mmocr/docs/en/user_guides/dataset_prepare.md

# Dataset Preparation

## Introduction

After decades of development, the OCR community has produced a series of related datasets that often provide annotations of text in a variety of styles, making it necessary for users to convert these datasets to the required format when using them. MMOCR supports dozens of commonly used text-related datasets and provides a [data preparation script](./data_prepare/dataset_preparer.md) to help users prepare the datasets with only one command.

In this section, we will introduce a typical process of preparing a dataset for MMOCR:

1. [Download datasets and convert its format to the suggested one](#downloading-datasets-and-converting-format)
2. [Modify the config file](#dataset-configuration)

However, the first step is not necessary if you already have a dataset in the format that MMOCR supports. You can read [Dataset Classes](../basic_concepts/datasets.md#dataset-classes-and-annotation-formats) for more details.

## Downloading Datasets and Converting Format

As an example of the data preparation steps, you can use the following command to prepare the ICDAR 2015 dataset for text detection task.

```shell
python tools/dataset_converters/prepare_dataset.py icdar2015 --task textdet
```

Then, the dataset has been downloaded and converted to MMOCR format, and the file directory structure is as follows:

```text
data/icdar2015
├── textdet_imgs
│   ├── test
│   └── train
├── textdet_test.json
└── textdet_train.json
```

Once your dataset has been prepared, you can use the [browse_dataset.py](./useful_tools.md#dataset-visualization-tool) to visualize the dataset and check if the annotations are correct.

```bash
python tools/analysis_tools/browse_dataset.py configs/textdet/_base_/datasets/icdar2015.py
```

## Dataset Configuration

### Single Dataset Training

When training or evaluating a model on new datasets, we need to write the dataset config where the image path, annotation path, and image prefix are set. The path `configs/xxx/_base_/datasets/` is pre-configured with the commonly used datasets in MMOCR (if you use `prepare_dataset.py` to prepare dataset, this config will be generated automatically), here we take the ICDAR 2015 dataset as an example (see `configs/textdet/_base_/datasets/icdar2015.py`).

```Python
icdar2015_textdet_data_root = 'data/icdar2015' # dataset root path

# Train set config
icdar2015_textdet_train = dict(
    type='OCRDataset',
    data_root=icdar2015_textdet_data_root,               # dataset root path
    ann_file='textdet_train.json',                       # name of annotation
    filter_cfg=dict(filter_empty_gt=True, min_size=32),  # filtering empty images
    pipeline=None)
# Test set config
icdar2015_textdet_test = dict(
    type='OCRDataset',
    data_root=icdar2015_textdet_data_root,
    ann_file='textdet_test.json',
    test_mode=True,
    pipeline=None)
```

After configuring the dataset, we can import it in the corresponding model configs. For example, to train the "DBNet_R18" model on the ICDAR 2015 dataset.

```Python
_base_ = [
    '_base_dbnet_r18_fpnc.py',
    '../_base_/datasets/icdar2015.py',  # import the dataset config
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_sgd_1200e.py',
]

icdar2015_textdet_train = _base_.icdar2015_textdet_train            # specify the training set
icdar2015_textdet_train.pipeline = _base_.train_pipeline   # specify the training pipeline
icdar2015_textdet_test = _base_.icdar2015_textdet_test              # specify the testing set
icdar2015_textdet_test.pipeline = _base_.test_pipeline     # specify the testing pipeline

train_dataloader = dict(
    batch_size=16,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=icdar2015_textdet_train)    # specify the dataset in train_dataloader

val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=icdar2015_textdet_test)    # specify the dataset in val_dataloader

test_dataloader = val_dataloader
```

### Multi-dataset Training

In addition, [`ConcatDataset`](mmocr.datasets.ConcatDataset) enables users to train or test the model on a combination of multiple datasets. You just need to set the dataset type in the dataloader to `ConcatDataset` in the configuration file and specify the corresponding list of datasets.

```Python
train_list = [ic11, ic13, ic15]
train_dataloader = dict(
    dataset=dict(
        type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
```

For example, the following configuration uses the MJSynth dataset for training and 6 academic datasets (CUTE80, IIIT5K, SVT, SVTP, ICDAR2013, ICDAR2015) for testing.

```Python
_base_ = [ # Import all dataset configurations you want to use
    '../_base_/datasets/mjsynth.py',
    '../_base_/datasets/cute80.py',
    '../_base_/datasets/iiit5k.py',
    '../_base_/datasets/svt.py',
    '../_base_/datasets/svtp.py',
    '../_base_/datasets/icdar2013.py',
    '../_base_/datasets/icdar2015.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_adadelta_5e.py',
    '_base_crnn_mini-vgg.py',
]

# List of training datasets
train_list = [_base_.mjsynth_textrecog_train]
# List of testing datasets
test_list = [
    _base_.cute80_textrecog_test, _base_.iiit5k_textrecog_test, _base_.svt_textrecog_test,
    _base_.svtp_textrecog_test, _base_.icdar2013_textrecog_test, _base_.icdar2015_textrecog_test
]

# Use ConcatDataset to combine the datasets in the list
train_dataset = dict(
       type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
test_dataset = dict(
       type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)

train_dataloader = dict(
    batch_size=192 * 4,
    num_workers=32,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=train_dataset)

test_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=test_dataset)

val_dataloader = test_dataloader
```