官网：github.com/rbgirshick/fast-rcnn
结合博客：blog.csdn.net/samylee/article/details/50965935
注意：Fast RCNN的github代码已经不再进行维护了，更新的工作请看Faster RCNN的网站
但是此处仅仅是为了熟悉一下Fast RCNN
此处先严格按照官网的步骤来：
１、Clone the Fast R-CNN repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/rbgirshick/fast-rcnn.git＼
２、Build the Cython modules
cd $FRCN_ROOT/lib
make
３、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
注意：make -j8 #反正由于cudnn版本的问题，还是会有一些问题,解决方法：blog.csdn.net/u011070171/article/details/52399317中的差不多的替换文件即可类似的去解决
４、Download pre-computed Fast R-CNN detectors
cd $FRCN_ROOT
./data/scripts/fetch_fast_rcnn_models.sh
#下载模型，注意以下命令几乎非常慢，用下载网址：（链接：pan.baidu.com/s/1pJVburD 密码：11m0）可以下载fetch_fast_rcnn_models
downloading...
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看./tools/demo.py
结论：
（１）为了避免不必要的麻烦，什么该放在什么文件夹下都应该遵守，否则容易出错，也浪费时间去找。
（２）果然事先每一张图片的proposals信息都是记录好的，比如说：～/vision/fast-rcnn/data/demo/000004_boxes.mat，每行对应有４个值，应该是位置信息；
（３）scores, boxes = im_detect(net, im, obj_proposals)，网络检测结束之后，返回的是 scores, boxes两个张量，解析如下代码：
CONF_THRESH = 0.8 ＃应该是在非极大值抑制之前，选择要保留下来的框
NMS_THRESH = 0.3 ＃非极大值抑制的阀值
for cls in classes: ＃针对每一类做nms
cls_ind = CLASSES.index(cls) #背景对应的 cls_ind = 0
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] ＃所以boxes的格式应该是Ｎ×（４×（k+1））,N是proposals的个数
＃下面几行就是非极大值抑制了
cls_scores = scores[:, cls_ind]
keep = np.where(cls_scores >= CONF_THRESH)[0]
cls_boxes = cls_boxes[keep, :]
cls_scores = cls_scores[keep]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls,CONF_THRESH)
vis_detections(im, cls, dets, thresh=CONF_THRESH)＃可视化置信度大于0.5的框
对于background的那类，至今不知道得出boxes之后是如何处理的
vgg_cnn_m_1024：是VGG16的中等模型，比VGG16小很多
Computing object proposals暂时没看如何使用
Beyond the demo: installation for training and testing models
１、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
２、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
３、It should have this basic structure
$VOCdevkit/ # development kit
$VOCdevkit/VOCcode/ # VOC utility code
$VOCdevkit/VOC2007 # image sets, annotations, etc.
# ... and several other directories …
４、Create symlinks for the PASCAL VOC dataset
cd $FRCN_ROOT/data
ln -s $VOCdevkit VOCdevkit2007
我是：ln -s /home/echo/vision/Data/VOCdevkit /home/echo/vision/fast-rcnn/data/VOCdevkit2007
注意：Using symlinks is a good idea because you will likely want to share the same PASCAL dataset installation between multiple projects.
Download pre-computed Selective Search object proposals
cd $FRCN_ROOT
./data/scripts/fetch_selective_search_data.sh
This will populate the $FRCN_ROOT/data folder with selective_selective_data.
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).
cd $FRCN_ROOT
./data/scripts/fetch_imagenet_models.sh
These models are all available in the Caffe Model Zoo, but are provided here for your convenience.
#注意：以上都不是很好下载，就算不下载，Selective Search object proposals就是VOC图片集的预先生成的proposals而已， ImageNet models就是之前预先的分类网络。继续看用法：
Usage
Train a Fast R-CNN detector. For example, train a VGG16 network on VOC 2007 trainval:
./tools/train_net.py --gpu 0 --solver models/VGG16/solver.prototxt --weights data/imagenet_models/VGG16.v2.caffemodel
看./tools/train_net.py，其中的/home/echo/vision/fast-rcnn/lib/fast_rcnn/config.py中就是对各种关于网络的参数的设计
train_net.py 中，imdb = get_imdb(args.imdb_name)＃不是很懂，哦～原来上面有default='voc_2007_trainval', type=str，眼睛太小，之前没有看见呀～～～但是还是不知道数据的路径设置在哪
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...巴拉...巴拉
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后面一直往下看，也没啥特别的，估计自己不做，是找不出具体问题的，做起来——》
接着飞机的数据往下做：
其实所有的，我只需要去替换框架中的数据即可，这样会很简单。人家什么样子，我就提供什么样子的数据即可呀！但是这太肤浅了吧～～