diff --git a/configs/det/det_r50_dcn_fce_ctw.yml b/configs/det/det_r50_vd_dcn_fce_ctw.yml
similarity index 100%
rename from configs/det/det_r50_dcn_fce_ctw.yml
rename to configs/det/det_r50_vd_dcn_fce_ctw.yml
diff --git a/doc/doc_ch/algorithm_overview.md b/doc/doc_ch/algorithm_overview.md
index 4da949c2adb9b561c389e60cd98ef379ca66feea..a784067a001ee575adf72c258f8e96de6e615a7a 100755
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@@ -1,11 +1,11 @@
 # 两阶段算法
 
-- [两阶段算法](#-----)
-  * [1. 算法介绍](#1)
-    + [1.1 文本检测算法](#11)
-    + [1.2 文本识别算法](#12)
-  * [2. 模型训练](#2)
-  * [3. 模型推理](#3)
+- [两阶段算法](#两阶段算法)
+  - [1. 算法介绍](#1-算法介绍)
+    - [1.1 文本检测算法](#11-文本检测算法)
+    - [1.2 文本识别算法](#12-文本识别算法)
+  - [2. 模型训练](#2-模型训练)
+  - [3. 模型推理](#3-模型推理)
 
 <a name="1"></a>
 
@@ -43,8 +43,8 @@ PaddleOCR开源的文本检测算法列表：
 在CTW1500文本检测公开数据集上，算法效果如下：
 
 |模型|骨干网络|precision|recall|Hmean|下载链接|
-| --- | --- | --- | --- | --- | --- |
-|FCE|ResNet50_dcn|88.13%|82.60%|85.28%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)|
+| --- | --- | --- | --- | --- | --- |         
+|FCE|ResNet50_dcn|88.39%|82.18%|85.27%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/det_r50_dcn_fce_ctw_v2.0_train.tar)|
 
 **说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：
 * [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
diff --git a/ppocr/metrics/det_metric.py b/ppocr/metrics/det_metric.py
index 184283c68212b2582689abc310e449ac7b26ed16..c9ec8dd2e9082d7fd00db1086a352a61f0239cb1 100644
--- a/ppocr/metrics/det_metric.py
+++ b/ppocr/metrics/det_metric.py
@@ -135,7 +135,7 @@ class DetFCEMetric(object):
             #     metircs['{}_{}'.format(key, score_thr)] = value
             metirc_str = 'precision:{:.5f} recall:{:.5f} hmean:{:.5f}'.format(
                 metirc['precision'], metirc['recall'], metirc['hmean'])
-            metircs['\n thr {}'.format(score_thr)] = metirc_str
+            metircs['thr {}'.format(score_thr)] = metirc_str
             hmean = max(hmean, metirc['hmean'])
         metircs['hmean'] = hmean