diff --git a/docs/en/tutorials/config.md b/docs/en/tutorials/config.md
index 9f7759a2079..8fd37dbae25 100644
--- a/docs/en/tutorials/config.md
+++ b/docs/en/tutorials/config.md
@@ -181,71 +181,72 @@ model = dict(
             loss_mask=dict(  # Config of loss function for the mask branch.
                 type='CrossEntropyLoss',  # Type of loss used for segmentation
                 use_mask=True,  # Whether to only train the mask in the correct class.
-                loss_weight=1.0))))  # Loss weight of mask branch.
-train_cfg = dict(  # Config of training hyperparameters for rpn and rcnn
-    rpn=dict(  # Training config of rpn
-        assigner=dict(  # Config of assigner
-            type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
-            pos_iou_thr=0.7,  # IoU >= threshold 0.7 will be taken as positive samples
-            neg_iou_thr=0.3,  # IoU < threshold 0.3 will be taken as negative samples
-            min_pos_iou=0.3,  # The minimal IoU threshold to take boxes as positive samples
-            match_low_quality=True,  # Whether to match the boxes under low quality (see API doc for more details).
-            ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
-        sampler=dict(  # Config of positive/negative sampler
-            type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
-            num=256,  # Number of samples
-            pos_fraction=0.5,  # The ratio of positive samples in the total samples.
-            neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
-            add_gt_as_proposals=False),  # Whether add GT as proposals after sampling.
-        allowed_border=-1,  # The border allowed after padding for valid anchors.
-        pos_weight=-1,  # The weight of positive samples during training.
-        debug=False),  # Whether to set the debug mode
-    rpn_proposal=dict(  # The config to generate proposals during training
-        nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
-        nms_pre=2000,  # The number of boxes before NMS
-        nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
-        max_per_img=1000,  # The number of boxes to be kept after NMS.
-        nms=dict( # Config of NMS
-            type='nms',  # Type of NMS
-            iou_threshold=0.7 # NMS threshold
-        ),
-        min_bbox_size=0),  # The allowed minimal box size
-    rcnn=dict(  # The config for the roi heads.
-        assigner=dict(  # Config of assigner for second stage, this is different for that in rpn
-            type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
-            pos_iou_thr=0.5,  # IoU >= threshold 0.5 will be taken as positive samples
-            neg_iou_thr=0.5,  # IoU < threshold 0.5 will be taken as negative samples
-            min_pos_iou=0.5,  # The minimal IoU threshold to take boxes as positive samples
-            match_low_quality=False,  # Whether to match the boxes under low quality (see API doc for more details).
-            ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
-        sampler=dict(
-            type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
-            num=512,  # Number of samples
-            pos_fraction=0.25,  # The ratio of positive samples in the total samples.
-            neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
-            add_gt_as_proposals=True
-        ),  # Whether add GT as proposals after sampling.
-        mask_size=28,  # Size of mask
-        pos_weight=-1,  # The weight of positive samples during training.
-        debug=False))  # Whether to set the debug mode
-test_cfg = dict(  # Config for testing hyperparameters for rpn and rcnn
-    rpn=dict(  # The config to generate proposals during testing
-        nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
-        nms_pre=1000,  # The number of boxes before NMS
-        nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
-        max_per_img=1000,  # The number of boxes to be kept after NMS.
-        nms=dict( # Config of NMS
-            type='nms',  #Type of NMS
-            iou_threshold=0.7 # NMS threshold
-        ),
-        min_bbox_size=0),  # The allowed minimal box size
-    rcnn=dict(  # The config for the roi heads.
-        score_thr=0.05,  # Threshold to filter out boxes
-        nms=dict(  # Config of NMS in the second stage
-            type='nms',  # Type of NMS
-            iou_thr=0.5),  # NMS threshold
-        max_per_img=100,  # Max number of detections of each image
-        mask_thr_binary=0.5))  # Threshold of mask prediction
+                loss_weight=1.0))),  # Loss weight of mask branch.
+    train_cfg = dict(  # Config of training hyperparameters for rpn and rcnn
+        rpn=dict(  # Training config of rpn
+            assigner=dict(  # Config of assigner
+                type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
+                pos_iou_thr=0.7,  # IoU >= threshold 0.7 will be taken as positive samples
+                neg_iou_thr=0.3,  # IoU < threshold 0.3 will be taken as negative samples
+                min_pos_iou=0.3,  # The minimal IoU threshold to take boxes as positive samples
+                match_low_quality=True,  # Whether to match the boxes under low quality (see API doc for more details).
+                ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
+            sampler=dict(  # Config of positive/negative sampler
+                type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
+                num=256,  # Number of samples
+                pos_fraction=0.5,  # The ratio of positive samples in the total samples.
+                neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
+                add_gt_as_proposals=False),  # Whether add GT as proposals after sampling.
+            allowed_border=-1,  # The border allowed after padding for valid anchors.
+            pos_weight=-1,  # The weight of positive samples during training.
+            debug=False),  # Whether to set the debug mode
+        rpn_proposal=dict(  # The config to generate proposals during training
+            nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
+            nms_pre=2000,  # The number of boxes before NMS
+            nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
+            max_per_img=1000,  # The number of boxes to be kept after NMS.
+            nms=dict( # Config of NMS
+                type='nms',  # Type of NMS
+                iou_threshold=0.7 # NMS threshold
+                ),
+            min_bbox_size=0),  # The allowed minimal box size
+        rcnn=dict(  # The config for the roi heads.
+            assigner=dict(  # Config of assigner for second stage, this is different for that in rpn
+                type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
+                pos_iou_thr=0.5,  # IoU >= threshold 0.5 will be taken as positive samples
+                neg_iou_thr=0.5,  # IoU < threshold 0.5 will be taken as negative samples
+                min_pos_iou=0.5,  # The minimal IoU threshold to take boxes as positive samples
+                match_low_quality=False,  # Whether to match the boxes under low quality (see API doc for more details).
+                ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
+            sampler=dict(
+                type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
+                num=512,  # Number of samples
+                pos_fraction=0.25,  # The ratio of positive samples in the total samples.
+                neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
+                add_gt_as_proposals=True
+            ),  # Whether add GT as proposals after sampling.
+            mask_size=28,  # Size of mask
+            pos_weight=-1,  # The weight of positive samples during training.
+            debug=False)),  # Whether to set the debug mode
+    test_cfg = dict(  # Config for testing hyperparameters for rpn and rcnn
+        rpn=dict(  # The config to generate proposals during testing
+            nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
+            nms_pre=1000,  # The number of boxes before NMS
+            nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
+            max_per_img=1000,  # The number of boxes to be kept after NMS.
+            nms=dict( # Config of NMS
+                type='nms',  #Type of NMS
+                iou_threshold=0.7 # NMS threshold
+                ),
+            min_bbox_size=0),  # The allowed minimal box size
+        rcnn=dict(  # The config for the roi heads.
+            score_thr=0.05,  # Threshold to filter out boxes
+            nms=dict(  # Config of NMS in the second stage
+                type='nms',  # Type of NMS
+                iou_thr=0.5),  # NMS threshold
+            max_per_img=100,  # Max number of detections of each image
+            mask_thr_binary=0.5)))  # Threshold of mask prediction
+
 dataset_type = 'CocoDataset'  # Dataset type, this will be used to define the dataset
 data_root = 'data/coco/'  # Root path of data
 img_norm_cfg = dict(  # Image normalization config to normalize the input images
diff --git a/docs/zh_cn/tutorials/config.md b/docs/zh_cn/tutorials/config.md
index 40460878006..5ed9a6ed84a 100644
--- a/docs/zh_cn/tutorials/config.md
+++ b/docs/zh_cn/tutorials/config.md
@@ -164,71 +164,72 @@ model = dict(
             loss_mask=dict(  # mask 分支的损失函数配置。
                 type='CrossEntropyLoss',  # 用于分割的损失类型。
                 use_mask=True,  # 是否只在正确的类中训练 mask。
-                loss_weight=1.0))))  # mask 分支的损失权重.
-train_cfg = dict(  # rpn 和 rcnn 训练超参数的配置
-    rpn=dict(  # rpn 的训练配置
-        assigner=dict(  # 分配器(assigner)的配置
-            type='MaxIoUAssigner',  # 分配器的类型，MaxIoUAssigner 用于许多常见的检测器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
-            pos_iou_thr=0.7,  # IoU >= 0.7(阈值) 被视为正样本。
-            neg_iou_thr=0.3,  # IoU < 0.3(阈值) 被视为负样本。
-            min_pos_iou=0.3,  # 将框作为正样本的最小 IoU 阈值。
-            match_low_quality=True,  # 是否匹配低质量的框(更多细节见 API 文档).
-            ignore_iof_thr=-1),  # 忽略 bbox 的 IoF 阈值。
-        sampler=dict(  # 正/负采样器(sampler)的配置
-            type='RandomSampler',  # 采样器类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
-            num=256,  # 样本数量。
-            pos_fraction=0.5,  # 正样本占总样本的比例。
-            neg_pos_ub=-1,  # 基于正样本数量的负样本上限。
-            add_gt_as_proposals=False),  # 采样后是否添加 GT 作为 proposal。
-        allowed_border=-1,  # 填充有效锚点后允许的边框。
-        pos_weight=-1,  # 训练期间正样本的权重。
-        debug=False),  # 是否设置调试(debug)模式
-    rpn_proposal=dict(  # 在训练期间生成 proposals 的配置
-        nms_across_levels=False,  # 是否对跨层的 box 做 NMS。仅适用于 `GARPNHead` ，naive rpn 不支持 nms cross levels。
-        nms_pre=2000,  # NMS 前的 box 数
-        nms_post=1000,  # NMS 要保留的 box 的数量，只在 GARPNHHead 中起作用。
-        max_per_img=1000,  # NMS 后要保留的 box 数量。
-        nms=dict( # NMS 的配置
-            type='nms',  # NMS 的类别
-            iou_threshold=0.7 # NMS 的阈值
-        ),
-        min_bbox_size=0),  # 允许的最小 box 尺寸
-    rcnn=dict(  # roi head 的配置。
-        assigner=dict(  # 第二阶段分配器的配置，这与 rpn 中的不同
-            type='MaxIoUAssigner',  # 分配器的类型，MaxIoUAssigner 目前用于所有 roi_heads。更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
-            pos_iou_thr=0.5,  # IoU >= 0.5(阈值)被认为是正样本。
-            neg_iou_thr=0.5,  # IoU < 0.5(阈值)被认为是负样本。
-            min_pos_iou=0.5,  # 将 box 作为正样本的最小 IoU 阈值
-            match_low_quality=False,  # 是否匹配低质量下的 box(有关更多详细信息，请参阅 API 文档)。
-            ignore_iof_thr=-1),  # 忽略 bbox 的 IoF 阈值
-        sampler=dict(
-            type='RandomSampler',  #采样器的类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
-            num=512,  # 样本数量
-            pos_fraction=0.25,  # 正样本占总样本的比例。.
-            neg_pos_ub=-1,  # 基于正样本数量的负样本上限。.
-            add_gt_as_proposals=True
-        ),  # 采样后是否添加 GT 作为 proposal。
-        mask_size=28,  # mask 的大小
-        pos_weight=-1,  # 训练期间正样本的权重。
-        debug=False))  # 是否设置调试模式。
-test_cfg = dict(  # 用于测试 rpn 和 rcnn 超参数的配置
-    rpn=dict(  # 测试阶段生成 proposals 的配置
-        nms_across_levels=False,  # 是否对跨层的 box 做 NMS。仅适用于`GARPNHead`，naive rpn 不支持做 NMS cross levels。
-        nms_pre=1000,  # NMS 前的 box 数
-        nms_post=1000,  # NMS 要保留的 box 的数量，只在`GARPNHHead`中起作用。
-        max_per_img=1000,  # NMS 后要保留的 box 数量
-        nms=dict( # NMS 的配置
-            type='nms',  # NMS 的类型
-            iou_threshold=0.7 # NMS 阈值
-        ),
-        min_bbox_size=0),  # box 允许的最小尺寸
-    rcnn=dict(  # roi heads 的配置
-        score_thr=0.05,  # bbox 的分数阈值
-        nms=dict(  # 第二步的 NMS 配置
-            type='nms',  # NMS 的类型
-            iou_thr=0.5),  # NMS 的阈值
-        max_per_img=100,  # 每张图像的最大检测次数
-        mask_thr_binary=0.5))  # mask 预处的阈值
+                loss_weight=1.0))),  # mask 分支的损失权重.
+    train_cfg = dict(  # rpn 和 rcnn 训练超参数的配置
+        rpn=dict(  # rpn 的训练配置
+            assigner=dict(  # 分配器(assigner)的配置
+                type='MaxIoUAssigner',  # 分配器的类型，MaxIoUAssigner 用于许多常见的检测器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
+                pos_iou_thr=0.7,  # IoU >= 0.7(阈值) 被视为正样本。
+                neg_iou_thr=0.3,  # IoU < 0.3(阈值) 被视为负样本。
+                min_pos_iou=0.3,  # 将框作为正样本的最小 IoU 阈值。
+                match_low_quality=True,  # 是否匹配低质量的框(更多细节见 API 文档).
+                ignore_iof_thr=-1),  # 忽略 bbox 的 IoF 阈值。
+            sampler=dict(  # 正/负采样器(sampler)的配置
+                type='RandomSampler',  # 采样器类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
+                num=256,  # 样本数量。
+                pos_fraction=0.5,  # 正样本占总样本的比例。
+                neg_pos_ub=-1,  # 基于正样本数量的负样本上限。
+                add_gt_as_proposals=False),  # 采样后是否添加 GT 作为 proposal。
+            allowed_border=-1,  # 填充有效锚点后允许的边框。
+            pos_weight=-1,  # 训练期间正样本的权重。
+            debug=False),  # 是否设置调试(debug)模式
+        rpn_proposal=dict(  # 在训练期间生成 proposals 的配置
+            nms_across_levels=False,  # 是否对跨层的 box 做 NMS。仅适用于 `GARPNHead` ，naive rpn 不支持 nms cross levels。
+            nms_pre=2000,  # NMS 前的 box 数
+            nms_post=1000,  # NMS 要保留的 box 的数量，只在 GARPNHHead 中起作用。
+            max_per_img=1000,  # NMS 后要保留的 box 数量。
+            nms=dict( # NMS 的配置
+                type='nms',  # NMS 的类别
+                iou_threshold=0.7 # NMS 的阈值
+                ),
+            min_bbox_size=0),  # 允许的最小 box 尺寸
+        rcnn=dict(  # roi head 的配置。
+            assigner=dict(  # 第二阶段分配器的配置，这与 rpn 中的不同
+                type='MaxIoUAssigner',  # 分配器的类型，MaxIoUAssigner 目前用于所有 roi_heads。更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
+                pos_iou_thr=0.5,  # IoU >= 0.5(阈值)被认为是正样本。
+                neg_iou_thr=0.5,  # IoU < 0.5(阈值)被认为是负样本。
+                min_pos_iou=0.5,  # 将 box 作为正样本的最小 IoU 阈值
+                match_low_quality=False,  # 是否匹配低质量下的 box(有关更多详细信息，请参阅 API 文档)。
+                ignore_iof_thr=-1),  # 忽略 bbox 的 IoF 阈值
+            sampler=dict(
+                type='RandomSampler',  #采样器的类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
+                num=512,  # 样本数量
+                pos_fraction=0.25,  # 正样本占总样本的比例。.
+                neg_pos_ub=-1,  # 基于正样本数量的负样本上限。.
+                add_gt_as_proposals=True
+            ),  # 采样后是否添加 GT 作为 proposal。
+            mask_size=28,  # mask 的大小
+            pos_weight=-1,  # 训练期间正样本的权重。
+            debug=False)),  # 是否设置调试模式。
+    test_cfg = dict(  # 用于测试 rpn 和 rcnn 超参数的配置
+        rpn=dict(  # 测试阶段生成 proposals 的配置
+            nms_across_levels=False,  # 是否对跨层的 box 做 NMS。仅适用于`GARPNHead`，naive rpn 不支持做 NMS cross levels。
+            nms_pre=1000,  # NMS 前的 box 数
+            nms_post=1000,  # NMS 要保留的 box 的数量，只在`GARPNHHead`中起作用。
+            max_per_img=1000,  # NMS 后要保留的 box 数量
+            nms=dict( # NMS 的配置
+                type='nms',  # NMS 的类型
+                iou_threshold=0.7 # NMS 阈值
+                ),
+            min_bbox_size=0),  # box 允许的最小尺寸
+        rcnn=dict(  # roi heads 的配置
+            score_thr=0.05,  # bbox 的分数阈值
+            nms=dict(  # 第二步的 NMS 配置
+                type='nms',  # NMS 的类型
+                iou_thr=0.5),  # NMS 的阈值
+            max_per_img=100,  # 每张图像的最大检测次数
+            mask_thr_binary=0.5)))  # mask 预处的阈值
+
 dataset_type = 'CocoDataset'  # 数据集类型，这将被用来定义数据集。
 data_root = 'data/coco/'  # 数据的根路径。
 img_norm_cfg = dict(  # 图像归一化配置，用来归一化输入的图像。