From 1bc1b33ca642ca9c648d4e11657dd5cfd8a43ba2 Mon Sep 17 00:00:00 2001
From: JiankangDeng <j.deng16@imperial.ac.uk>
Date: Sat, 4 May 2019 11:53:59 +0100
Subject: [PATCH] Update README.md

---
 RetinaFace/README.md | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/RetinaFace/README.md b/RetinaFace/README.md
index d0cb8c3..f108de3 100644
--- a/RetinaFace/README.md
+++ b/RetinaFace/README.md
@@ -2,14 +2,18 @@
 
 ## Introduction
 
-RetinaFace is a robust single stage face detector which initially described as an [arXiv technical report](https://arxiv.org/abs/1905.00641)
+RetinaFace is a practical single-stage face detector which is initially described in [arXiv technical report](https://arxiv.org/abs/1905.00641)
 
 ![demoimg](https://github.com/deepinsight/insightface/blob/master/resources/11513D05.jpg)
 
+## Data
 
-## Training
+1. Download our annotations (face bounding boxes & five facial landmarks) from [baiducloud](https://pan.baidu.com/s/1Laby0EctfuJGgGMgRRgykA) or [dropbox](https://www.dropbox.com/s/7j70r3eeepe4r2g/retinaface_gt_v1.1.zip?dl=0)
+
+2. Download the [WIDERFACE](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html) dataset.
+
+3. Organise the dataset directory under ``insightface/RetinaFace/`` as follows:
 
-1. Download groundtruth labels from [baiducloud](https://pan.baidu.com/s/1Laby0EctfuJGgGMgRRgykA) or dropbox and organise the dataset directory under ``insightface/RetinaFace/`` as follows(images can be downloaded from WiderFace website directly):
 ```Shell
   data/retinaface/
     train/
@@ -23,11 +27,22 @@ RetinaFace is a robust single stage face detector which initially described as a
       label.txt
 ```
 
+## Training
+
+Please check ``train.py`` for training.
+
 ## Testing
 
-Please check ``test.py`` for model usage.
+Please check ``test.py`` for testing.
+
+## Models
+
+Pretrained Model: RetinaFace-R50 ([baiducloud](https://pan.baidu.com/s/1C6nKq122gJxRhb37vK0_LQ) or [dropbox](https://www.dropbox.com/s/53ftnlarhyrpkg2/retinaface-R50.zip?dl=0)) is a medium size model with ResNet50 backbone.
+It can output face bounding boxes and five facial landmarks in a single forward pass.
+WiderFace validation mAP: Easy 96.5, Medium 95.6, Hard 90.4. 
+
+To avoid the confliction with the WiderFace Challenge (ICCV 2019), we postpone the release time of our best model.
 
-Pretrained Model: [RetinaFace-R50](https://pan.baidu.com/s/1C6nKq122gJxRhb37vK0_LQ) is a medium size model with ResNet50 backbone. WiderFace validation mAP: Easy 96.5, Medium 95.6, Hard 90.4. It can output face bounding boxes and five landmarks in a single forward pass.