caffe-fast-rcnn（Caffe、FSRCNN、FastRCNN）

转载请注明：http://blog.csdn.net/forest_world

二、FSRCNN开发环境搭建：

 faster-rcnn: matlab版本ShaoqingRen/faster_rcnn: Faster R-CNN rbg提供的python版本rbgirshick/py-faster-rcnn 

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git clone https://github.com/LMDB/lmdb Cloning into 'lmdb'... remote: Counting objects: 7201, done. remote: Total 7201 (delta 0), reused 0 (delta 0), pack-reused 7201 Receiving objects: 100% (7201/7201), 1.40 MiB | 7.00 KiB/s, done. Resolving deltas: 100% (3097/3097), done. Checking connectivity... done.

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sudo make install

https://github.com/rbgirshick/fast-rcnn.git

make

make -j8 && make pycaffe

@ubuntu:~/fast-rcnn/caffe-fast-rcnn$ make -j8 && make pycaffe LD -o .build_release/lib/libcaffe.so CXX/LD -o .build_release/tools/compute_image_mean.bin CXX/LD -o .build_release/tools/upgrade_net_proto_binary.bin CXX/LD -o .build_release/tools/upgrade_net_proto_text.bin CXX/LD -o .build_release/tools/finetune_net.bin CXX/LD -o .build_release/tools/net_speed_benchmark.bin CXX/LD -o .build_release/tools/train_net.bin CXX/LD -o .build_release/tools/caffe.bin CXX/LD -o .build_release/tools/convert_imageset.bin CXX/LD -o .build_release/tools/extract_features.bin CXX/LD -o .build_release/tools/device_query.bin CXX/LD -o .build_release/tools/test_net.bin CXX/LD -o .build_release/examples/cifar10/convert_cifar_data.bin CXX/LD -o .build_release/examples/mnist/convert_mnist_data.bin CXX/LD -o .build_release/examples/siamese/convert_mnist_siamese_data.bin CXX/LD -o python/caffe/_caffe.so python/caffe/_caffe.cpp touch python/caffe/proto/__init__.py PROTOC (python) src/caffe/proto/caffe.proto

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错误1：

LD -o .build_release/lib/libcaffe.so .build_release/src/caffe/layers/absval_layer.o: file not recognized: File truncated collect2: error: ld returned 1 exit status make: *** [.build_release/lib/libcaffe.so] Error 1 

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CXX src/caffe/layers/dummy_data_layer.cpp In file included from ./include/caffe/layer.hpp:8:0,                  from src/caffe/layers/relu_layer.cpp:4: ./include/caffe/blob.hpp:9:34: fatal error: caffe/proto/caffe.pb.h: No such file or directory  #include "caffe/proto/caffe.pb.h"                                   ^ compilation terminated. The bug is not reproducible, so it is likely a hardware or OS problem. make: *** [.build_release/src/caffe/layers/relu_layer.o] Error 1 make: *** Waiting for unfinished jobs.... In file included from ./include/caffe/fast_rcnn_layers.hpp:13:0,                  from src/caffe/layers/roi_pooling_layer.cpp:10: ./include/caffe/blob.hpp:9:34: fatal error: caffe/proto/caffe.pb.h: No such file or directory  #include "caffe/proto/caffe.pb.h"                                   ^ compilation terminated. The bug is not reproducible, so it is likely a hardware or OS problem. make: *** [.build_release/src/caffe/layers/roi_pooling_layer.o] Error 1

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~/fast-rcnn/caffe-fast-rcnn/src/caffe/proto$ protoc --cpp_out=/home/hello/fast-rcnn/caffe-fast-rcnn/include/caffe/ caffe.proto

sudo apt-get install libatlas-base-dev

三、训练检测

下载模型文件：

@ubuntu:~/fast-rcnn$ ./data/scripts/fetch_fast_rcnn_models.sh Downloading Fast R-CNN demo models (0.96G)... --2016-11-08 11:01:15--  http://www.cs.berkeley.edu/~rbg/fast-rcnn-data/fast_rcnn_models.tgz Resolving www.cs.berkeley.edu (www.cs.berkeley.edu)... 23.253.180.102 Connecting to www.cs.berkeley.edu (www.cs.berkeley.edu)|23.253.180.102|:80... connected. HTTP request sent, awaiting response... 302 Found Location: http://101.96.10.61/www.cs.berkeley.edu/~rbg/fast-rcnn-data/fast_rcnn_models.tgz [following] --2016-11-08 11:01:16--  http://101.96.10.61/www.cs.berkeley.edu/~rbg/fast-rcnn-data/fast_rcnn_models.tgz Connecting to 101.96.10.61:80... connected. HTTP request sent, awaiting response... 303 See Other Location: https://people.eecs.berkeley.edu/~rbg/fast-rcnn-data/fast_rcnn_models.tgz [following] 

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1、出现问题：

~/fast-rcnn/tools$ ./demo.py Traceback (most recent call last):   File "./demo.py", line 17, in <module>     from fast_rcnn.config import cfg   File "/home//fast-rcnn/tools/../lib/fast_rcnn/__init__.py", line 8, in <module>     from . import config   File "/home//fast-rcnn/tools/../lib/fast_rcnn/config.py", line 23, in <module>     from easydict import EasyDict as edict ImportError: No module named easydict 

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sudo pip install easydict

~/fast-rcnn/tools$ ./demo.py Traceback (most recent call last):   File "./demo.py", line 17, in <module>     from fast_rcnn.config import cfg   File "/home//fast-rcnn/tools/../lib/fast_rcnn/__init__.py", line 9, in <module>     from . import train   File "/home//fast-rcnn/tools/../lib/fast_rcnn/train.py", line 10, in <module>     import caffe   File "/home//fast-rcnn/tools/../caffe-fast-rcnn/python/caffe/__init__.py", line 1, in <module>     from .pycaffe import Net, SGDSolver   File "/home//fast-rcnn/tools/../caffe-fast-rcnn/python/caffe/pycaffe.py", line 14, in <module>     import caffe.io   File "/home//fast-rcnn/tools/../caffe-fast-rcnn/python/caffe/io.py", line 2, in <module>     import skimage.io ImportError: No module named skimage.io 

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sudo apt-get install python-numpy python-scipy python-matplotlib python-sklearn python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython

2、出现问题：

@ubuntu:~/fast-rcnn/tools$ ./demo.py WARNING: Logging before InitGoogleLogging() is written to STDERR F1108 15:18:01.710467 13445 common.cpp:55] Cannot use GPU in CPU-only Caffe: check mode. *** Check failure stack trace: *** Aborted (core dumped) 

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解决：

#if args.cpu_mode:         caffe.set_mode_cpu()     #else:         #caffe.set_mode_gpu()         #caffe.set_device(args.gpu_id)

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3、出现问题：

libprotobuf WARNING google/protobuf/io/coded_stream.cc:505] Reading dangerously large protocol message.  If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons.  To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. [libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 538766130   Loaded network /home//fast-rcnn/data/fast_rcnn_models/vgg16_fast_rcnn_iter_40000.caffemodel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Demo for data/demo/000004.jpg F1108 15:28:31.449816 13520 syncedmem.hpp:27] Check failed: *ptr host allocation of size 255744000 failed *** Check failure stack trace: *** Aborted (core dumped) 

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内存问题

测试结果：

Loaded network /home//fast-rcnn/data/fast_rcnn_models/vgg16_fast_rcnn_iter_40000.caffemodel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Demo for data/demo/000004.jpg Detection took 56.309s for 2888 object proposals All car detections with p(car | box) >= 0.8 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Demo for data/demo/001551.jpg Detection took 40.754s for 2057 object proposals All sofa detections with p(sofa | box) >= 0.8 All tvmonitor detections with p(tvmonitor | box) >= 0.8 

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1、http://blog.csdn.net/cyh_24/article/details/51440344

Department of Information Engineering, The Chinese University of Hong Kong

3、http://www.cnblogs.com/louyihang-loves-baiyan/p/4885659.html
https://github.com/rbgirshick/fast-rcnn.git

ubuntu:~$ git clone --recursive https://github.com/rbgirshick/fast-rcnn.git fatal: destination path 'fast-rcnn' already exists and is not an empty directory. @ubuntu:~$ rm -rf fast-rcnn/ @ubuntu:~$ git clone --recursive https://github.com/rbgirshick/fast-rcnn.git Cloning into 'fast-rcnn'... remote: Counting objects: 1269, done. remote: Compressing objects: 100% (3/3), done. remote: Total 1269 (delta 2), reused 2 (delta 2), pack-reused 1264 Receiving objects: 100% (1269/1269), 452.91 KiB | 27.00 KiB/s, done. Resolving deltas: 100% (793/793), done. Checking connectivity... done. Submodule 'caffe-fast-rcnn' (https://github.com/rbgirshick/caffe-fast-rcnn.git) registered for path 'caffe-fast-rcnn' Cloning into 'caffe-fast-rcnn'... remote: Counting objects: 23976, done. remote: Compressing objects: 100% (2/2), done. remote: Total 23976 (delta 0), reused 0 (delta 0), pack-reused 23974 Receiving objects: 100% (23976/23976), 31.60 MiB | 37.00 KiB/s, done. Resolving deltas: 100% (15681/15681), done. Checking connectivity... done. Submodule path 'caffe-fast-rcnn': checked out 'bcd9b4eadc7d8fbc433aeefd564e82ec63aaf69c'

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出现问题:

@ubuntu:~/fast-rcnn/lib$ make python setup.py build_ext --inplace Traceback (most recent call last):   File "setup.py", line 11, in <module>     from Cython.Distutils import build_ext ImportError: No module named Cython.Distutils make: *** [all] Error 1 

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解决：sudo apt-get install cython

sudo apt-get install protobuf-devel leveldb-devel snappy-devel opencv-devel boost-devel hdf5-devel

4、http://www.w2bc.com/article/125121
5、http://www.w2bc.com/article/168733
6、http://www.w2bc.com/article/128354
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

7、http://www.w2bc.com/article/120530
8、http://www.w2bc.com/article/136766
9、http://www.cnblogs.com/louyihang-loves-baiyan/p/5485955.html

10、https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus

11、http://blog.csdn.net/xyy19920105/article/details/50440957

12、http://m.blog.csdn.net/article/details?id=51036677

13、http://jingyan.baidu.com/article/eae07827f7f2d01fec5485f7.html

14、http://www.cnblogs.com/linkboy1980/p/5469994.html

15、http://blog.csdn.net/ture_dream/article/details/52758422

https://github.com/rbgirshick/fast-rcnn

Fast R-CNN: Fast Region-based Convolutional Networks for object detection

Introduction

Fast R-CNN is a fast framework for object detection with deep ConvNets. Fast R-CNN

trains state-of-the-art models, like VGG16, 9x faster than traditional R-CNN and 3x faster than SPPnet, runs 200x faster than R-CNN and 10x faster than SPPnet at test-time, has a significantly higher mAP on PASCAL VOC than both R-CNN and SPPnet, and is written in Python and C++/Caffe. 

License

Citing Fast R-CNN

If you find Fast R-CNN useful in your research, please consider citing:

}

Contents

Requirements: software Requirements: hardware Basic installation Demo Beyond the demo: training and testing Usage Extra downloads 

Requirements: software

Requirements for Caffe and pycaffe (see: Caffe installation instructions)  Note: Caffe must be built with support for Python layers!  # In your Makefile.config, make sure to have this line uncommented WITH_PYTHON_LAYER := 1  You can download my Makefile.config for reference. Python packages you might not have: cython, python-opencv, easydict [optional] MATLAB (required for PASCAL VOC evaluation only) 

Requirements: hardware

For training smaller networks (CaffeNet, VGG_CNN_M_1024) a good GPU (e.g., Titan, K20, K40, ...) with at least 3G of memory suffices For training with VGG16, you'll need a K40 (~11G of memory) 

Installation (sufficient for the demo)

Clone the Fast R-CNN repository  # Make sure to clone with --recursive git clone --recursive https://github.com/rbgirshick/fast-rcnn.git  We'll call the directory that you cloned Fast R-CNN into FRCN_ROOT  Ignore notes 1 and 2 if you followed step 1 above.  Note 1: If you didn't clone Fast R-CNN with the --recursive flag, then you'll need to manually clone the caffe-fast-rcnn submodule:  git submodule update --init --recursive  Note 2: The caffe-fast-rcnn submodule needs to be on the fast-rcnn branch (or equivalent detached state). This will happen automatically if you follow these instructions.  Build the Cython modules  cd $FRCN_ROOT/lib make  Build Caffe and pycaffe  cd $FRCN_ROOT/caffe-fast-rcnn # Now follow the Caffe installation instructions here: #   http://caffe.berkeleyvision.org/installation.html  # If you're experienced with Caffe and have all of the requirements installed # and your Makefile.config in place, then simply do: make -j8 && make pycaffe  Download pre-computed Fast R-CNN detectors  cd $FRCN_ROOT ./data/scripts/fetch_fast_rcnn_models.sh  This will populate the $FRCN_ROOT/data folder with fast_rcnn_models. See data/README.md for details. 

Demo

After successfully completing basic installation, you’ll be ready to run the demo.

Python

To run the demo

./tools/demo.py

The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007. The object proposals are pre-computed in order to reduce installation requirements.

MATLAB

There’s also a basic MATLAB demo, though it’s missing some minor bells and whistles compared to the Python version.

matlab # wait for matlab to start…

At the matlab prompt, run the script:

fast_rcnn_demo

Fast R-CNN training is implemented in Python only, but test-time detection functionality also exists in MATLAB. See matlab/fast_rcnn_demo.m and matlab/fast_rcnn_im_detect.m for details.

Computing object proposals

The demo uses pre-computed selective search proposals computed with this code. If you’d like to compute proposals on your own images, there are many options. Here are some pointers; if you run into trouble using these resources please direct questions to the respective authors.

Selective Search: original matlab code, python wrapper EdgeBoxes: matlab code GOP and LPO: python code MCG: matlab code RIGOR: matlab code 

Beyond the demo: installation for training and testing models

Download the training, validation, test data and VOCdevkit  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  Extract all of these tars into one directory named VOCdevkit  tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar  It should have this basic structure  $VOCdevkit/                           # development kit $VOCdevkit/VOCcode/                   # VOC utility code $VOCdevkit/VOC2007                    # image sets, annotations, etc. # ... and several other directories ...  Create symlinks for the PASCAL VOC dataset  cd $FRCN_ROOT/data ln -s $VOCdevkit VOCdevkit2007  Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects. [Optional] follow similar steps to get PASCAL VOC 2010 and 2012 Follow the next sections to download pre-computed object proposals and pre-trained ImageNet models 

Download pre-computed Selective Search object proposals

Pre-computed selective search boxes can also be downloaded for VOC2007 and VOC2012.

./data/scripts/fetch_selective_search_data.sh

Download pre-trained ImageNet models

Pre-trained ImageNet models can be downloaded for the three networks described in the paper: CaffeNet (model S), VGG_CNN_M_1024 (model M), and VGG16 (model L).

./data/scripts/fetch_imagenet_models.sh

Usage

Train a Fast R-CNN detector. For example, train a VGG16 network on VOC 2007 trainval:

If you see this error

EnvironmentError: MATLAB command ‘matlab’ not found. Please add ‘matlab’ to your PATH.

then you need to make sure the matlab binary is in your $PATH. MATLAB is currently required for PASCAL VOC evaluation.

Test a Fast R-CNN detector. For example, test the VGG 16 network on VOC 2007 test:

Test output is written underneath $FRCN_ROOT/output.

Compress a Fast R-CNN model using truncated SVD on the fully-connected layers:

Test the model you just compressed

Experiment scripts

Scripts to reproduce the experiments in the paper (up to stochastic variation) are provided in $FRCN_ROOT/experiments/scripts. Log files for experiments are located in experiments/logs.

Extra downloads

Experiment logs PASCAL VOC test set detections     voc_2007_test_results_fast_rcnn_caffenet_trained_on_2007_trainval.tgz     voc_2007_test_results_fast_rcnn_vgg16_trained_on_2007_trainval.tgz     voc_2007_test_results_fast_rcnn_vgg_cnn_m_1024_trained_on_2007_trainval.tgz     voc_2012_test_results_fast_rcnn_vgg16_trained_on_2007_trainvaltest_2012_trainval.tgz     voc_2012_test_results_fast_rcnn_vgg16_trained_on_2012_trainval.tgz Fast R-CNN VGG16 model trained on VOC07 train,val,test union with VOC12 train,val 

本文地址：http://blog.csdn.net/forest_world

文章来源: caffe-fast-rcnn（Caffe、FSRCNN、FastRCNN）