完善修正内容.

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

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

@@ -14,7 +14,14 @@
     git checkout ssd
     ```
 
-2. Build the code. Please follow Caffe instruction to install all necessary packages and build it.
+2. 修改 Makefile 文件：
+
+    ```Makefile
+    -LIBRARIES += glog gflags protobuf boost_system boost_filesystem boost_regex m hdf5_hl hdf5
+    +LIBRARIES += glog gflags protobuf boost_system boost_filesystem boost_regex m hdf5_serial_hl hdf5_serial
+    ```
+
+3. Build the code. Please follow Caffe instruction to install all necessary packages and build it.
 
     ```bash
     # Modify Makefile.config according to your Caffe installation.
@@ -55,11 +62,52 @@
     tar -xvf VOCtest_06-Nov-2007.tar
     ```
 
-3. Create the LMDB file.
+3. 修改 data/VOC0712/create_list.sh 文件：
+
+    ```bash
+    -root_dir=$HOME/data/VOCdevkit/
+    +root_dir=/mnt/workspace/caffe/VOC/VOCdevkit/
+    ```
+
+4. 修改 data/VOC0712/create_data.sh 文件：
+
+    ```bash
+    -data_root_dir="$HOME/data/VOCdevkit"
+    +data_root_dir="/mnt/workspace/caffe/VOC/VOCdevkit"
+    ```
+
+5. Create the LMDB file.
+
+    ```bash
+    cd $CAFFE_ROOT
+    # Create the trainval.txt, test.txt, and test_name_size.txt in data/VOC0712/
+    ./data/VOC0712/create_list.sh
+    # You can modify the parameters in create_data.sh if needed.
+    # It will create lmdb files for trainval and test with encoded original image:
+    #   - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_trainval_lmdb
+    #   - $HOME/data/VOCdevkit/VOC0712/lmdb/VOC0712_test_lmdb
+    # and make soft links at examples/VOC0712/
+    ./data/VOC0712/create_data.sh
+    ```
 
 ## 3.Train/Eval
 
-1. Train your model and evaluate the model on the fly.
+1. 修改 examples/ssd/ssd_pascal.py 文件：
+
+    ```python
+    -if num_gpus > 0:
+    -  batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))
+    -  iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
+    -  solver_mode = P.Solver.GPU
+    -  device_id = int(gpulist[0])
+    +#if num_gpus > 0:
+    +#  batch_size_per_device = int(math.ceil(float(batch_size) / num_gpus))
+    +#  iter_size = int(math.ceil(float(accum_batch_size) / (batch_size_per_device * num_gpus)))
+    +#  solver_mode = P.Solver.GPU
+    +#  device_id = int(gpulist[0])
+    ```
+
+2. Train your model and evaluate the model on the fly.
 
     ```bash
     # It will create model definition files and save snapshot models in:
@@ -74,14 +122,14 @@
 
     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.
+3. 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.
+4. 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:
@@ -90,8 +138,8 @@
 
     [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.
+5. 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.
+6. 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