增加 Caffe SSD 目标检测.

Signed-off-by: rick.chan <chenyang@autoai.com>

Software/Applications/Caffe/Caffe_SSD_目标检测.md

@@ -0,0 +1,112 @@
+# Caffe SSD 目标检测
+
+基于 Ubuntu16.04 和 Python2.7。
+
+## 1.Installation
+
+按《Ubuntu 初始配置》中的方法来安装开发工具。
+
+1. Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT
+
+    ```bash
+    git clone https://github.com/weiliu89/caffe.git
+    cd caffe
+    git checkout ssd
+    ```
+
+2. Build the code. Please follow Caffe instruction to install all necessary packages and build it.
+
+    ```bash
+    # Modify Makefile.config according to your Caffe installation.
+    cp Makefile.config.example Makefile.config
+    # 通过去除 Makefile.config 中“CPU_ONLY := 1”前面的 “#” 号可选择编译 CPU 版本的 Caffe
+    make -j8
+    # Make sure to include $CAFFE_ROOT/python to your PYTHONPATH.
+    make py
+    make test -j8
+    # (Optional)
+    make runtest -j8
+    cd caffe/python
+    for req in $(cat requirements.txt); do pip install $req; done
+    pip install -r requirements.txt
+    export PATHONPATH=$PATHONPATH:$CAFFE_ROOT/python/caffe
+    make pycaffe
+    # 验证 pycaffe 接口
+    python
+    >>>import caffe
+    >>>exit()
+    ```
+
+## 2.Preparation
+
+1. Download [fully convolutional reduced (atrous) VGGNet](https://gist.github.com/weiliu89/2ed6e13bfd5b57cf81d6). By default, we assume the model is stored in $CAFFE_ROOT/models/VGGNet/
+2. Download [VOC2007 and VOC2012 dataset](https://pjreddie.com/projects/pascal-voc-dataset-mirror/). By default, we assume the data is stored in $HOME/data/
+
+    ```bash
+    # Download the data.
+    cd $HOME/data
+    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+    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
+    # Extract the data.
+    tar -xvf VOCtrainval_11-May-2012.tar
+    tar -xvf VOCtrainval_06-Nov-2007.tar
+    tar -xvf VOCtest_06-Nov-2007.tar
+    ```
+
+3. Create the LMDB file.
+
+## 3.Train/Eval
+
+1. Train your model and evaluate the model on the fly.
+
+    ```bash
+    # It will create model definition files and save snapshot models in:
+    #   - $CAFFE_ROOT/models/VGGNet/VOC0712/SSD_300x300/
+    # and job file, log file, and the python script in:
+    #   - $CAFFE_ROOT/jobs/VGGNet/VOC0712/SSD_300x300/
+    # and save temporary evaluation results in:
+    #   - $HOME/data/VOCdevkit/results/VOC2007/SSD_300x300/
+    # It should reach 77.* mAP at 120k iterations.
+    python examples/ssd/ssd_pascal.py
+    ```
+
+    If you don't have time to train your model, you can download a pre-trained model at [here](https://drive.google.com/open?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA).
+
+2. Evaluate the most recent snapshot.
+
+    ```bash
+    # If you would like to test a model you trained, you can do:
+    python examples/ssd/score_ssd_pascal.py
+    ```
+
+3. Test your model using a webcam. Note: press "esc" to stop.
+
+    ```bash
+    # If you would like to attach a webcam to a model you trained, you can do:
+    python examples/ssd/ssd_pascal_webcam.py
+    ```
+
+    [Here](https://drive.google.com/file/d/0BzKzrI_SkD1_R09NcjM1eElLcWc/view) is a demo video of running a SSD500 model trained on [MSCOCO](http://mscoco.org/) dataset.
+
+4. Check out [examples/ssd_detect.ipynb](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd_detect.ipynb) or [examples/ssd/ssd_detect.cpp](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd/ssd_detect.cpp) on how to detect objects using a SSD model. Check out [examples/ssd/plot_detections.py](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd/plot_detections.py) on how to plot detection results output by ssd_detect.cpp.
+5. To train on other dataset, please refer to data/OTHERDATASET for more details. We currently add support for COCO and ILSVRC2016. We recommend using [examples/ssd.ipynb](https://hub.fastgit.org/weiliu89/caffe/blob/ssd/examples/ssd_detect.ipynb) to check whether the new dataset is prepared correctly.
+
+## 4.Models
+
+We have provided the latest models that are trained from different datasets. To help reproduce the results in [Table 6](https://arxiv.org/pdf/1512.02325v4.pdf), most models contain a pretrained `.caffemodel` file, many `.prototxt` files, and python scripts.
+
+1. PASCAL VOC models:
+   * 07+12: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_WVVTSmQxU0dVRzA), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_ZDIxVHBEcUNBb2s)
+   * 07++12: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_WnR2T1BGVWlCZHM), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_MjFjNTlnempHNWs)
+   * COCO[1]: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_NDlVeFJDc2tIU1k), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_TW4wTC14aDdCTDQ)
+   * 07+12+COCO: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_UFpoU01yLS1SaG8), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_X3ZXQUUtM0xNeEk)
+   * 07++12+COCO: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_TkFPTEQ1Z091SUE), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_NVVNdWdYNEh1WTA)
+
+2. COCO models:
+   * trainval35k: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_dUY1Ml9GRTFpUWc), [SSD512*](https://drive.google.com/open?id=0BzKzrI_SkD1_dlJpZHJzOXd3MTg)
+
+3. ILSVRC models:
+   * trainval1: [SSD300*](https://drive.google.com/open?id=0BzKzrI_SkD1_a2NKQ2d1d043VXM), [SSD500](https://drive.google.com/open?id=0BzKzrI_SkD1_X2ZCLVgwLTgzaTQ)
+
+[1]We use [`examples/convert_model.ipynb`](https://github.com/weiliu89/caffe/blob/ssd/examples/convert_model.ipynb) to extract a VOC model from a pretrained COCO model.

Software/Development/Language/Python/Package/Python_包.md

@@ -5,9 +5,9 @@
 一般可到 <https://pypi.org> 搜索 Python 包，通过：
 
 ```bash
-pip2 install <package name> -i http://mirrors.aliyun.com/pypi/simple/
+pip2 install <package name>[==<version>] -i http://mirrors.aliyun.com/pypi/simple/
 # 或
-pip3 install <package name> -i https://pypi.mirrors.ustc.edu.cn/simple/
+pip3 install <package name>[==<version>] -i https://pypi.mirrors.ustc.edu.cn/simple/
 ```
 
 来安装包。

Software/System/Linux/Editions/Ubuntu/Ubuntu_初始配置.md

@@ -106,7 +106,7 @@ dpkg -l
 ## 安装开发工具
 
 ```bash
-sudo apt-get install bison build-essential make gcc gcc-multilib global git-core git openssl module-init-tools gnu-efi xz-utils debianutils iputils-ping e2fslibs-dev ccache gawk wget diffstat bc zip unzip chrpath socat texinfo cpio flex minicom xterm gtkterm parted gparted tmux python python-pip python-wand python-crypto python3 python3-pip python3-pexpect libncurses-dev libssl-dev libpciaccess-dev uuid-dev libsystemd-dev libevent-dev libxml2-dev libusb-1.0-0-dev liblz4-tool libsdl1.2-dev libssl-dev libblkid-dev libcv-dev libopencv-*-dev protobuf-c-compiler protobuf-compiler libprotobuf-dev libboost-dev libleveldb-dev libgflags-dev libgoogle-glog-dev libblas-dev liblmdb-dev libsnappy-dev libopenblas-dev python-numpy libboost-python-dev
+sudo apt-get install bison build-essential make gcc gcc-multilib global git-core git openssl module-init-tools gnu-efi xz-utils debianutils iputils-ping e2fslibs-dev ccache gawk wget diffstat bc zip unzip chrpath socat texinfo cpio flex minicom xterm gtkterm parted gparted tmux python python-pip python-wand python-crypto python3 python3-pip python3-pexpect libncurses-dev libssl-dev libpciaccess-dev uuid-dev libsystemd-dev libevent-dev libxml2-dev libusb-1.0-0-dev liblz4-tool libsdl1.2-dev libssl-dev libblkid-dev libcv-dev libopencv-*-dev protobuf-c-compiler protobuf-compiler libprotobuf-dev libboost-dev libleveldb-dev libgflags-dev libgoogle-glog-dev libblas-dev liblmdb-dev libsnappy-dev libopenblas-dev python-numpy libboost-python-dev gfortran python-scikits-learn python-skimage-lib
 
 sudo pip3 install kconfiglib
 ```