From fb27c48352f242174b5cef1c721449f43426ec15 Mon Sep 17 00:00:00 2001
From: wuyefeilin <30919197+wuyefeilin@users.noreply.github.com>
Date: Mon, 17 Aug 2020 17:32:22 +0800
Subject: [PATCH] Update train_group.md

---
 docs/configs/train_group.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/configs/train_group.md b/docs/configs/train_group.md
index 2fc8806c..96f2f640 100644
--- a/docs/configs/train_group.md
+++ b/docs/configs/train_group.md
@@ -45,7 +45,7 @@ TRAIN Group存放所有和训练相关的配置
 是否在多卡间同步BN的均值和方差。
 
 Synchronized Batch Norm跨GPU批归一化策略最早在[MegDet: A Large Mini-Batch Object Detector](https://arxiv.org/abs/1711.07240)
-论文中提出，在[Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/pdf/1902.04103.pdf)论文中以Yolov3验证了这一策略的有效性，[PaddleCV/yolov3](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/yolov3)实现了这一系列策略并比Darknet框架版本在COCO17数据上mAP高5.9.
+论文中提出，在[Bag of Freebies for Training Object Detection Neural Networks](https://arxiv.org/pdf/1902.04103.pdf)论文中以Yolov3验证了这一策略的有效性。
 
 PaddleSeg基于PaddlePaddle框架的sync_batch_norm策略，可以支持通过多卡实现大batch size的分割模型训练，可以得到更高的mIoU精度。
 
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