mirror of https://github.com/open-mmlab/mmocr.git
204 lines
7.2 KiB
Markdown
204 lines
7.2 KiB
Markdown
# Dataset Preparation
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## Introduction
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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.
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In the following, we provide a brief overview of the data formats defined in MMOCR for each task.
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- 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.
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```json
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{
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"metainfo":
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{
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"dataset_type": "TextDetDataset",
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"task_name": "textdet",
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"category": [{"id": 0, "name": "text"}]
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},
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"data_list":
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[
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{
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"img_path": "test_img.jpg",
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"height": 640,
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"width": 640,
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"instances":
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[
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{
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"polygon": [0, 0, 0, 10, 10, 20, 20, 0],
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"bbox": [0, 0, 10, 20],
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"bbox_label": 0,
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"ignore": false,
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},
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],
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//...
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}
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]
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}
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```
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- 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.
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```json
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{
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"metainfo":
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{
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"dataset_type": "TextRecogDataset",
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"task_name": "textrecog",
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},
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"data_list":
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[
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{
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"img_path": "test_img.jpg",
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"instances":
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[
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{
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"text": "GRAND"
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}
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]
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}
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]
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}
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```
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## Downloading Datasets and Format Conversion
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As an example of the data preparation steps, you can use the following command to prepare the ICDAR 2015 dataset for text detection task.
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```shell
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python tools/dataset_converters/prepare_dataset.py icdar2015 --task textdet
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```
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Then, the dataset has been downloaded and converted to MMOCR format, and the file directory structure is as follows:
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```text
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data/icdar2015
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├── textdet_imgs
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│ ├── test
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│ └── train
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├── textdet_test.json
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└── textdet_train.json
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```
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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.
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```bash
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python tools/analysis_tools/browse_dataset.py configs/textdet/_base_/datasets/icdar2015.py
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```
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## Dataset Configuration
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### Single Dataset Training
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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`).
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```Python
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icdar2015_textdet_data_root = 'data/icdar2015' # dataset root path
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# Train set config
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icdar2015_textdet_train = dict(
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type='OCRDataset',
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data_root=icdar2015_textdet_data_root, # dataset root path
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ann_file='textdet_train.json', # name of annotation
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filter_cfg=dict(filter_empty_gt=True, min_size=32), # filtering empty images
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pipeline=None)
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# Test set config
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icdar2015_textdet_test = dict(
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type='OCRDataset',
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data_root=icdar2015_textdet_data_root,
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ann_file='textdet_test.json',
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test_mode=True,
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pipeline=None)
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```
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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.
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```Python
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_base_ = [
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'_base_dbnet_r18_fpnc.py',
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'../_base_/datasets/icdar2015.py', # import the dataset config
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'../_base_/default_runtime.py',
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'../_base_/schedules/schedule_sgd_1200e.py',
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]
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icdar2015_textdet_train = _base_.icdar2015_textdet_train # specify the training set
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icdar2015_textdet_train.pipeline = _base_.train_pipeline # specify the training pipeline
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icdar2015_textdet_test = _base_.icdar2015_textdet_test # specify the testing set
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icdar2015_textdet_test.pipeline = _base_.test_pipeline # specify the testing pipeline
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train_dataloader = dict(
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batch_size=16,
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num_workers=8,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=icdar2015_textdet_train) # specify the dataset in train_dataloader
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val_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=icdar2015_textdet_test) # specify the dataset in val_dataloader
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test_dataloader = val_dataloader
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```
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### Multi-dataset Training
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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.
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```Python
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train_list = [ic11, ic13, ic15]
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train_dataloader = dict(
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dataset=dict(
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type='ConcatDataset', datasets=train_list, pipeline=train_pipeline))
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```
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For example, the following configuration uses the MJSynth dataset for training and 6 academic datasets (CUTE80, IIIT5K, SVT, SVTP, ICDAR2013, ICDAR2015) for testing.
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```Python
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_base_ = [ # Import all dataset configurations you want to use
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'../_base_/datasets/mjsynth.py',
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'../_base_/datasets/cute80.py',
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'../_base_/datasets/iiit5k.py',
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'../_base_/datasets/svt.py',
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'../_base_/datasets/svtp.py',
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'../_base_/datasets/icdar2013.py',
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'../_base_/datasets/icdar2015.py',
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'../_base_/default_runtime.py',
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'../_base_/schedules/schedule_adadelta_5e.py',
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'_base_crnn_mini-vgg.py',
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]
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# List of training datasets
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train_list = [_base_.mjsynth_textrecog_test]
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# List of testing datasets
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test_list = [
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_base_.cute80_textrecog_test, _base_.iiit5k_textrecog_test, _base_.svt_textrecog_test,
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_base_.svtp_textrecog_test, _base_.icdar2013_textrecog_test, _base_.icdar2015_textrecog_test
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]
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# Use ConcatDataset to combine the datasets in the list
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train_dataset = dict(
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type='ConcatDataset', datasets=train_list, pipeline=_base_.train_pipeline)
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test_dataset = dict(
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type='ConcatDataset', datasets=test_list, pipeline=_base_.test_pipeline)
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train_dataloader = dict(
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batch_size=192 * 4,
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num_workers=32,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=True),
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dataset=train_dataset)
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test_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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drop_last=False,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=test_dataset)
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val_dataloader = test_dataloader
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```
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