mmsegmentation/docs/en/advanced_guides/add_dataset.md

# Add New Datasets

## Customize datasets by reorganizing data

The simplest way is to convert your dataset to organize your data into folders.

An example of file structure is as followed.

```none
├── data
│   ├── my_dataset
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   │   ├── xxx{img_suffix}
│   │   │   │   ├── yyy{img_suffix}
│   │   │   │   ├── zzz{img_suffix}
│   │   │   ├── val
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   │   ├── xxx{seg_map_suffix}
│   │   │   │   ├── yyy{seg_map_suffix}
│   │   │   │   ├── zzz{seg_map_suffix}
│   │   │   ├── val

```

A training pair will consist of the files with same suffix in img_dir/ann_dir.

If `split` argument is given, only part of the files in img_dir/ann_dir will be loaded.
We may specify the prefix of files we would like to be included in the split txt.

More specifically, for a split txt like following,

```none
xxx
zzz
```

Only
`data/my_dataset/img_dir/train/xxx{img_suffix}`,
`data/my_dataset/img_dir/train/zzz{img_suffix}`,
`data/my_dataset/ann_dir/train/xxx{seg_map_suffix}`,
`data/my_dataset/ann_dir/train/zzz{seg_map_suffix}` will be loaded.

:::{note}
The annotations are images of shape (H, W), the value pixel should fall in range `[0, num_classes - 1]`.
You may use `'P'` mode of [pillow](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#palette) to create your annotation image with color.
:::

## Customize datasets by mixing dataset

MMSegmentation also supports to mix dataset for training.
Currently it supports to concat, repeat and multi-image mix datasets.

### Repeat dataset

We use `RepeatDataset` as wrapper to repeat the dataset.
For example, suppose the original dataset is `Dataset_A`, to repeat it, the config looks like the following

```python
dataset_A_train = dict(
        type='RepeatDataset',
        times=N,
        dataset=dict(  # This is the original config of Dataset_A
            type='Dataset_A',
            ...
            pipeline=train_pipeline
        )
    )
```

### Concatenate dataset

In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.

```python
dataset_A_train = dict()
dataset_B_train = dict()
concatenate_dataset = dict(
    type='ConcatDataset',
    datasets=[dataset_A_train, dataset_B_train])
```

A more complex example that repeats `Dataset_A` and `Dataset_B` by N and M times, respectively, and then concatenates the repeated datasets is as the following.

```python
dataset_A_train = dict(
    type='RepeatDataset',
    times=N,
    dataset=dict(
        type='Dataset_A',
        ...
        pipeline=train_pipeline
    )
)
dataset_A_val = dict(
    ...
    pipeline=test_pipeline
)
dataset_A_test = dict(
    ...
    pipeline=test_pipeline
)
dataset_B_train = dict(
    type='RepeatDataset',
    times=M,
    dataset=dict(
        type='Dataset_B',
        ...
        pipeline=train_pipeline
    )
)
train_dataloader = dict(
    dataset=dict('ConcatDataset', datasets=[dataset_A_train, dataset_B_train]))

val_dataloader = dict(dataset=dataset_A_val)
test_dataloader = dict(dataset=dataset_A_test)

```

You can refer base dataset [tutorial](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/basedataset.html) from mmengine for more details

### Multi-image Mix Dataset

We use `MultiImageMixDataset` as a wrapper to mix images from multiple datasets.
`MultiImageMixDataset` can be used by multiple images mixed data augmentation
like mosaic and mixup.

An example of using `MultiImageMixDataset` with `Mosaic` data augmentation:

```python
train_pipeline = [
    dict(type='RandomMosaic', prob=1),
    dict(type='Resize', img_scale=(1024, 512), keep_ratio=True),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PackSegInputs')
]

train_dataset = dict(
    type='MultiImageMixDataset',
    dataset=dict(
        classes=classes,
        palette=palette,
        type=dataset_type,
        reduce_zero_label=False,
        img_dir=data_root + "images/train",
        ann_dir=data_root + "annotations/train",
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations'),
        ]
    ),
    pipeline=train_pipeline
)

```