From 71ecf60fa9ad10705547610bf401e97ee65aeccd Mon Sep 17 00:00:00 2001
From: Nemo Xiong <xiongnemo@126.com>
Date: Tue, 22 Mar 2022 14:38:26 +0800
Subject: [PATCH] colab notebook: fix outdated link for doc (#1392)

* colab notebook: fix outdated link for doc

Fixed outdated link for how to customize your datasets by reorganizing data.

* fix lint
---
 demo/MMSegmentation_Tutorial.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/demo/MMSegmentation_Tutorial.ipynb b/demo/MMSegmentation_Tutorial.ipynb
index c52a31b6..9cefa399 100644
--- a/demo/MMSegmentation_Tutorial.ipynb
+++ b/demo/MMSegmentation_Tutorial.ipynb
@@ -230,7 +230,7 @@
     "\n",
     "Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same prefix. To support a new dataset, we may need to modify the original file structure. \n",
     "\n",
-    "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/docs/en/tutorials/new_dataset.md) for details about dataset reorganization. \n",
+    "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/tutorials/customize_datasets.md#customize-datasets-by-reorganizing-data) for details about dataset reorganization. \n",
     "\n",
     "We use [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
     "In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object. "
-- 
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