diff --git a/.gitignore b/.gitignore
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--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,93 @@
+# IntelliJ project files
+.idea
+*.iml
+out
+gen
+
+### Vim template
+[._]*.s[a-w][a-z]
+[._]s[a-w][a-z]
+*.un~
+Session.vim
+.netrwhist
+*~
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+### IPythonNotebook template
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+.installed.cfg
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+# PyInstaller
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+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+# Installer logs
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+pip-delete-this-directory.txt
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+# Unit test / coverage reports
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+# Translations
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+# PyBuilder
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+*.ipynb
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+lib/dataset/pycocotools/*.c
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+lib/nms/*.c
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+external
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index 0000000..552af9f
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..6cc86f3
--- /dev/null
+++ b/README.md
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+# Deep Feature Flow
+
+
+The major contributors of this repository include [Xizhou Zhu](https://github.com/einsiedler0408), [Yuwen Xiong](https://github.com/Orpine), [Jifeng Dai](https://github.com/daijifeng001), [Lu Yuan](http://www.lyuan.org/), and [Yichen Wei](https://github.com/YichenWei).
+
+## Introduction
+
+
+**Deep Feature Flow** is initially described in a [CVPR 2017 paper](https://arxiv.org/abs/1611.07715).
+
+**R-FCN** is initially described in a [NIPS 2016 paper](https://arxiv.org/abs/1605.06409).
+
+***Click image to watch our demo video***
+
+[](http://www.youtube.com/watch?v=J0rMHE6ehGw)
+[](http://www.youtube.com/watch?v=J0rMHE6ehGw)
+
+## Disclaimer
+
+This is an official implementation for [Deep Feature Flow for Video Recognition](https://arxiv.org/abs/1611.07715) (DFF) based on MXNet. It is worth noticing that:
+
+ * 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 Deep Feature Flow.
+ * We trained our model based on the ImageNet pre-trained [ResNet-v1-101](https://github.com/KaimingHe/deep-residual-networks) model and [Flying Chairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html) pre-trained [FlowNet](https://lmb.informatik.uni-freiburg.de/resources/binaries/dispflownet/dispflownet-release-1.2.tar.gz) model using a [model converter](https://github.com/dmlc/mxnet/tree/430ea7bfbbda67d993996d81c7fd44d3a20ef846/tools/caffe_converter). The converted [ResNet-v1-101](https://github.com/KaimingHe/deep-residual-networks) 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).
+
+
+
+
+## License
+
+© Microsoft, 2017. Licensed under an Apache-2.0 license.
+
+## Citing Deep Feature Flow
+
+If you find Deep Feature Flow useful in your research, please consider citing:
+```
+@inproceedings{zhu17dff,
+ Author = {Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei},
+ Title = {Deep Feature Flow for Video Recognition},
+ Conference = {CVPR},
+ Year = {2017}
+}
+
+@inproceedings{dai16rfcn,
+ Author = {Jifeng Dai, Yi Li, Kaiming He, Jian Sun},
+ Title = {{R-FCN}: Object Detection via Region-based Fully Convolutional Networks},
+ Conference = {NIPS},
+ Year = {2016}
+}
+```
+
+## Main Results
+
+
+| | training data | testing data | mAP@0.5 | time/image (Tesla K40) | time/image(Maxwell Titan X) |
+|---------------------------------|-------------------|--------------|---------|---------|--------|
+| R-FCN, ResNet-v1-101 | ImageNet DET + VID train | ImageNet VID validation | 74.1 | 0.271s | 0.133s |
+| Deep Feature Flow(R-FCN, ResNet-v1-101, FlowNet) | ImageNet DET + VID train | ImageNet VID validation