add readme for RepPoints

Author: ancientmooner

configs/reppoints/README.md

@@ -0,0 +1,62 @@
+# RepPoints: Point Set Representation for Object Detection
+
+By [Ze Yang](https://yangze.tech/), [Shaohui Liu](http://b1ueber2y.me/), and [Han Hu](https://ancientmooner.github.io/).
+
+We provide code support and configuration files to reproduce the results in the paper for
+["RepPoints: Point Set Representation for Object Detection"](https://arxiv.org/abs/1904.11490) on COCO object detection. 
+
+## Introduction
+
+**RepPoints**, initially described in [arXiv](https://arxiv.org/abs/1904.11490), is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by `bounding box + RoIPool (RoIAlign)`. The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a `point set` instead of a `bounding box`, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s `spatial extent` and enables `semantically aligned feature extraction`. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection.
+
+Another feature of this repo is the demonstration of an `anchor-free detector`, which can be as effective as state-of-the-art anchor-based detection methods. The anchor-free detector can utilize either `bounding box` or `RepPoints` as the basic object representation.
+
+<div align="center">
+  <img src="reppoints.png" width="400px" />
+  <p>Learning RepPoints in Object Detection.</p>
+</div>
+
+## Citing RepPoints
+
+```
+@inproceedings{yang2019reppoints,
+  title={RepPoints: Point Set Representation for Object Detection},
+  author={Yang, Ze and Liu, Shaohui and Hu, Han and Wang, Liwei and Lin, Stephen},
+  booktitle={The IEEE International Conference on Computer Vision (ICCV)},
+  month={Oct},
+  year={2019}
+}
+```
+
+## Results and models
+
+The results on COCO 2017val are shown in the table below.
+
+| Method | Backbone | Anchor | convert func | Lr schd | box AP | Download |
+| :----: | :------: | :-------: | :------: | :-----: | :----: | :------: |
+| BBox | R-50-FPN | single | -    | 1x      | 36.3|[model](https://drive.google.com/open?id=1TaVAFGZP2i7RwtlQjy3LBH1WI-YRH774) |
+| BBox | R-50-FPN | none     | -    | 1x      | 37.3| [model](https://drive.google.com/open?id=1hpfu-I7gtZnIb0NU2WvUvaZz_dm-THuZ) |
+| RepPoints | R-50-FPN | none     | partial MinMax | 1x      | 38.1| [model](https://drive.google.com/open?id=11zFtdKH-QGz_zH7vlcIih6FQAjV84CWc) |
+| RepPoints | R-50-FPN | none     | MinMax | 1x      | 38.2| [model](https://drive.google.com/open?id=1Cg9818dpkL-9qjmYdkhrY_BRiQFjV4xu)  |
+| RepPoints | R-50-FPN | none     | moment | 1x      | 38.2| [model](https://drive.google.com/open?id=1rQg-lE-5nuqO1bt6okeYkti4Q-EaBsu_) |
+| RepPoints | R-50-FPN | none     | moment | 2x      | 38.6| [model](https://drive.google.com/open?id=1TfR-5geVviKhRoXL9JP6cG3fkN2itbBU) |
+| RepPoints | R-50-FPN | none     | moment | 2x (ms train)   | 40.8| [model](https://drive.google.com/open?id=1oaHTIaP51oB5HJ6GWV3WYK19lMm9iJO6) |
+| RepPoints | R-50-FPN | none     | moment | 2x (ms train&ms test)   | 42.2|          |
+| RepPoints | R-101-FPN | none   | moment | 2x   | 40.3| [model](https://drive.google.com/open?id=1BAmGeUQ_zVQi2u7rgOuPQem2EjXDLgWm) |
+| RepPoints | R-101-FPN | none   | moment | 2x (ms train)   | 42.3| [model](https://drive.google.com/open?id=14Lf0p4fXElXaxFu8stk3hek3bY8tNENX) |
+| RepPoints | R-101-FPN | none   | moment | 2x (ms train&ms test)   | 44.1|          |
+| RepPoints | R-101-FPN-DCN | none   | moment | 2x   | 43.0| [model](https://drive.google.com/open?id=1hpptxpb4QtNuB-HnV5wHbDltPHhlYq4z) |
+| RepPoints | R-101-FPN-DCN | none   | moment | 2x (ms train)   | 44.8| [model](https://drive.google.com/open?id=1fsTckK99HYjOURwcFeHfy5JRRtsCajfX) |
+| RepPoints | R-101-FPN-DCN | none   | moment | 2x (ms train&ms test)   | 46.4|          |
+| RepPoints | X-101-FPN-DCN | none   | moment | 2x   | 44.5| [model](https://drive.google.com/open?id=1Y8vqaqU88-FEqqwl6Zb9exD5O246yrMR) |
+| RepPoints | X-101-FPN-DCN | none   | moment | 2x (ms train)   | 45.6| [model](https://drive.google.com/open?id=1nr9gcVWxzeakbfPC6ON9yvKOuLzj_RrJ) |
+| RepPoints | X-101-FPN-DCN | none   | moment | 2x (ms train&ms test)   | 46.8|          |
+
+**Notes:**
+
+- `R-xx`, `X-xx` denote the ResNet and ResNeXt architectures, respectively. 
+- `DCN` denotes replacing 3x3 conv with the 3x3 deformable convolution in `c3-c5` stages of backbone.
+- `none` in the `anchor` column means 2-d `center point` (x,y) is used to represent the initial object hypothesis. `single` denotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted. 
+- `moment`, `partial MinMax`, `MinMax` in the `convert func` column are three functions to convert a point set to a pseudo box.
+- `ms` denotes multi-scale training or multi-scale test.
+- Note the results here are slightly different from those reported in the paper, due to framework change. While the original paper uses an [MXNet](https://mxnet.apache.org/) implementation, we re-implement the method in [PyTorch](https://pytorch.org/) based on mmdetection.