diff --git a/docs/en/guide_to_framework.md b/docs/en/guide_to_framework.md
index 953aa589b3..167a65d949 100644
--- a/docs/en/guide_to_framework.md
+++ b/docs/en/guide_to_framework.md
@@ -161,7 +161,7 @@ dataset_info = dict(
 - `joint_weights` assigns different loss weights to different keypoints.
 - `sigmas` is used to calculate the OKS score. You can read [keypoints-eval](https://cocodataset.org/#keypoints-eval) to learn more about it.
 
-In the model config, the user needs to specify the metainfo path of the custom dataset (e.g. `$MMPOSE/configs/_base_/datasets/custom.py`) as follows:
+In the model config, the user needs to specify the metainfo path of the custom dataset (e.g. `$MMPOSE/configs/_base_/datasets/{your_dataset}.py`) as follows:
 
 ```python
 # dataset and dataloader settings
@@ -171,9 +171,9 @@ train_dataloader = dict(
     batch_size=2,
     dataset=dict(
         type=dataset_type,
-        data_root='root/of/your/train/data',
-        ann_file='path/to/your/train/json',
-        data_prefix=dict(img='path/to/your/train/img'),
+        data_root='root of your train data',
+        ann_file='path to your json file',
+        data_prefix=dict(img='path to your train img'),
         # specify the new dataset meta information config file
         metainfo=dict(from_file='configs/_base_/datasets/custom.py'),
         ...),
@@ -183,9 +183,9 @@ val_dataloader = dict(
     batch_size=2,
     dataset=dict(
         type=dataset_type,
-        data_root='root/of/your/val/data',
-        ann_file='path/to/your/val/json',
-        data_prefix=dict(img='path/to/your/val/img'),
+        data_root='root of your val data',
+        ann_file='path to your val json',
+        data_prefix=dict(img='path to your val img'),
         # specify the new dataset meta information config file
         metainfo=dict(from_file='configs/_base_/datasets/custom.py'),
         ...),
@@ -194,6 +194,32 @@ val_dataloader = dict(
 test_dataloader = val_dataloader
 ```
 
+More specifically speaking, if you organize your data as follows:
+
+```shell
+data
+├── annotations
+│   ├── train.json
+│   ├── val.json
+├── train
+│   ├── images
+│   │   ├── 000001.jpg
+├── val
+│   ├── images
+│   │   ├── 000002.jpg
+```
+
+You need to set your config as follows:
+
+```
+dataset=dict(
+    ...
+    data_root='data/',
+    ann_file='annotations/train.json',
+    data_prefix=dict(img='train/images/'),
+    ...),
+```
+
 ### Dataset
 
 To use custom dataset in MMPose, we recommend converting the annotations into a supported format (e.g. COCO or MPII) and directly using our implementation of the corresponding dataset. If this is not applicable, you may need to implement your own dataset class.
diff --git a/docs/zh_cn/guide_to_framework.md b/docs/zh_cn/guide_to_framework.md
index 5585b88179..df86f8634f 100644
--- a/docs/zh_cn/guide_to_framework.md
+++ b/docs/zh_cn/guide_to_framework.md
@@ -167,7 +167,7 @@ dataset_info = dict(
 - `joint_weights`：每个关键点的权重，用于损失函数计算。
 - `sigma`：标准差，用于计算 OKS 分数，详细信息请参考 [keypoints-eval](https://cocodataset.org/#keypoints-eval)。
 
-在模型配置文件中，你需要为自定义数据集指定对应的元信息配置文件。假如该元信息配置文件路径为 `$MMPOSE/configs/_base_/datasets/custom.py`，指定方式如下：
+在模型配置文件中，你需要为自定义数据集指定对应的元信息配置文件。假如该元信息配置文件路径为 `$MMPOSE/configs/_base_/datasets/{your_dataset}.py`，指定方式如下：
 
 ```python
 # dataset and dataloader settings
@@ -176,9 +176,9 @@ train_dataloader = dict(
     batch_size=2,
     dataset=dict(
         type=dataset_type,
-        data_root='root/of/your/train/data',
-        ann_file='path/to/your/train/json',
-        data_prefix=dict(img='path/to/your/train/img'),
+        data_root='root of your train data',
+        ann_file='path to your json file',
+        data_prefix=dict(img='path to your train img'),
         # 指定对应的元信息配置文件
         metainfo=dict(from_file='configs/_base_/datasets/custom.py'),
         ...),
@@ -187,9 +187,9 @@ val_dataloader = dict(
     batch_size=2,
     dataset=dict(
         type=dataset_type,
-        data_root='root/of/your/val/data',
-        ann_file='path/to/your/val/json',
-        data_prefix=dict(img='path/to/your/val/img'),
+        data_root='root of your val data',
+        ann_file='path to your val json',
+        data_prefix=dict(img='path to your val img'),
         # 指定对应的元信息配置文件
         metainfo=dict(from_file='configs/_base_/datasets/custom.py'),
         ...),
@@ -197,6 +197,32 @@ val_dataloader = dict(
 test_dataloader = val_dataloader
 ```
 
+下面是一个更加具体的例子，假设你的数据集按照以下结构进行组织：
+
+```shell
+data
+├── annotations
+│   ├── train.json
+│   ├── val.json
+├── train
+│   ├── images
+│   │   ├── 000001.jpg
+├── val
+│   ├── images
+│   │   ├── 000002.jpg
+```
+
+你的数据集路径应该如下所示：
+
+```
+dataset=dict(
+    ...
+    data_root='data/',
+    ann_file='annotations/train.json',
+    data_prefix=dict(img='train/images/'),
+    ...),
+```
+
 ### 数据集
 
 在 MMPose 中使用自定义数据集时，我们推荐将数据转化为已支持的格式（如 COCO 或 MPII），并直接使用我们提供的对应数据集实现。如果这种方式不可行，则用户需要实现自己的数据集类。