有用的blog

转载文章，著作权归作者所有。如有不妥之处，请联系删帖。

2018 paper:https://github.com/amusi/daily-paper-computer-vision

108关键点人脸识别：https://github.com/zeusees/HyperLandmark
vm配置：http://blog.csdn.net/u013142781/article/details/50529030
CMakeLists.txt：http://blog.csdn.net/z_h_s/article/details/50699905
莫烦python：https://morvanzhou.github.io/
廖雪峰git与python：https://www.liaoxuefeng.com/
～～ios上实现小规模yolo模型：http://colabug.com/107304.html
图像检测汇总：https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html#t-cnn
压缩caffemodel：https://github.com/RalphMao/demo_nn_compress
实时车辆检测：https://github.com/upul/CarND-Vehicle-Detection
MPII行人位姿数据集，可PK：http://human-pose.mpi-inf.mpg.de/#overview
识别领域汇总：http://www.cnblogs.com/zlslch/p/6970680.html
xnor采访名言:https://techcrunch.com/2017/01/19/xnor-ai-frees-ai-from-the-prison-of-the-supercomputer/
博客专栏：http://blog.csdn.net/column/mycolumn.html
多人位姿检测（骨骼点）2017：https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation
github 57个最火热的框架：http://www.oschina.net/news/79500/57-most-popular-deep-learning-project-at-github
人脸检测、arm加速caffe：https://github.com/OAID/FaceRecognition
关于人脸检测的几个框架:http://www.sohu.com/a/154168717_206784
聊天机器人：https://www.cnblogs.com/zdz8207/p/DeepLearning-tensorflow-chatbot.html
大牛：http://www.shareditor.com/blogshow/?blogId=121
语料数据集 https://github.com/candlewill/Dialog_Corpus
各种数据集:https://zhuanlan.zhihu.com/p/25138563
车辆检测（RepNet）：https://github.com/xuqiantong/RepNet
反卷积：https://www.zhihu.com/question/43609045
条形码检测识别：https://github.com/seajune/Barcode-Scanner
CVPR 2018：http://openaccess.thecvf.com/CVPR2018.py
BN层讲解：https://blog.csdn.net/myarrow/article/details/51848285
0、以后有用

Boosted Tree：一篇很有见识的文章 http://dataunion.org/15787.html

Kaggle—So Easy!百行代码实现排名Top 5%的图像分类比赛 ：
http://blog.csdn.net/v_july_v/article/details/71598551
卷积的尺度和位置不变形解析：https://www.quora.com/How-is-a-convolutional-neural-network-able-to-learn-invariant-features
1.LSTM与RNN

官方:http://colah.github.io/posts/2015-08-Understanding-LSTMs/
翻译:http://www.jianshu.com/p/9dc9f41f0b29
cs231n老师的博客:http://karpathy.github.io/2015/05/21/rnn-effectiveness/
这个是我见得写的最好的RNN，里面还有关于机器翻译，语音识别，语言模式，图像描述的论文，绝了：http://blog.csdn.net/heyongluoyao8/article/details/48636251
图像描述论文（cs231n的Andrej Karpathy）与github代码地址：
http://cs.stanford.edu/people/karpathy/deepimagesent/
https://github.com/karpathy/neuraltalk

2.YOLO2训练自己的数据集

http://lib.csdn.net/article/deeplearning/57863?knId=1726
http://blog.csdn.net/samylee/article/details/53414108
V1版:http://blog.csdn.net/sinat_30071459/article/details/53100791

3.faster训练自己的数据集

http://blog.csdn.net/sinat_30071459/article/details/51332084
http://blog.csdn.net/sinat_30071459/article/details/50723212

4、深度学习

类似cs231n：http://blog.csdn.net/u014595019/article/details/52571966
大牛：http://blog.csdn.net/xbinworld/article/details/44464663
大牛！全：http://blog.csdn.net/hjimce/article/details/49255013
基础深度学习，有代码：
https://www.zybuluo.com/hanbingtao/note/433855
Deep Learning Tutorials
http://www.deeplearning.net/tutorial/
BN层原理：http://www.cnblogs.com/stingsl/p/6428694.html

5.Theano学习

学习指南翻译（版本1）：
http://www.cnblogs.com/xueliangliu/archive/2013/04/03/2997437.html
学习指南翻译（版本2）：
http://www.cnblogs.com/charleshuang/p/3648804.html
官方：
http://deeplearning.net/software/theano/tutorial/
★github示例：
https://github.com/Newmu/Theano-Tutorials
★挺全的theano知识点讲解
http://www.cnblogs.com/shouhuxianjian/category/699462.html
theano函数官方文档：
http://deeplearning.net/software/theano/library/tensor/basic.html
简单易懂：
http://www.cnblogs.com/YiXiaoZhou/p/6079428.html
keras官方文档：http://keras-cn.readthedocs.io/en/latest/

6.python基础

super：http://www.cnblogs.com/lovemo1314/archive/2011/05/03/2035005.html
format:http://www.jb51.net/article/63672.htm
anaconda安装tensorflow：
http://www.cnblogs.com/aloiswei/p/6510355.html

7.lua

lua面向对象编程，写的很透彻：
http://www.jellythink.com/archives/529
.与：区别：
http://www.cnblogs.com/youxilua/archive/2011/07/28/2119059.html
元表方法：
http://www.cnblogs.com/JimLy-BUG/p/5364281.html

8.mxnet

https://github.com/dmlc/mxnet
BMNet：https://github.com/hpi-xnor/BMXNet

9.torch

卷积层源码：http://blog.csdn.net/shenxiaolu1984/article/details/52373174
tensor操作：http://blog.csdn.net/whgyxy/article/details/52204206
60分钟学torch：
原版：https://github.com/soumith/cvpr2015/blob/master/Deep Learning with Torch.ipynb
翻译：http://blog.csdn.net/baidu_17806763/article/details/61630538
torch学习资料：http://blog.csdn.net/victoriaw/article/details/71703568
torch学习系列（1），包含tensor操作：http://blog.csdn.net/whgyxy/article/category/6352333
torch学习系列（2）http://blog.csdn.net/u010946556/article/category/6216156
torch.nn模块学习（还有tensorflow、matlab、softmax问题）：http://blog.csdn.net/hejunqing14/article/category/6356970
torch学习系列（3）：http://blog.csdn.net/Hungryof/article/category/6245605
torch、自己建立一个层（如xnor）：https://zhuanlan.zhihu.com/p/21550685
官方：http://torch.ch/docs/developer-docs.html
一些torch小函数：http://blog.csdn.net/JIEJINQUANIL/article/category/5884857
关于torch建立层汇总：
http://blog.csdn.net/lanran2/article/details/50494570
http://lib.csdn.net/article/deeplearning/51259
http://www.cnblogs.com/crossing/p/4826668.html
http://torch.ch/docs/developer-docs.html

10.Tensorflow-faster

tensorflow与Keras（Keras中文官方文档）：https://keras-cn.readthedocs.io/en/latest/blog/keras_and_tensorflow/

Tensorflow-API：https://www.tensorflow.org/api_docs/
Faster-RCNN_TF:https://github.com/smallcorgi/Faster-RCNN_TF
tf-faster-rcnn:https://github.com/endernewton/tf-faster-rcnn
将tensorflow移植到android手机实现物体识别！！http://blog.csdn.net/xiaopihaierletian/article/details/61933695
tensorflow-API：http://www.jianshu.com/nb/5517733
常用函数：http://blog.csdn.net/lenbow/article/details/52152766
tensorflow基础学习博客：http://blog.csdn.net/u012436149/article/details/53018924
tensorflow应用较全：http://blog.csdn.net/helei001/article/details/51842531
11、faster

http://blog.csdn.net/wopawn/article/details/52223282
demo.py解析
论文解析，很好：http://blog.csdn.net/wopawn/article/details/52223282#reply
论文翻译，这人有一套rcnn博客：http://blog.csdn.net/u011534057/article/details/51259812
overfeat：http://blog.sciencenet.cn/blog-1583812-844178.html
rpn代码解析：http://blog.csdn.net/sloanqin/article/details/51545125

12、安装tcl/tk和Tkinter

http://blog.csdn.net/cryhelyxx/article/details/22514871

13、在阅读Faster-RCNN_TF代码中观看的博客：

os.path:http://book.51cto.com/art/201405/440066.htm
eastdict:https://pypi.python.org/pypi/easydict/
strip:http://www.cnblogs.com/itdyb/p/5046472.html
zip:http://www.cnblogs.com/frydsh/archive/2012/07/10/2585370.html
range与xrange:http://blog.csdn.net/ithomer/article/details/17285449
xml.etree.ElementTree:http://www.cnblogs.com/hongten/p/hongten_python_xml_etree_elementtree.html
xml文件解析：http://blog.csdn.net/zhangjunbob/article/details/52769381
http://blog.csdn.net/gingerredjade/article/details/21944675
@作用：http://blog.sina.com.cn/s/blog_571b19a001013h7j.html
stack:http://blog.csdn.net/huruzun/article/details/39801217
generate_anchors.py注释:http://blog.csdn.net/xzzppp/article/details/52317863
numpy.ravel() vs numpy.flatten() :http://blog.csdn.net/lanchunhui/article/details/50354978
meshgrid:http://blog.csdn.net/grey_csdn/article/details/69663432
anchor产生的问题：http://blog.csdn.net/zqjackking/article/details/59725989
bbox.pyx：http://blog.csdn.net/guotong1988/article/details/54729530
tf.nn.softmax_cross_entropy_with_logits：http://blog.csdn.net/mao_xiao_feng/article/details/53382790
tensor与ndarray操作与转换：http://blog.csdn.net/wyl1987527/article/details/62458057
★变量、相关变量初始化、保存：http://www.cnblogs.com/claude-gyh/p/6554322.html
saver：http://blog.csdn.net/u011500062/article/details/51728830
uninitial error:https://stackoverflow.com/questions/34001922/failedpreconditionerror-attempting-to-use-uninitialized-in-tensorflow
★与模型保存读取（saver）相关的一切操作：http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/
翻译：http://blog.163.com/wujiaxing009@126/blog/static/7198839920175671529472/
其余相关：http://www.cnblogs.com/azheng333/archive/2017/06/09/6972619.html
http://blog.csdn.net/thriving_fcl/article/details/71423039
http://www.jianshu.com/p/8487db911d9a
http://blog.csdn.net/u014659656/article/details/53954793
tf.trainable_variables与tf.all_variables:http://blog.csdn.net/uestc_c2_403/article/details/72356448
tf.gradients 与 tf.stop_gradient() :http://blog.csdn.net/u012436149/article/details/53905797

14、haar与adaboost

利用OpenCV自带的haar training程序训练分类器 ：http://blog.csdn.net/carson2005/article/details/8171571
http://blog.csdn.net/liulina603/article/details/8184451
haar原理：http://www.cnblogs.com/ello/archive/2012/04/28/2475419.html
http://blog.csdn.net/bbzz2/article/details/50764159
dlib+opencv人脸特征检测：http://blog.csdn.net/zmdsjtu/article/details/52422847
训练常见问题：http://www.cnblogs.com/chensheng-zhou/p/5542887.html

15、dlib array2d与Mat

https://stackoverflow.com/questions/29118317/how-to-convert-mat-to-array2drgb-pixel
http://blog.csdn.net/huixingshao/article/details/55510950
http://blog.csdn.net/qq_26671711/article/details/53713529
http://blog.csdn.net/yubin1277408629/article/details/53561037

16、三维重建

vcglib -github：https://github.com/cnr-isti-vclab/vcglib
win10+vs+cuda ：http://blog.csdn.net/u011821462/article/details/50145221
★三维可视化工程：http://redwood-data.org/indoor/tutorial.html
win10+vs2015 meshlab编译 ：http://blog.csdn.net/hanshuobest/article/details/71525388
windows-git：http://blog.csdn.net/qq_34698126/article/details/53521187

17、kinfu环境配置

kinfu安装配置全解：http://blog.csdn.net/alan_1550587588/article/details/54582192
VS2010和IVF2011的安装教程 Fortran：
http://wenku.baidu.com/view/9e28de1b01f69e314332949c.html
Fortran认证书Intel_Visual_Fortran_XE2011.lic：
http://pan.baidu.com/s/1kT9lR8r
ros下使用kinfu：http://blog.csdn.net/l_h2010/article/details/38349927

18、c++

c++笔记：http://www.cnblogs.com/ggjucheng/archive/2012/08/18/2645319.html
typedef：http://blog.csdn.net/ameyume/article/details/6326278
指针的指针：http://blog.jobbole.com/60647/
成员函数指针：http://blog.csdn.net/jinjinclouded/article/details/5189540
c++指针：http://www.cnblogs.com/ggjucheng/archive/2011/12/13/2286391.html
qt学习：http://www.kuqin.com/qtdocument/tutorial.html
argc和argv含义及用法 ：http://blog.csdn.net/dcrmg/article/details/51987413
虚函数与纯虚函数：http://blog.csdn.net/xwpc702/article/details/8670025
http://blog.csdn.net/hackbuteer1/article/details/7558868

19、ELL

安装中出现问题解决（中文）博客：https://www.codelast.com/原创-在ubuntu-14-04系统中为ell安装python-3-6-通过miniconda/
https://www.codelast.com/原创-在树莓派3上使用微软ell嵌入式学习库1/

20、光流测速度

http://blog.csdn.net/chentravelling/article/details/50924144
http://blog.csdn.net/taily_duan/article/details/51011288
http://blog.csdn.net/zouxy09/article/details/8683859
http://blog.csdn.net/dcrmg/article/details/52684477
http://blog.csdn.net/crzy_sparrow/article/details/7407604
加载深度信息求速度原理：

21、识别走过的弯路：

【人体姿态】Stacked Hourglass算法详解 （torch代码）：http://blog.csdn.net/shenxiaolu1984/article/details/51428392
Object Detection︱RCNN、faster-RCNN框架的浅读与延伸内容笔记 ：
http://blog.csdn.net/roslei/article/details/73459873
ipython-notebook caffe识别与可视化：http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb#Classification:-Instant-Recognition-with-Caffe

22、语义分割与识别

FCN-github：https://github.com/shelhamer/fcn.berkeleyvision.org
fcn-知乎：https://zhuanlan.zhihu.com/p/22976342
FCN-CRF：https://zhuanlan.zhihu.com/p/22308032

23、deep compression与SqueezeNet

SqueezeNet-github:https://github.com/DeepScale/SqueezeNet
Deep Compression阅读理解及Caffe源码修改 ：http://blog.csdn.net/may0324/article/details/52935869
阅读理解：
http://blog.csdn.net/zijin0802034/article/details/53982812
http://blog.csdn.net/boon_228/article/details/51718521
http://blog.csdn.net/cyh_24/article/details/51708469
http://blog.csdn.net/joshuaxx316/article/details/52514978
压缩caffemodel：https://github.com/RalphMao/demo_nn_compress
SqueezeNet+Faster-RCNN+OHEM：http://blog.csdn.net/u011956147/article/details/53714616

24、TFFRCNN（基于Faster-RCNN_TF修改）

https://github.com/CharlesShang/TFFRCNN，内含PVANET模型
运行TFFRCNN：http://blog.csdn.net/yuexin2/article/details/75019330
TFRRCNN训练自己的模型：http://blog.csdn.net/yuexin2/article/details/75025720

25、嵌入式网络

CNN 模型压缩与加速算法综述 :http://blog.csdn.net/QcloudCommunity/article/details/77719498

YOLO对比SVM+HOG 耗时更短 处理更精确（附github资源）：http://www.weiot.net/article-59672.html
LCDet：http://blog.csdn.net/zhangjunhit/article/details/72831440
PVANet（TFFRCNN中有）：https://arxiv.org/pdf/1608.08021.pdf
http://blog.csdn.net/wydbyxr/article/details/68931337
mobilenet：http://blog.csdn.net/zchang81/article/details/73321202
//（有实现链接）http://blog.csdn.net/mao_feng/article/details/75116085
//论文翻译：https://baijiahao.baidu.com/s?id=1566004753349359&wfr=spider&for=pc
ARM-NEON：http://blog.csdn.net/qiek/article/details/50900890
http://blog.csdn.net/ljp12345/article/details/53490094
popcnt：http://www.cnblogs.com/zyl910/archive/2012/11/02/testpopcnt.html
26、有用知乎

https://www.zhihu.com/question/39921464
https://www.zhihu.com/question/53697001
https://www.zhihu.com/question/27872849

27.卷积加速相关

http://blog.csdn.net/mao_kun/article/details/54882528
http://blog.csdn.net/u010620604/article/details/52464529

28.CMakeLists

http://www.360doc.com/content/12/0507/10/9369336_209205930.shtml
http://blog.csdn.net/zhubaohua_bupt/article/details/52760411

29.强化学习

david 9:http://nooverfit.com/wp/15-增强学习101-闪电入门-reinforcement-learning/
精彩:http://www.cnblogs.com/jinxulin/p/3517377.html
知乎大神:https://www.zhihu.com/question/41775291

30、3D-deeplearning-face_detector

https://zhuanlan.zhihu.com/p/24816781

31、xnor相关工程与讨论

https://github.com/allenai/XNOR-Net/issues

https://github.com/jiecaoyu/XNOR-Net-PyTorch
https://github.com/tensorflow/tensorflow/issues/1592
https://github.com/MatthieuCourbariaux/BinaryNet
https://github.com/loswensiana/BWN-XNOR-caffe/tree/master/examples/imagenet
https://github.com/tensorflow/tensorflow/tree/master/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint
☆修改梯度相关 http://blog.csdn.net/buyi_shizi/article/details/51512848
http://blog.csdn.net/yihaizhiyan/article/details/44159063
32、FCN训练自己的数据集

详细版本：http://blog.csdn.net/jiongnima/article/details/78549326?locationNum=1&fps=1
http://blog.csdn.net/zoro_lov3/article/details/74550735
http://blog.csdn.net/z13653662052/article/details/70949440
http://blog.csdn.net/supe_king/article/details/58121993
http://blog.csdn.net/u010402786/article/details/72883421
http://www.cnblogs.com/xuanxufeng/p/6243342.html
https://github.com/315386775/FCN_train
https://github.com/msracver/FCIS

33、服务器搭建

http://blog.csdn.net/totodum/article/details/51059380
http://blog.csdn.net/fishman_yinwang/article/details/78029309
http://blog.csdn.net/jiaojialulu/article/details/77430563
34、推荐系统
https://github.com/jfkirk/tensorrec
https://github.com/bnak/Recommendations_Engine
https://github.com/songgc/TF-recomm
https://github.com/sonyisme/keras-recommendation
https://github.com/geeky-bit/Recommendation_systems-using-SVD-KNN-etc
https://github.com/amzn/amazon-dsstne
recommendation decomposition

https://www.douban.com/note/510047571/
https://github.com/Lockvictor/MovieLens-RecSys

http://blog.csdn.net/u011467621/article/details/48624973
https://github.com/joeyqzhou/recommendation-system

讲解：http://blog.csdn.net/joycewyj/article/details/51692976

https://github.com/chengstone/movie_recommender
http://blog.csdn.net/chengcheng1394/article/details/78820529

35、caffe

https://github.com/loswensiana/BWN-XNOR-caffe
添加新层：
https://blog.csdn.net/shuzfan/article/details/51322976（cutoff）
https://blog.csdn.net/wfei101/article/details/76735760（盗版）
https://blog.csdn.net/kuaitoukid/article/details/41865803（maxout与NIN）
https://blog.csdn.net/xizero00/article/details/52529341（图像缩放）
https://blog.csdn.net/happyflyy/article/details/54866037（maxout）
caffe函数解析：
https://blog.csdn.net/seven_first/article/details/47378697

caffe卷积实现原理：
https://blog.csdn.net/jiongnima/article/details/69055941
im2col原理：
https://blog.csdn.net/jiongnima/article/details/69736844

36、TernausNet

https://github.com/ternaus/TernausNet
https://github.com/ternaus/robot-surgery-segmentation

37、caffe添加新层与实现原理

很多篇都有用：https://blog.csdn.net/tangwei2014?viewmode=contents
添层（facenet）：https://blog.csdn.net/langb2014/article/details/50489305
https://blog.csdn.net/seven_first/article/details/50272005
caffe源码解析：https://blog.csdn.net/seven_first/article/category/5721883

38、人脸画像

https://github.com/BoyuanJiang/Age-Gender-Estimate-TF

39、tensorflow-model2caffemodel

https://blog.csdn.net/jiongnima/article/category/7245212

40、聊天机器人汇总

https://github.com/fateleak/awesome-chatbot-list

41、SGM-NET:

github:
https://github.com/dhernandez0/sgm
https://github.com/fixstars/libSGM
https://github.com/WanchaoYao/SGM
博客：
https://blog.csdn.net/zilanpotou182/article/details/73382412
https://blog.csdn.net/wsj998689aa/article/details/49464017
https://www.cnblogs.com/hrlnw/p/4746170.html
https://blog.csdn.net/qq_31785865/article/details/78451880
https://blog.csdn.net/laobai1015/article/details/52527233
http://lib.csdn.net/article/datastructure/10208

42、caffe矩阵操作

cblas_sgemm：https://blog.csdn.net/ZhikangFu/article/details/78258393
conv层讲解：https://blog.csdn.net/sinat_22336563/article/details/69808612
caffe讲解全（1）：https://blog.csdn.net/seven_first/article/details/47378697#1-caffecpugemm-函数
caffe讲解全（2）：https://blog.csdn.net/langb2014/article/details/51558515

caffe讲解全：https://blog.csdn.net/jiongnima/article/category/6436731

43、xnor新理解与新工程

pyrorch：https://github.com/jiecaoyu/XNOR-Net-PyTorch/tree/7f84f1bf6921bc99b05bbc869dddf2bd64989a40
tensorflow：https://github.com/AngusG/tensorflow-xnor-bnn
问题：https://github.com/jiecaoyu/XNOR-Net-PyTorch/issues/13
反向传播：https://github.com/jiecaoyu/XNOR-Net-PyTorch/blob/master/notes/notes.pdf

44、多线程

boost::thread
https://www.jianshu.com/p/0b2e360243f4
https://www.jianshu.com/p/30e79d2a8f7d
https://blog.csdn.net/zhuxiaoyang2000/article/details/6588031/

pthread
http://www.runoob.com/cplusplus/cpp-multithreading.html
https://blog.csdn.net/hudashi/article/details/7709413
http://www.docin.com/p-65547314.html
https://www.ibm.com/developerworks/cn/linux/l-pthred/index.html
http://www.cppblog.com/saha/articles/189802.html

45、NEON

https://download.csdn.net/download/jieweijason/10200330
https://developer.arm.com/technologies/neon/intrinsics
https://blog.csdn.net/may0324/article/details/72847800
https://www.jianshu.com/p/68879baa7c1f?from=timeline&isappinstalled=0

https://www.cnblogs.com/hrlnw/p/3767853.html
https://www.cnblogs.com/hrlnw/p/3723072.html

46、MobileNet

prototxt：
https://github.com/suzhenghang/MobileNetv2/tree/master/.gitignore
https://github.com/austingg/MobileNet-v2-caffe
mobilenet-sdd：
https://github.com/PINTO0309/MobileNet-SSD
https://github.com/PINTO0309/MobileNet-SSDLite-RealSense-TF

47、MobileNet/shuffleNet with YOLO/SSD

https://github.com/eric612/MobileNet-YOLO
https://github.com/PINTO0309/MobileNet-SSD
https://github.com/chuanqi305/MobileNet-SSD
https://github.com/chuanqi305/MobileNetv2-SSDLite
https://github.com/FreeApe/VGG-or-MobileNet-SSD
https://github.com/farmingyard/ShuffleNet
https://github.com/linchaozhang/shufflenet-ssd

https://github.com/chuanqi305/SqueezeNet-SSD
https://github.com/ChenYingpeng/caffe-ssd-frameworks

48 嵌入式/压缩神经网络相关工作汇总

https://www.ctolib.com/ZhishengWang-Embedded-Neural-Network.html

49、数据增强

gamma矫正：https://blog.csdn.net/linqianbi/article/details/78617615
仿射变化：https://blog.csdn.net/wsx_9999/article/details/80441125

原文链接: http://blog.csdn.net/l297969586/article/details/71159675
页面链接: https://unordered.org/timelines/59edf552e4801000