Update dataset_prepare.md

docs/en/user_guides/dataset_prepare.md

@@ -255,6 +255,78 @@ val_dataloader = dict(
     )
 )
 
+test_dataloader = val_dataloader
+```
+## RefCOCO Dataset
+
+RefCOCO is a popular dataset used for the task of visual grounding. Here are the steps for accessing and utilizing the RefCOCO dataset.
+
+### Downloading
+
+The RefCOCO dataset can be downloaded directly from the following link:
+[https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip](https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip)
+After downloading, you need to unzip the file to retrieve the dataset.
+
+### Official Source
+
+Alternatively, you can access the RefCOCO dataset from the official source:
+[https://github.com/lichengunc/refer](https://github.com/lichengunc/refer)
+
+### Directory Structure of the RefCOCO Dataset
+
+The RefCOCO dataset is organized in a structured format. The structure of the features is as follows:
+
+```python
+FeaturesDict({
+    'coco_annotations': Sequence({
+        'area': int64,
+        'bbox': BBoxFeature(shape=(4,), dtype=float32),
+        'id': int64,
+        'label': int64,
+    }),
+    'image': Image(shape=(None, None, 3), dtype=uint8),
+    'image/id': int64,
+    'objects': Sequence({
+        'area': int64,
+        'bbox': BBoxFeature(shape=(4,), dtype=float32),
+        'gt_box_index': int64,
+        'id': int64,
+        'label': int64,
+        'refexp': Sequence({
+            'raw': Text(shape=(), dtype=string),
+            'refexp_id': int64,
+        }),
+    }),
+})
+```
+
+### Configuration
+
+Once the RefCOCO dataset is organized as above, you can configure your project to use the dataset. An example of how to set up a data loader using the RefCOCO dataset is provided below:
+
+```python
+train_dataloader = dict(
+    ...
+    # Training dataset configurations
+    dataset=dict(
+        type='RefCOCO',
+        data_root='data/refcoco',
+        split='train',
+        pipeline=...,
+    )
+)
+
+val_dataloader = dict(
+    ...
+    # Validation dataset configurations
+    dataset=dict(
+        type='RefCOCO',
+        data_root='data/refcoco',
+        split='val',
+        pipeline=...,
+    )
+)
+
 test_dataloader = val_dataloader
 ```
 
@@ -280,6 +352,14 @@ test_dataloader = val_dataloader
 
 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).
 
+## Supported Multi-modality Datasets
+
+| Datasets                                                                           | split                               | HomePage                                                                            |
+| ---------------------------------------------------------------------------------- | :---------------------------------- | ----------------------------------------------------------------------------------- |
+| [`RefCOCO`](mmpretrain.datasets.RefCOCO)(data_root[, split, pipeline, ...])       | ["train", "val", "test"]           | [RefCOCO](https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip) Dataset. |
+
+Some dataset homepage links may be unavailable, and you can download datasets through [OpenDataLab](https://opendatalab.com/), such as [RefCOCO](https://opendatalab.com/RefCOCO/download).
+
 ## OpenMMLab 2.0 Standard Dataset
 
 In order to facilitate the training of multi-task algorithm models, we unify the dataset interfaces of different tasks. OpenMMLab has formulated the **OpenMMLab 2.0 Dataset Format Specification**. When starting a trainning task, the users can choose to convert their dataset annotation into the specified format, and use the algorithm library of OpenMMLab to perform algorithm training and testing based on the data annotation file.