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[Doc] update opendatalab download link (add postfix) (open-mmlab#9738)
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docs/en/user_guides/dataset_prepare.md

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@@ -7,7 +7,7 @@ It is recommended to download and extract the dataset somewhere outside the proj
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If your folder structure is different, you may need to change the corresponding paths in config files.
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We provide a script to download datasets such as COCO, you can run `python tools/misc/download_dataset.py --dataset-name coco2017` to download COCO dataset.
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For users in China, more datasets can be downloaded from the opensource dataset platform: [OpenDataLab](https://opendatalab.com/).
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For users in China, more datasets can be downloaded from the opensource dataset platform: [OpenDataLab](https://opendatalab.com/?source=OpenMMLab%20GitHub).
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For more usage please refer to [dataset-download](./useful_tools.md#dataset-download)
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docs/en/user_guides/useful_tools.md

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python tools/misc/download_dataset.py --dataset-name lvis
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```
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For users in China, these datasets can also be downloaded from [OpenDataLab](https://opendatalab.com/) with high speed:
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For users in China, these datasets can also be downloaded from [OpenDataLab](https://opendatalab.com/?source=OpenMMLab%20GitHub) with high speed:
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- [COCO2017](https://opendatalab.com/COCO_2017/download)
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- [VOC2007](https://opendatalab.com/PASCAL_VOC2007/download)
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- [VOC2012](https://opendatalab.com/PASCAL_VOC2012/download)
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- [LVIS](https://opendatalab.com/LVIS/download)
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- [COCO2017](https://opendatalab.com/COCO_2017/download?source=OpenMMLab%20GitHub)
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- [VOC2007](https://opendatalab.com/PASCAL_VOC2007/download?source=OpenMMLab%20GitHub)
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- [VOC2012](https://opendatalab.com/PASCAL_VOC2012/download?source=OpenMMLab%20GitHub)
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- [LVIS](https://opendatalab.com/LVIS/download?source=OpenMMLab%20GitHub)
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## Benchmark
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docs/zh_cn/user_guides/dataset_prepare.md

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我们建议将数据集下载,然后解压到项目外部的某个文件夹内,然后通过符号链接的方式,将数据集根目录链接到 `$MMDETECTION/data` 文件夹下,格式如下所示。
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如果你的文件夹结构和下方不同的话,你需要在配置文件中改变对应的路径。
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我们提供了下载 COCO 等数据集的脚本,你可以运行 `python tools/misc/download_dataset.py --dataset-name coco2017` 下载 COCO 数据集。
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对于中国境内的用户,我们也推荐通过开源数据平台 [OpenDataLab](https://opendatalab.com/) 来下载数据,以获得更好的下载体验。
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对于中国境内的用户,我们也推荐通过开源数据平台 [OpenDataLab](https://opendatalab.com/?source=OpenMMLab%20GitHub) 来下载数据,以获得更好的下载体验。
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```plain
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mmdetection

docs/zh_cn/user_guides/useful_tools.md

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python tools/misc/download_dataset.py --dataset-name lvis
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```
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对于中国境内的用户,我们也推荐使用开源数据平台 [OpenDataLab](https://opendatalab.com/) 来获取这些数据集,以获得更好的下载体验:
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对于中国境内的用户,我们也推荐使用开源数据平台 [OpenDataLab](https://opendatalab.com/?source=OpenMMLab%20GitHub) 来获取这些数据集,以获得更好的下载体验:
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- [COCO2017](https://opendatalab.com/COCO_2017/download)
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- [VOC2007](https://opendatalab.com/PASCAL_VOC2007/download)
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- [VOC2012](https://opendatalab.com/PASCAL_VOC2012/download)
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- [LVIS](https://opendatalab.com/LVIS/download)
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- [COCO2017](https://opendatalab.com/COCO_2017/download?source=OpenMMLab%20GitHub)
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- [VOC2007](https://opendatalab.com/PASCAL_VOC2007/download?source=OpenMMLab%20GitHub)
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- [VOC2012](https://opendatalab.com/PASCAL_VOC2012/download?source=OpenMMLab%20GitHub)
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- [LVIS](https://opendatalab.com/LVIS/download?source=OpenMMLab%20GitHub)
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## 基准测试
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