Update README.md

README.md

@@ -22,6 +22,7 @@ This is an official implementation for [Deformable Convolutional Networks](https
 
   * The original implementation is based on our internal Caffe version on Windows. There are slight differences in the final accuracy and running time due to the plenty details in platform switch.
   * The code is tested on official [MXNet@(commit 62ecb60)](https://github.com/dmlc/mxnet/tree/62ecb60) with the extra operators for Deformable ConvNets.
+  	* After [MXNet@(commit ce2bca6)](https://github.com/dmlc/mxnet/tree/ce2bca6) the offical MXNet support all operators for Deformable ConvNets.
   * We trained our model based on the ImageNet pre-trained [ResNet-v1-101](https://github.com/KaimingHe/deep-residual-networks) using a [model converter](https://github.com/dmlc/mxnet/tree/430ea7bfbbda67d993996d81c7fd44d3a20ef846/tools/caffe_converter). The converted model produces slightly lower accuracy (Top-1 Error on ImageNet val: 24.0% v.s. 23.6%).
   * This repository used code from [MXNet rcnn example](https://github.com/dmlc/mxnet/tree/master/example/rcnn) and [mx-rfcn](https://github.com/giorking/mx-rfcn).
 
@@ -102,6 +103,8 @@ git clone https://github.com/msracver/Deformable-ConvNets.git
 ~~~
 2. For Windows users, run ``cmd .\init.bat``. For Linux user, run `sh ./init.sh`. The scripts will build cython module automatically and create some folders.
 3. Copy operators in `./rfcn/operator_cxx` to `$(YOUR_MXNET_FOLDER)/src/operator/contrib` and recompile MXNet.
+
+	3.1. After [MXNet@(commit ce2bca6)](https://github.com/dmlc/mxnet/tree/ce2bca6) the offical MXNet support all operators for Deformable ConvNets. You do not need to copy operators and recompile MXNet if you use MXNet after this version.
 4. Please install MXNet following the official guide of MXNet. For advanced users, you may put your Python packge into `./external/mxnet/$(YOUR_MXNET_PACKAGE)`, and modify `MXNET_VERSION` in `./experiments/rfcn/cfgs/*.yaml` to `$(YOUR_MXNET_PACKAGE)`. Thus you can switch among different versions of MXNet quickly.
 5. For Deeplab, we use the argumented VOC 2012 dataset. The argumented annotations are provided by [SBD](http://home.bharathh.info/pubs/codes/SBD/download.html) dataset. For convenience, we provide the converted PNG annotations and the lists of train/val images, please download them from [OneDrive](https://1drv.ms/u/s!Am-5JzdW2XHzhqMRhVImMI1jRrsxDg).