diff --git a/fluid/image_classification/caffe2fluid/.gitignore b/fluid/image_classification/caffe2fluid/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..0289f964a81f92cde27b2ef63198ae445cdcffad
--- /dev/null
+++ b/fluid/image_classification/caffe2fluid/.gitignore
@@ -0,0 +1,2 @@
+proto/caffepb.py
+proto/caffe_pb2.py
diff --git a/fluid/image_classification/caffe2fluid/README.md b/fluid/image_classification/caffe2fluid/README.md
index dae7d05d1e41ee47a87630eaa7c5321c2e9ec270..98173775c9f37e990da9f3a7536bb00ade5b66e1 100644
--- a/fluid/image_classification/caffe2fluid/README.md
+++ b/fluid/image_classification/caffe2fluid/README.md
@@ -8,10 +8,10 @@ This tool is used to convert a Caffe model to a Fluid model
 
 3. Caffe's customized layers convertion also be supported by extending this tool
 
-4. A bunch of tools in '*examples/imagenet/tools*' are provided to compare the difference
+4. A bunch of tools in `examples/imagenet/tools` are provided to compare the difference
 
 ### HowTo
-1. Prepare caffepb.py in ./proto if your python has no 'pycaffe' module, two options provided here:
+1. Prepare `caffepb.py` in `./proto` if your python has no `pycaffe` module, two options provided here:
     - Generate pycaffe from caffe.proto
         ```
         bash ./proto/compile.sh
@@ -24,37 +24,39 @@ This tool is used to convert a Caffe model to a Fluid model
 
 2. Convert the Caffe model to Fluid model
    - Generate fluid code and weight file
-        ```
-        python convert.py alexnet.prototxt \
-                --caffemodel alexnet.caffemodel \
-                --data-output-path alexnet.npy \
-                --code-output-path alexnet.py
-     ```
-
-    - Save weights as fluid model file
-        ```
-        python alexnet.py alexnet.npy ./fluid #only infer the last layer's result
-        python alexnet.py alexnet.npy ./fluid fc8,prob #infer these 2 layer's result
-      ```
+       ```
+       python convert.py alexnet.prototxt \
+               --caffemodel alexnet.caffemodel \
+               --data-output-path alexnet.npy \
+               --code-output-path alexnet.py
+       ```
+
+   - Save weights as fluid model file
+       ```
+       # only infer the last layer's result
+       python alexnet.py alexnet.npy ./fluid
+       # infer these 2 layer's result
+       python alexnet.py alexnet.npy ./fluid fc8,prob
+       ```
 
 3. Use the converted model to infer
-    - See more details in '*examples/imagenet/tools/run.sh*'
+    - See more details in `examples/imagenet/tools/run.sh`
 
 4. Compare the inference results with caffe
-    - See more details in '*examples/imagenet/tools/diff.sh*'
+    - See more details in `examples/imagenet/tools/diff.sh`
 
 ### How to convert custom layer
-1. Implement your custom layer in a file under '*kaffe/custom_layers*', eg: mylayer.py
+1. Implement your custom layer in a file under `kaffe/custom_layers`, eg: mylayer.py
     - Implement ```shape_func(input_shape, [other_caffe_params])``` to calculate the output shape
     - Implement ```layer_func(inputs, name, [other_caffe_params])``` to construct a fluid layer
     - Register these two functions ```register(kind='MyType', shape=shape_func, layer=layer_func)```
-    - Notes: more examples can be found in '*kaffe/custom_layers*'
+    - Notes: more examples can be found in `kaffe/custom_layers`
 
-2. Add ```import mylayer``` to  '*kaffe/custom_layers/\_\_init__.py*'
+2. Add ```import mylayer``` to  `kaffe/custom_layers/\_\_init__.py`
 
 3. Prepare your pycaffe as your customized version(same as previous env prepare)
-    - (option1) replace 'proto/caffe.proto' with your own caffe.proto and compile it
-    - (option2) change your pycaffe to the customized version
+    - (option1) replace `proto/caffe.proto` with your own caffe.proto and compile it
+    - (option2) change your `pycaffe` to the customized version
 
 4. Convert the Caffe model to Fluid model
 
@@ -63,7 +65,7 @@ This tool is used to convert a Caffe model to a Fluid model
    export CAFFE2FLUID_CUSTOM_LAYERS=/path/to/caffe2fluid/kaffe
    ```
 
-6. Use the converted model when loading model in 'xxxnet.py' and 'xxxnet.npy'(no need if model is already in 'fluid/model' and 'fluid/params')
+6. Use the converted model when loading model in `xxxnet.py` and `xxxnet.npy`(no need if model is already in `fluid/model` and `fluid/params`)
 
 ### Tested models
 - Lenet: