add GN documentation

Summary: Add GN documentation.

Reviewed By: rbgirshick, ppwwyyxx

Differential Revision: D7745878

fbshipit-source-id: 8a49160b1e4026fbcf41082df4a3a8d0f8e90d85

README.md

@@ -2,7 +2,7 @@
 
 Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.
 
-At FAIR, Detectron has enabled numerous research projects, including: [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144), [Mask R-CNN](https://arxiv.org/abs/1703.06870), [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333), [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002), [Non-local Neural Networks](https://arxiv.org/abs/1711.07971), [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370), [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440), and [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/abs/1802.00434).
+At FAIR, Detectron has enabled numerous research projects, including: [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144), [Mask R-CNN](https://arxiv.org/abs/1703.06870), [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333), [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002), [Non-local Neural Networks](https://arxiv.org/abs/1711.07971), [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370), [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440), [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/abs/1802.00434), and [Group Normalization](https://arxiv.org/abs/1803.08494).
 
 <div align="center">
   <img src="demo/output/33823288584_1d21cf0a26_k_example_output.jpg" width="700px" />
@@ -32,6 +32,10 @@ using the following backbone network architectures:
 
 Additional backbone architectures may be easily implemented. For more details about these models, please see [References](#references) below.
 
+## Update
+
+- 4/2018: Support Group Normalization - see [`gn/README.md`](./projects/gn/README.md)
+
 ## License
 
 Detectron is released under the [Apache 2.0 license](https://github.com/facebookresearch/detectron/blob/master/LICENSE). See the [NOTICE](https://github.com/facebookresearch/detectron/blob/master/NOTICE) file for additional details.