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[DOC] Update datset download score from opendatalab to openXlab #1765

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Aug 22, 2023
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[DOC] Update datset download score from opendatalab to openXlab #1765

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4c91c59
update opendatalab to openXlab
Ezra-Yu Aug 14, 2023
03bf56c
update dataset-index
fangyixiao18 Aug 22, 2023
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4 changes: 2 additions & 2 deletions dataset-index.yml
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Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
imagenet1k:
dataset: ImageNet-1K
dataset: OpenDataLab/ImageNet-1K
download_root: data
data_root: data/imagenet
script: tools/dataset_converters/odl_imagenet1k_preprocess.sh

cub:
dataset: CUB-200-2011
dataset: OpenDataLab/CUB-200-2011
download_root: data
data_root: data/CUB_200_2011
script: tools/dataset_converters/odl_cub_preprocess.sh
14 changes: 7 additions & 7 deletions docs/en/user_guides/dataset_prepare.md
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Expand Up @@ -144,15 +144,15 @@ ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](

````{group-tab} Download by MIM

MIM supports downloading from [OpenDataLab](https://opendatalab.com/) and preprocessing ImageNet dataset with one command line.
MIM supports downloading from [OpenXlab](https://openxlab.org.cn/datasets) and preprocessing ImageNet dataset with one command line.

_You need to register an account at [OpenDataLab official website](https://opendatalab.com/) and login by CLI._
_You need to register an account at [OpenXlab official website](https://openxlab.org.cn/datasets) and login by CLI._

```Bash
# install OpenDataLab CLI tools
pip install -U opendatalab
# log in OpenDataLab, register if you don't have an account.
odl login
# install OpenXlab CLI tools
pip install -U openxlab
# log in OpenXLab
openxlab login
# download and preprocess by MIM, better to execute in $MMPreTrain directory.
mim download mmpretrain --dataset imagenet1k
```
Expand Down Expand Up @@ -278,7 +278,7 @@ test_dataloader = val_dataloader
| [`SUN397`](mmpretrain.datasets.SUN397)(data_root[, split, pipeline, ...]) | ["train", "test"] | [SUN397](https://vision.princeton.edu/projects/2010/SUN/) Dataset. |
| [`VOC`](mmpretrain.datasets.VOC)(data_root[, image_set_path, pipeline, ...]) | ["train", "val", "tranval", "test"] | [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) Dataset. |

Some dataset homepage links may be unavailable, and you can download datasets through [OpenDataLab](https://opendatalab.com/), such as [Stanford Cars](https://opendatalab.com/Stanford_Cars/download).
Some dataset homepage links may be unavailable, and you can download datasets through [OpenXLab](https://openxlab.org.cn/datasets), such as [Stanford Cars](https://openxlab.org.cn/datasets/OpenDataLab/Stanford_Cars).

## OpenMMLab 2.0 Standard Dataset

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14 changes: 7 additions & 7 deletions docs/zh_CN/user_guides/dataset_prepare.md
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Expand Up @@ -142,15 +142,15 @@ ImageNet 有多个版本，但最常用的一个是 [ILSVRC 2012](http://www.ima

````{group-tab} MIM 下载

MIM支持使用一条命令行从 [OpenDataLab](https://opendatalab.com/) 下载并预处理 ImageNet 数据集。
MIM支持使用一条命令行从 [OpenXLab](https://openxlab.org.cn/datasets?lang=zh-CN) 下载并预处理 ImageNet 数据集。

_需要在 [OpenDataLab 官网](https://opendatalab.com/) 注册账号并命令行登录_。
_需要在 [OpenXLab 官网](https://openxlab.org.cn/datasets?lang=zh-CN) 注册账号并命令行登录_。

```Bash
# 安装opendatalab库
pip install -U opendatalab
# 登录到 OpenDataLab, 如果还没有注册，请到官网注册一个
odl login
# 安装 OpenXLab CLI 工具
pip install -U openxlab
# 登录 OpenXLab
openxlab login
# 使用 MIM 下载数据集, 最好在 $MMPreTrain 目录执行
mim download mmpretrain --dataset imagenet1k
```
Expand Down Expand Up @@ -276,7 +276,7 @@ test_dataloader = val_dataloader
| [`SUN397`](mmpretrain.datasets.SUN397)(data_root[, split, pipeline, ...]) | ["train", "test"] | [SUN397](https://vision.princeton.edu/projects/2010/SUN/) 数据集 |
| [`VOC`](mmpretrain.datasets.VOC)(data_root[, image_set_path, pipeline, ...]) | ["train", "val", "tranval", "test"] | [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) 数据集 |

有些数据集主页链接可能已经失效，您可以通过[OpenDataLab](https://opendatalab.com/)下载数据集，例如 [Stanford Cars](https://opendatalab.com/Stanford_Cars/download)数据集。
有些数据集主页链接可能已经失效，您可以通过[OpenXLab](https://openxlab.org.cn/datasets?lang=zh-CN)下载数据集，例如 [Stanford Cars](https://openxlab.org.cn/datasets/OpenDataLab/Stanford_Cars)数据集。

## OpenMMLab 2.0 标准数据集

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