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+**致谢：该代码是对yjxiong代码的改进，弥补了里面的些许错误，非常感谢yjxiong的开源精神**
+###**前言**：
+该代码是在caffe框架运行，是yjxiong的https://github.com/yjxiong/caffe 的一些改进，caffe是用的他的修改版，即里面包含了openmpi多线程，具体使用方法参考上面的网址。
+由于他的caffe里面东西较多，在亲自移植代码后，发现有许多小错误，对其进行了修改。
+该代码在移植的过程中，使用了4块GTX1080Ti的显卡。
+###**使用**：
+大部分跟yjxiong所言一致，只是有部分地方进行了修正。
+
+- models/action_recognition文件夹下有多个prototxt文件，其中flow.solver文件有两个，New版本是我修改后的版本，是与论文一致的版本。不带New的版本是原版的solver文件。区别在于是否将图片resize成340*256和scale_ratios是否含有0.66。原版将其省略，而我将其加上。虽然准确率并没有太大的变化。因此，**使用带New的**.
+ 
+- action_python/下有许多测试文件，测试temporal net使用**demoTemporal.py**，测试spatial net使用**demoSpatial.py**。测试temporal+spatial使用**demoTemporalSpatial.py**。文件全部调用的是VideoSpatialPredictionTest.py 和VideoTemporalPredictionTest.py 。原版的有一些错误，因此改成使用后缀有Test的。
+
+- examples/action_recognition/dataset_file_examples/ 下的txt文件修改。因为用之前我的github中的denseflow提取出的rgb图和optical flow 图的个数与txt中的个数不能对应。对原版的txt文件中视频提取图片的帧数进行了修改。否则，若提取的图片小于txt文件中的视频帧数，网络输入图片的时候，将会出现找不到某些图片的报错。**因此，使用后缀带new的。**
+
+上面三个是本系统的关键，另外还对caffe的源码进行了修改，这跟https://github.com/yjxiong/caffe 所说的修改是一致的。
+
+
+最终可以根据本代码直接进行，进行训练的时候，在caffe根目录下，输入mpirun -np 4 ./install/bin/caffe train --solver=<Your Solver File>  -weights=< Pretrained caffemodel>即可运行。
+(当然，要提取安装openmpi)。
+
+具体的准确率情况参考csdn博客:http://blog.csdn.net/small_ARM/article/details/78283205
+
+
+###**Citation**
+
+You are encouraged to also cite one of the following papers if you find this repo helpful
+
+
+
+> @article{MultiGPUCaffe2015,
+  author    = {Limin Wang and
+               Yuanjun Xiong and
+               Zhe Wang and
+               Yu Qiao},
+  title     = {Towards Good Practices for Very Deep Two-Stream ConvNets},
+  journal   = {CoRR},
+  volume    = {abs/1507.02159},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1507.02159},
+}
+
+
+
+
+
+
+
+Following is the original README of Caffe.
+
+###**Caffe**
+
+
+Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.
+
+Check out the project site for all the details like
+
+    DIY Deep Learning for Vision with Caffe
+    Tutorial Documentation
+    BVLC reference models and the community model zoo
+    Installation instructions
+
+and step-by-step examples.
+
+Join the chat at https://gitter.im/BVLC/caffe
+
+Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.
+
+Happy brewing!
+###**License and Citation**
+
+Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.
+
+Please cite Caffe in your publications if it helps your research:
+
+> @article{jia2014caffe,
+  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
+  Journal = {arXiv preprint arXiv:1408.5093},
+  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
+  Year = {2014}
+}