7.2 KiB

Raw Blame History

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 to help users prepare the datasets with only one command.

In the following, we provide a brief overview of the data formats defined in MMOCR for each task.

As shown in the following code block, the text detection task uses the data format TextDetDataset, which holds the bounding box annotations, file names, and other information required for the text detection task. We provide a sample annotation file in the tests/data/det_toy_dataset/instances_test.json path.

{
  "metainfo":
    {
      "dataset_type": "TextDetDataset",
      "task_name": "textdet",
      "category": [{"id": 0, "name": "text"}]
    },
  "data_list":
    [
      {
        "img_path": "test_img.jpg",
        "height": 640,
        "width": 640,
        "instances":
          [
            {
              "polygon": [0, 0, 0, 10, 10, 20, 20, 0],
              "bbox": [0, 0, 10, 20],
              "bbox_label": 0,
              "ignore": false,
            },
          ],
          //...
      }
    ]
}

As shown in the following code block, the text recognition task uses the data format TextRecogDataset, which holds information such as text instances and image paths required by the text recognition task. An example annotation file is provided in the tests/data/rec_toy_dataset/labels.json path.

{
  "metainfo":
    {
      "dataset_type": "TextRecogDataset",
      "task_name": "textrecog",
    },
  "data_list":
    [
      {
        "img_path": "test_img.jpg",
        "instances":
          [
            {
              "text": "GRAND"
            }
          ]
        }
    ]
}

Downloading Datasets and Format Conversion

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

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:

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 to visualize the dataset and check if the annotations are correct.

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).

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.

_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 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.

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.

_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

7.2 KiB Raw Blame History