diff --git a/fluid/PaddleCV/caffe2fluid/README.md b/fluid/PaddleCV/caffe2fluid/README.md
index 3d2f846df459add50c7710c1a5addee2efc6564a..d502e8900b35ccdc530d4a0928fc180191053f0e 100644
--- a/fluid/PaddleCV/caffe2fluid/README.md
+++ b/fluid/PaddleCV/caffe2fluid/README.md
@@ -3,88 +3,3 @@ This tool is used to convert a Caffe model to a Fluid model
 
 ### Statement
 This module is migrated to [X2Paddle](https://github.com/PaddlePaddle/X2Paddle)
-
-### Key Features
-1. Convert caffe model to fluid model with codes of defining a network(useful for re-training)
-
-2. Pycaffe is not necessary when just want convert model without do caffe-inference
-
-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
-
-### HowTo
-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
-        ```
-
-    - Download one from github directly
-        ```
-        cd proto/ && wget https://raw.githubusercontent.com/ethereon/caffe-tensorflow/master/kaffe/caffe/caffepb.py
-        ```
-
-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
-       ```
-       # 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`
-
-4. Compare the inference results with caffe
-    - 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
-    - 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`
-
-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
-
-4. Convert the Caffe model to Fluid model
-
-5. Set env $CAFFE2FLUID_CUSTOM_LAYERS to the parent directory of 'custom_layers'
-   ```
-   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`)
-
-### Tested models
-- Lenet:
-[model addr](https://github.com/ethereon/caffe-tensorflow/blob/master/examples/mnist)
-
-- ResNets:(ResNet-50, ResNet-101, ResNet-152)
-[model addr](https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777)
-
-- GoogleNet:
-[model addr](https://gist.github.com/jimmie33/7ea9f8ac0da259866b854460f4526034)
-
-- VGG:
-[model addr](https://gist.github.com/ksimonyan/211839e770f7b538e2d8)
-
-- AlexNet:
-[model addr](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet)
-
-### Notes
-Some of this code come from here: [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow)