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open-mmlab · Mar 30, 2022 · a4e4b0a · a4e4b0a
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diff --git a/docs/en/tutorials/training_tricks.md b/docs/en/tutorials/training_tricks.md
@@ -73,7 +73,7 @@ Note: If you want this loss item to be included into the backward graph, `loss_`
 
 In default setting, `avg_non_ignore=False` which means each pixel counts for loss calculation although some of them belong to ignore-index labels.
 
-For loss calculation, we support ignore index of certain label by `avg_non_ignore` and `ignore_index`. In this way, the average loss would only be calculated in non-ignored labels which may achieve better performance. Here is an example config of training `unet` on `Cityscapes` dataset: in loss calculation it would ignore label 0 which is background and loss average is only calculated on non-ignore labels:
+For loss calculation, we support ignore index of certain label by `avg_non_ignore` and `ignore_index`. In this way, the average loss would only be calculated in non-ignored labels which may achieve better performance, and here is the [reference](https://github.com/open-mmlab/mmsegmentation/pull/1409). Here is an example config of training `unet` on `Cityscapes` dataset: in loss calculation it would ignore label 0 which is background and loss average is only calculated on non-ignore labels:
 
 ```python
 _base_ = './fcn_unet_s5-d16_4x4_512x1024_160k_cityscapes.py'

diff --git a/docs/zh_cn/tutorials/training_tricks.md b/docs/zh_cn/tutorials/training_tricks.md
@@ -73,7 +73,7 @@ model = dict(
 
 默认设置 `avg_non_ignore=False`， 即每个像素都用来计算损失函数。尽管其中的一些像素属于需要被忽略的类别。
 
-对于训练时损失函数的计算，我们目前支持使用 `avg_non_ignore` 和 `ignore_index` 来忽略 label 特定的类别。 这样损失函数将只在非忽略类别像素中求平均值，会获得更好的表现。以 `unet` 使用 `Cityscapes` 数据集训练为例，
+对于训练时损失函数的计算，我们目前支持使用 `avg_non_ignore` 和 `ignore_index` 来忽略 label 特定的类别。 这样损失函数将只在非忽略类别像素中求平均值，会获得更好的表现。这里是[相关 PR](https://github.com/open-mmlab/mmsegmentation/pull/1409)。以 `unet` 使用 `Cityscapes` 数据集训练为例，
 在计算损失函数时，忽略 label 为0的背景，并且仅在不被忽略的像素上计算均值。配置文件写为:
 
 ```python

diff --git a/mmseg/models/losses/cross_entropy_loss.py b/mmseg/models/losses/cross_entropy_loss.py
@@ -107,8 +107,7 @@ def binary_cross_entropy(pred,
         avg_factor (int, optional): Average factor that is used to average
             the loss. Defaults to None.
         class_weight (list[float], optional): The weight for each class.
-        ignore_index (int): The label index to be ignored.
-            Default: -100.
+        ignore_index (int): The label index to be ignored. Default: -100.
         avg_non_ignore (bool): The flag decides to whether the loss is
             only averaged over non-ignored targets. Default: False.
             `New in version 0.23.0.`