稱
'zf': ('ZF',
'ZF_Caltech_faster_rcnn_final.caffemodel')} #這裡需要修改為訓練後得到的模型的名稱
修改：
VGG16 Train.prototxt
Line 530行：
num_classes:”2‘’修改成 型別數+1（背景算一類）；
Line 620行：
num_output 同上；
Line 643行：
num_output: 8
（類別數+1）* 4 ，“4”是指bbox的四個角
解決：
@-PC:~/py-faster-rcnn$ cd ..
@-PC:~$ geditpy-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/stage1_fast_rcnn_train.pt
@-PC:~$ gedit py-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/stage1_rpn_train.pt
@-PC:~$ geditpy-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/stage2_fast_rcnn_train.pt
@-PC:~$ geditpy-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/stage2_rpn_train.pt
@-PC:~$ geditpy-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/faster_rcnn_test.pt
@-PC:~$
相關推薦
Faster RCNN訓練出現問題：ValueError: operands could not be broadcast together with shapes
ss = 0.0154733 (* 1 = 0.0154733 loss) I1017 02:21:18.817603 16224 solver.cpp:245] Train net output #3: rpn_loss_bbox = 0.101269 (* 1
python問題：ValueError: operands could not be broadcast together with shapes (100,3) (3,1)
原文連結：http://www.mamicode.com/info-detail-1072145.html 背景：dataMatrix是(100,3)的列表，labelMat是(1，100)的列表，weights是(3,1)的陣列，屬性如下程式碼所示： >>
ValueError: operands could not be broadcast together with shapes (2,) (1640,)已解決
在量化交易資料處理中遇到一個問題 違反了ufunc的廣播機制（有關廣播概念，單開一篇詳細介紹：傳送蟲洞） 廣播機制如下： 當我們使用ufunc函式對兩個陣列進行計算時，ufunc函式會對這兩個陣列的對應元素進行計算，因此它要求這兩個陣列有相同的大小(shape
Python-Numpy: operands could not be broadcast together with shapes
BUG樣式： ValueError: operands could not be broadcast together with shapes (353,) (355,) 舉個栗子: import numpy numpy_a=numpy.array([1, 2, 3, 4,
Faster RCNN訓練出現問題：Matlab版本訓練
I1113 20:56:49.405777 23834 net.cpp:42] Initializing net from parameters: name: "Zeiler_conv5" input: "data" input_dim: 1 input_dim: 3
Faster RCNN訓練出現問題：Selective search data not found at: /home/py-faster-rcnn/data/selective_search_dat
~/py-faster-rcnn$ ./experiments/scripts/fast_rcnn.sh 0 VGG16 pascal_voc + set -e + export PYTHONUNBUFFERED=True + PYTHONUNBUFFERED=True +
faster rcnn報錯：TypeError: slice indices must be integers or None or have an __index__ method
https://blog.csdn.net/qq_27637315/article/details/78849756 faster rcnn報錯：TypeError: slice indices must be integers or None or have an __index__ method
XCode 或者ITune 添加賬號時，提示：This action could not be completed. 或者 Access Privileges
net clas cti people led acc missing rec itunes 當遇到This action could not be completed 或者 You do not have enough access privileges for this
mac上 go-delve 安裝出現The specified item could not be found in the keychain 解決方法
mon pen 方法 rar spec hub 錯誤 sys lib 安裝go語言的編譯環境，在安裝到 delve時候出現如下錯誤： If reporting this issue please do so at (not Homebrew/brew or Hom
啟動報錯：The JRE could not be found. Edit the server and change the JRE location.
轉載內容，原文連結：http://blog.sina.com.cn/s/blog_7f865faf0100uvev.html Tomcat報錯： The JRE could not be found. Edit the server and change the JRE
SoapUI啟動報錯：The JVM could not be started. The maximum heap size (-Xmx) might be too large
SoapUI啟動的時候，報如題錯誤，經過排查，在安裝目錄\bin\下，找到SoapUI的vmoptions引數配置檔案 打開發現-Xmx引數配置，將預設值改小一些即可： -Xms128m -Xmx80
WARNING: The host 'WeiLei' could not be looked up with resolveip.
[[email protected] data]# /usr/local/mysql/scripts/mysql_install_db --user=mysql --basedir=/usr/local/mysql/ --datadir=/usr/local/mysql/data/WARNING:
用自己的資料集訓練faster-rcnn時出現的一些問題及總結(五)
關於faster-rcnn的原理訓練流程及其資料集的製備，已經都已完成。那麼問題來了，“自己製備的資料集，比如說自己為了增強資料集，進行圖片翻轉操作，然後記錄座標這樣的資料集，在訓練的過程中出現的問題”一、在呼叫append_flipped_images函式時出現： asse
faster-rcnn訓練時出現error == cudaSuccess (30 vs. 0)
./experiments/scripts/faster_rcnn_alt_opt.sh 0 ZF pascal_voc 使用這條命令訓練，出現下面這個錯誤 Check failed: error == cudaSuccess (30 vs. 0) unknow
spring-boot 啟動時候 出現異常：The bean 'xxx' could not be injected as a 'xx.xxxx' because it is a JDK dynamic proxy that implements:
ng- for sed AC 存在 cti 解決方法 sid 失敗 The bean ‘xxxService‘ could not be injected as a ‘AaaXxxService‘ because it is a JDK dynamic proxy tha
Ubuntu16.04+GeForce GTX 1070Ti+CUDA8.0+cuDNN5.1+TensorFlow1.2+tf-faster-rcnn訓練
1、下載CUDA8.0和CUDNN5.1 百度網盤下載地址（包含8.0和9.0）：https://pan.baidu.com/s/1ir3rKhUtU1aIRE7n1BQ5mg 2、安裝CUDA8.0 安裝方式1(字尾為.deb的)： sudo dpkg -i cuda-repo-u
使用pytorch版faster-rcnn訓練自己資料集
使用pytorch版faster-rcnn訓練自己資料集 引言 faster-rcnn pytorch程式碼下載 訓練自己資料集 接下來工作 參考文獻 引言 最近在復現目標檢測程式碼（師兄強烈推薦F
用Faster RCNN訓練自己的資料集
Faster RCNN（py caffe）工程各個目錄的作用： caffe-fast-rcnn：caffe框架目錄； data：用來存放pretrained（預訓練）模型以及讀取檔案的cache快取，還有一些下載模型的指令碼； experiments:存放配置
製作自己的目標檢測資料集再利用tf-faster-rcnn訓練
1.製作資料集的工具 我利用資料集標註工具是精靈標註助手，我認為很好用。奉勸一句標註資料集時不要隨便找人給你標，如果他沒有用心給你標註，你在後面訓練會出現很多問題。在一開始標註資料集的時候不要一下子給他標註完，先標註幾十張圖片，然後用在你的網路裡，看看有沒有錯誤，如果沒有問
Caffe學習系列——Faster-RCNN訓練自己的資料集
由於要實現服裝的目標檢測，所以一直在研究Faster-RCNN 。講到目標檢測，不得不提到rbg大神的深度神經網路檢測算法系列RCNN、Fast-RCNN、Faster-RCNN，其還在github上開源了自己的程式碼，造福廣大碼農。這是rbg大神的主頁 https://people.eecs.berk