[Docs] translate prepare_data.md into Chinese (#166)
* [Docs] translate prepare_data.md into Chinese * [Docs] fix typo in prepare_data.mdpull/168/head
Jiahao Xie
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# Prepare Datasets
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MMSelfsup supports multiple datasets. Please follow the corresponding guidelines for data preparation. It is recommended to symlink your dataset root to `$MMSELFSUP/data`. If your folder structure is different, you may need to change the corresponding paths in config files.
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MMSelfSup supports multiple datasets. Please follow the corresponding guidelines for data preparation. It is recommended to symlink your dataset root to `$MMSELFSUP/data`. If your folder structure is different, you may need to change the corresponding paths in config files.
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- [Prepare ImageNet](#prepare-imagenet)
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- [Prepare Place205](#prepare-place205)
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# Prepare Datasets
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# 准备数据集
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MMSelfsup supports multiple datasets. Please follow the corresponding guidelines for data preparation. It is recommended to symlink your dataset root to `$MMSELFSUP/data`. If your folder structure is different, you may need to change the corresponding paths in config files.
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MMSelfSup 支持多个数据集。请遵循相应的数据准备指南。建议将您的数据集根目录软链接到 `$MMSELFSUP/data`。如果您的文件夹结构不同，您可能需要更改配置文件中的相应路径。
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- [Prepare ImageNet](#prepare-imagenet)
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- [Prepare Place205](#prepare-place205)
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- [Prepare iNaturalist2018](#prepare-inaturalist2018)
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- [Prepare PASCAL VOC](#prepare-pascal-voc)
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- [Prepare CIFAR10](#prepare-cifar10)
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- [Prepare datasets for detection and segmentation](#prepare-datasets-for-detection-and-segmentation)
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- [Detection](#detection)
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- [Segmentation](#segmentation)
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- [准备 ImageNet 数据集](#准备-imagenet-数据集)
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- [准备 Places205 数据集](#准备-places205-数据集)
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- [准备 iNaturalist2018 数据集](#准备-inaturalist2018-数据集)
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- [准备 PASCAL VOC 数据集](#准备-pascal-voc-数据集)
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- [准备 CIFAR10 数据集](#准备-cifar10-数据集)
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- [准备检测和分割数据集](#准备检测和分割数据集)
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- [检测](#检测)
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- [分割](#分割)
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```
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mmselfsup
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```
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## Prepare ImageNet
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## 准备 ImageNet 数据集
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For ImageNet, it has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). It can be accessed with the following steps:
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对于 ImageNet，它有多个版本，但最常用的是 [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/)。可以通过以下步骤得到：
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1. Register an account and login to the [download page](http://www.image-net.org/download-images)
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2. Find download links for ILSVRC2012 and download the following two files
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1. 注册账号并登录 [下载页面](http://www.image-net.org/download-images)
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2. 找到 ILSVRC2012 的下载链接，下载以下两个文件
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- ILSVRC2012_img_train.tar (~138GB)
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- ILSVRC2012_img_val.tar (~6.3GB)
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3. Untar the downloaded files
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4. Download meta data using this [script](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh)
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3. 解压下载的文件
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4. 使用这个 [脚本](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) 下载元数据
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## Prepare Place205
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## 准备 Places205 数据集
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For Places205, you need to:
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对于 Places205，您需要：
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1. Register an account and login to the [download page](http://places.csail.mit.edu/downloadData.html)
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2. Download the resized images and the image list of train set and validation set of Places205
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3. Untar the downloaded files
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1. 注册账号并登录 [下载页面](http://places.csail.mit.edu/downloadData.html)
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2. 下载 Places205 经过缩放的图片以及训练集和验证集的图片列表
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3. 解压下载的文件
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## Prepare iNaturalist2018
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## 准备 iNaturalist2018 数据集
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For iNaturalist2018, you need to:
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对于 iNaturalist2018，您需要：
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1. Download the training and validation images and annotations from the [download page](https://github.com/visipedia/inat_comp/tree/master/2018)
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2. Untar the downloaded files
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3. Convert the original json annotation format to the list format using the script `tools/data_converters/convert_inaturalist.py`
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1. 从 [下载页面](https://github.com/visipedia/inat_comp/tree/master/2018) 下载训练集和验证集图像及标注
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2. 解压下载的文件
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3. 使用脚本 `tools/data_converters/convert_inaturalist.py` 将原来的 json 标注格式转换为列表格式
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## Prepare PASCAL VOC
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## 准备 PASCAL VOC 数据集
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Assuming that you usually store datasets in `$YOUR_DATA_ROOT`. The following command will automatically download PASCAL VOC 2007 into `$YOUR_DATA_ROOT`, prepare the required files, create a folder `data` under `$MMSELFSUP` and make a symlink `VOCdevkit`.
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假设您通常将数据集存储在 `$YOUR_DATA_ROOT` 中。下面的命令会自动将 PASCAL VOC 2007 下载到 `$YOUR_DATA_ROOT` 中，准备好所需的文件，在 `$MMSELFSUP` 下创建一个文件夹 `data`，并制作一个软链接 `VOCdevkit`。
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```shell
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bash tools/data_converters/prepare_voc07_cls.sh $YOUR_DATA_ROOT
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```
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## Prepare CIFAR10
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## 准备 CIFAR10 数据集
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CIFAR10 will be downloaded automatically if it is not found. In addition, `dataset` implemented by `MMSelfSup` will also automatically structure CIFAR10 to the appropriate format.
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如果没有找到 CIFAR10 系统将会自动下载。此外，由 `MMSelfSup` 实现的 `dataset` 也会自动将 CIFAR10 转换为适当的格式。
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## Prepare datasets for detection and segmentation
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## 准备检测和分割数据集
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### Detection
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### 检测
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To prepare COCO, VOC2007 and VOC2012 for detection, you can refer to [mmdet](https://github.com/open-mmlab/mmdetection/blob/master/docs/1_exist_data_model.md).
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您可以参考 [mmdet](https://github.com/open-mmlab/mmdetection/blob/master/docs/1_exist_data_model.md) 来准备 COCO，VOC2007 和 VOC2012 检测数据集。
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### Segmentation
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### 分割
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To prepare VOC2012AUG and Cityscapes for segmentation, you can refer to [mmseg](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#prepare-datasets)
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您可以参考 [mmseg](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/dataset_prepare.md#prepare-datasets) 来准备 VOC2012AUG 和 Cityscapes 分割数据集。
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