# Text Detection ```{note} This page is a manual preparation guide for datasets not yet supported by [Dataset Preparer](./dataset_preparer.md), which all these scripts will be eventually migrated into. ``` ## Overview | Dataset | Images | | Annotation Files | | | | :---------------: | :------------------------------------------------------: | :------------------------------------------------: | :-----------------------------------------------------------------: | :-----: | :-: | | | | training | validation | testing | | | ICDAR2011 | [homepage](https://rrc.cvc.uab.es/?ch=1) | - | - | | | | ICDAR2017 | [homepage](https://rrc.cvc.uab.es/?ch=8&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json) | [instances_val.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json) | - | | | CurvedSynText150k | [homepage](https://github.com/aim-uofa/AdelaiDet/blob/master/datasets/README.md) \| [Part1](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) \| [Part2](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) | [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) | - | - | | | DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | - | | | Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - | | | LSVT | [homepage](https://rrc.cvc.uab.es/?ch=16) | - | - | - | | | IMGUR | [homepage](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset) | - | - | - | | | KAIST | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) | - | - | - | | | MTWI | [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us) | - | - | - | | | ReCTS | [homepage](https://rrc.cvc.uab.es/?ch=12) | - | - | - | | | IIIT-ILST | [homepage](http://cvit.iiit.ac.in/research/projects/cvit-projects/iiit-ilst) | - | - | - | | | VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - | - | | | BID | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) | - | - | - | | | RCTW | [homepage](https://rctw.vlrlab.net/index.html) | - | - | - | | | HierText | [homepage](https://github.com/google-research-datasets/hiertext) | - | - | - | | | ArT | [homepage](https://rrc.cvc.uab.es/?ch=14) | - | - | - | | ### Install AWS CLI (optional) - Since there are some datasets that require the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) to be installed in advance, we provide a quick installation guide here: ```bash curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" unzip awscliv2.zip sudo ./aws/install ./aws/install -i /usr/local/aws-cli -b /usr/local/bin !aws configure # this command will require you to input keys, you can skip them except # for the Default region name # AWS Access Key ID [None]: # AWS Secret Access Key [None]: # Default region name [None]: us-east-1 # Default output format [None] ``` For users in China, these datasets can also be downloaded from [OpenDataLab](https://opendatalab.com/) with high speed: - [CTW1500](https://opendatalab.com/SCUT-CTW1500?source=OpenMMLab%20GitHub) - [ICDAR2013](https://opendatalab.com/ICDAR_2013?source=OpenMMLab%20GitHub) - [ICDAR2015](https://opendatalab.com/ICDAR2015?source=OpenMMLab%20GitHub) - [Totaltext](https://opendatalab.com/TotalText?source=OpenMMLab%20GitHub) - [MSRA-TD500](https://opendatalab.com/MSRA-TD500?source=OpenMMLab%20GitHub) ## Important Note ```{note} **For users who want to train models on CTW1500, ICDAR 2015/2017, and Totaltext dataset,** there might be some images containing orientation info in EXIF data. The default OpenCV backend used in MMCV would read them and apply the rotation on the images. However, their gold annotations are made on the raw pixels, and such inconsistency results in false examples in the training set. Therefore, users should use `dict(type='LoadImageFromFile', color_type='color_ignore_orientation')` in pipelines to change MMCV's default loading behaviour. (see [DBNet's pipeline config](https://github.com/open-mmlab/mmocr/blob/main/configs/_base_/det_pipelines/dbnet_pipeline.py) for example) ``` ## ICDAR 2011 (Born-Digital Images) - Step1: Download `Challenge1_Training_Task12_Images.zip`, `Challenge1_Training_Task1_GT.zip`, `Challenge1_Test_Task12_Images.zip`, and `Challenge1_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=1&com=downloads) `Task 1.1: Text Localization (2013 edition)`. ```bash mkdir icdar2011 && cd icdar2011 mkdir imgs && mkdir annotations # Download ICDAR 2011 wget https://rrc.cvc.uab.es/downloads/Challenge1_Training_Task12_Images.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/Challenge1_Training_Task1_GT.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/Challenge1_Test_Task12_Images.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/Challenge1_Test_Task1_GT.zip --no-check-certificate # For images unzip -q Challenge1_Training_Task12_Images.zip -d imgs/training unzip -q Challenge1_Test_Task12_Images.zip -d imgs/test # For annotations unzip -q Challenge1_Training_Task1_GT.zip -d annotations/training unzip -q Challenge1_Test_Task1_GT.zip -d annotations/test rm Challenge1_Training_Task12_Images.zip && rm Challenge1_Test_Task12_Images.zip && rm Challenge1_Training_Task1_GT.zip && rm Challenge1_Test_Task1_GT.zip ``` - Step 2: Generate `instances_training.json` and `instances_test.json` with the following command: ```bash python tools/dataset_converters/textdet/ic11_converter.py PATH/TO/icdar2011 --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── icdar2011 │ ├── imgs │ ├── instances_test.json │ └── instances_training.json ``` ## ICDAR 2017 - Follow similar steps as [ICDAR 2015](#icdar-2015). - The resulting directory structure looks like the following: ```text ├── icdar2017 │   ├── imgs │   ├── annotations │   ├── instances_training.json │   └── instances_val.json ``` ## CurvedSynText150k - Step1: Download [syntext1.zip](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) and [syntext2.zip](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) to `CurvedSynText150k/`. - Step2: ```bash unzip -q syntext1.zip mv train.json train1.json unzip images.zip rm images.zip unzip -q syntext2.zip mv train.json train2.json unzip images.zip rm images.zip ``` - Step3: Download [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) to `CurvedSynText150k/` - Or, generate `instances_training.json` with following command: ```bash python tools/dataset_converters/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4 ``` - The resulting directory structure looks like the following: ```text ├── CurvedSynText150k │   ├── syntext_word_eng │   ├── emcs_imgs │   └── instances_training.json ``` ## DeText - Step1: Download `ch9_training_images.zip`, `ch9_training_localization_transcription_gt.zip`, `ch9_validation_images.zip`, and `ch9_validation_localization_transcription_gt.zip` from **Task 3: End to End** on the [homepage](https://rrc.cvc.uab.es/?ch=9). ```bash mkdir detext && cd detext mkdir imgs && mkdir annotations && mkdir imgs/training && mkdir imgs/val && mkdir annotations/training && mkdir annotations/val # Download DeText wget https://rrc.cvc.uab.es/downloads/ch9_training_images.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/ch9_training_localization_transcription_gt.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/ch9_validation_images.zip --no-check-certificate wget https://rrc.cvc.uab.es/downloads/ch9_validation_localization_transcription_gt.zip --no-check-certificate # Extract images and annotations unzip -q ch9_training_images.zip -d imgs/training && unzip -q ch9_training_localization_transcription_gt.zip -d annotations/training && unzip -q ch9_validation_images.zip -d imgs/val && unzip -q ch9_validation_localization_transcription_gt.zip -d annotations/val # Remove zips rm ch9_training_images.zip && rm ch9_training_localization_transcription_gt.zip && rm ch9_validation_images.zip && rm ch9_validation_localization_transcription_gt.zip ``` - Step2: Generate `instances_training.json` and `instances_val.json` with following command: ```bash python tools/dataset_converters/textdet/detext_converter.py PATH/TO/detext --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── detext │   ├── annotations │   ├── imgs │   ├── instances_test.json │   └── instances_training.json ``` ## Lecture Video DB - Step1: Download [IIIT-CVid.zip](http://cdn.iiit.ac.in/cdn/preon.iiit.ac.in/~kartik/IIIT-CVid.zip) to `lv/`. ```bash mkdir lv && cd lv # Download LV dataset wget http://cdn.iiit.ac.in/cdn/preon.iiit.ac.in/~kartik/IIIT-CVid.zip unzip -q IIIT-CVid.zip mv IIIT-CVid/Frames imgs rm IIIT-CVid.zip ``` - Step2: Generate `instances_training.json`, `instances_val.json`, and `instances_test.json` with following command: ```bash python tools/dataset_converters/textdet/lv_converter.py PATH/TO/lv --nproc 4 ``` - The resulting directory structure looks like the following: ```text │── lv │   ├── imgs │   ├── instances_test.json │   ├── instances_training.json │   └── instances_val.json ``` ## LSVT - Step1: Download [train_full_images_0.tar.gz](https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_0.tar.gz), [train_full_images_1.tar.gz](https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_1.tar.gz), and [train_full_labels.json](https://dataset-bj.cdn.bcebos.com/lsvt/train_full_labels.json) to `lsvt/`. ```bash mkdir lsvt && cd lsvt # Download LSVT dataset wget https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_0.tar.gz wget https://dataset-bj.cdn.bcebos.com/lsvt/train_full_images_1.tar.gz wget https://dataset-bj.cdn.bcebos.com/lsvt/train_full_labels.json mkdir annotations tar -xf train_full_images_0.tar.gz && tar -xf train_full_images_1.tar.gz mv train_full_labels.json annotations/ && mv train_full_images_1/*.jpg train_full_images_0/ mv train_full_images_0 imgs rm train_full_images_0.tar.gz && rm train_full_images_1.tar.gz && rm -rf train_full_images_1 ``` - Step2: Generate `instances_training.json` and `instances_val.json` (optional) with the following command: ```bash # Annotations of LSVT test split is not publicly available, split a validation # set by adding --val-ratio 0.2 python tools/dataset_converters/textdet/lsvt_converter.py PATH/TO/lsvt ``` - After running the above codes, the directory structure should be as follows: ```text |── lsvt │   ├── imgs │   ├── instances_training.json │   └── instances_val.json (optional) ``` ## IMGUR - Step1: Run `download_imgur5k.py` to download images. You can merge [PR#5](https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset/pull/5) in your local repository to enable a **much faster** parallel execution of image download. ```bash mkdir imgur && cd imgur git clone https://github.com/facebookresearch/IMGUR5K-Handwriting-Dataset.git # Download images from imgur.com. This may take SEVERAL HOURS! python ./IMGUR5K-Handwriting-Dataset/download_imgur5k.py --dataset_info_dir ./IMGUR5K-Handwriting-Dataset/dataset_info/ --output_dir ./imgs # For annotations mkdir annotations mv ./IMGUR5K-Handwriting-Dataset/dataset_info/*.json annotations rm -rf IMGUR5K-Handwriting-Dataset ``` - Step2: Generate `instances_train.json`, `instance_val.json` and `instances_test.json` with the following command: ```bash python tools/dataset_converters/textdet/imgur_converter.py PATH/TO/imgur ``` - After running the above codes, the directory structure should be as follows: ```text │── imgur │ ├── annotations │ ├── imgs │ ├── instances_test.json │ ├── instances_training.json │ └── instances_val.json ``` ## KAIST - Step1: Complete download [KAIST_all.zip](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) to `kaist/`. ```bash mkdir kaist && cd kaist mkdir imgs && mkdir annotations # Download KAIST dataset wget http://www.iapr-tc11.org/dataset/KAIST_SceneText/KAIST_all.zip unzip -q KAIST_all.zip rm KAIST_all.zip ``` - Step2: Extract zips: ```bash python tools/dataset_converters/common/extract_kaist.py PATH/TO/kaist ``` - Step3: Generate `instances_training.json` and `instances_val.json` (optional) with following command: ```bash # Since KAIST does not provide an official split, you can split the dataset by adding --val-ratio 0.2 python tools/dataset_converters/textdet/kaist_converter.py PATH/TO/kaist --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── kaist │ ├── annotations │ ├── imgs │ ├── instances_training.json │ └── instances_val.json (optional) ``` ## MTWI - Step1: Download `mtwi_2018_train.zip` from [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us). ```bash mkdir mtwi && cd mtwi unzip -q mtwi_2018_train.zip mv image_train imgs && mv txt_train annotations rm mtwi_2018_train.zip ``` - Step2: Generate `instances_training.json` and `instance_val.json` (optional) with the following command: ```bash # Annotations of MTWI test split is not publicly available, split a validation # set by adding --val-ratio 0.2 python tools/dataset_converters/textdet/mtwi_converter.py PATH/TO/mtwi --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── mtwi │ ├── annotations │ ├── imgs │ ├── instances_training.json │ └── instances_val.json (optional) ``` ## ReCTS - Step1: Download [ReCTS.zip](https://datasets.cvc.uab.es/rrc/ReCTS.zip) to `rects/` from the [homepage](https://rrc.cvc.uab.es/?ch=12&com=downloads). ```bash mkdir rects && cd rects # Download ReCTS dataset # You can also find Google Drive link on the dataset homepage wget https://datasets.cvc.uab.es/rrc/ReCTS.zip --no-check-certificate unzip -q ReCTS.zip mv img imgs && mv gt_unicode annotations rm ReCTS.zip && rm -rf gt ``` - Step2: Generate `instances_training.json` and `instances_val.json` (optional) with following command: ```bash # Annotations of ReCTS test split is not publicly available, split a validation # set by adding --val-ratio 0.2 python tools/dataset_converters/textdet/rects_converter.py PATH/TO/rects --nproc 4 --val-ratio 0.2 ``` - After running the above codes, the directory structure should be as follows: ```text │── rects │ ├── annotations │ ├── imgs │ ├── instances_val.json (optional) │ └── instances_training.json ``` ## ILST - Step1: Download `IIIT-ILST` from [onedrive](https://iiitaphyd-my.sharepoint.com/:f:/g/personal/minesh_mathew_research_iiit_ac_in/EtLvCozBgaBIoqglF4M-lHABMgNcCDW9rJYKKWpeSQEElQ?e=zToXZP) - Step2: Run the following commands ```bash unzip -q IIIT-ILST.zip && rm IIIT-ILST.zip cd IIIT-ILST # rename files cd Devanagari && for i in `ls`; do mv -f $i `echo "devanagari_"$i`; done && cd .. cd Malayalam && for i in `ls`; do mv -f $i `echo "malayalam_"$i`; done && cd .. cd Telugu && for i in `ls`; do mv -f $i `echo "telugu_"$i`; done && cd .. # transfer image path mkdir imgs && mkdir annotations mv Malayalam/{*jpg,*jpeg} imgs/ && mv Malayalam/*xml annotations/ mv Devanagari/*jpg imgs/ && mv Devanagari/*xml annotations/ mv Telugu/*jpeg imgs/ && mv Telugu/*xml annotations/ # remove unnecessary files rm -rf Devanagari && rm -rf Malayalam && rm -rf Telugu && rm -rf README.txt ``` - Step3: Generate `instances_training.json` and `instances_val.json` (optional). Since the original dataset doesn't have a validation set, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example. ```bash python tools/dataset_converters/textdet/ilst_converter.py PATH/TO/IIIT-ILST --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── IIIT-ILST │   ├── annotations │   ├── imgs │   ├── instances_val.json (optional) │   └── instances_training.json ``` ## VinText - Step1: Download [vintext.zip](https://drive.google.com/drive/my-drive) to `vintext` ```bash mkdir vintext && cd vintext # Download dataset from google drive wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml' -O- │ sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml" -O vintext.zip && rm -rf /tmp/cookies.txt # Extract images and annotations unzip -q vintext.zip && rm vintext.zip mv vietnamese/labels ./ && mv vietnamese/test_image ./ && mv vietnamese/train_images ./ && mv vietnamese/unseen_test_images ./ rm -rf vietnamese # Rename files mv labels annotations && mv test_image test && mv train_images training && mv unseen_test_images unseen_test mkdir imgs mv training imgs/ && mv test imgs/ && mv unseen_test imgs/ ``` - Step2: Generate `instances_training.json`, `instances_test.json` and `instances_unseen_test.json` ```bash python tools/dataset_converters/textdet/vintext_converter.py PATH/TO/vintext --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── vintext │   ├── annotations │   ├── imgs │   ├── instances_test.json │   ├── instances_unseen_test.json │   └── instances_training.json ``` ## BID - Step1: Download [BID Dataset.zip](https://drive.google.com/file/d/1Oi88TRcpdjZmJ79WDLb9qFlBNG8q2De6/view) - Step2: Run the following commands to preprocess the dataset ```bash # Rename mv BID\ Dataset.zip BID_Dataset.zip # Unzip and Rename unzip -q BID_Dataset.zip && rm BID_Dataset.zip mv BID\ Dataset BID # The BID dataset has a problem of permission, and you may # add permission for this file chmod -R 777 BID cd BID mkdir imgs && mkdir annotations # For images and annotations mv CNH_Aberta/*in.jpg imgs && mv CNH_Aberta/*txt annotations && rm -rf CNH_Aberta mv CNH_Frente/*in.jpg imgs && mv CNH_Frente/*txt annotations && rm -rf CNH_Frente mv CNH_Verso/*in.jpg imgs && mv CNH_Verso/*txt annotations && rm -rf CNH_Verso mv CPF_Frente/*in.jpg imgs && mv CPF_Frente/*txt annotations && rm -rf CPF_Frente mv CPF_Verso/*in.jpg imgs && mv CPF_Verso/*txt annotations && rm -rf CPF_Verso mv RG_Aberto/*in.jpg imgs && mv RG_Aberto/*txt annotations && rm -rf RG_Aberto mv RG_Frente/*in.jpg imgs && mv RG_Frente/*txt annotations && rm -rf RG_Frente mv RG_Verso/*in.jpg imgs && mv RG_Verso/*txt annotations && rm -rf RG_Verso # Remove unnecessary files rm -rf desktop.ini ``` - Step3: - Step3: Generate `instances_training.json` and `instances_val.json` (optional). Since the original dataset doesn't have a validation set, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example. ```bash python tools/dataset_converters/textdet/bid_converter.py PATH/TO/BID --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── BID │   ├── annotations │   ├── imgs │   ├── instances_training.json │   └── instances_val.json (optional) ``` ## RCTW - Step1: Download `train_images.zip.001`, `train_images.zip.002`, and `train_gts.zip` from the [homepage](https://rctw.vlrlab.net/dataset.html), extract the zips to `rctw/imgs` and `rctw/annotations`, respectively. - Step2: Generate `instances_training.json` and `instances_val.json` (optional). Since the test annotations are not publicly available, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example. ```bash # Annotations of RCTW test split is not publicly available, split a validation set by adding --val-ratio 0.2 python tools/dataset_converters/textdet/rctw_converter.py PATH/TO/rctw --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── rctw │   ├── annotations │   ├── imgs │   ├── instances_training.json │   └── instances_val.json (optional) ``` ## HierText - Step1 (optional): Install [AWS CLI](https://mmocr.readthedocs.io/en/latest/datasets/det.html#install-aws-cli-optional). - Step2: Clone [HierText](https://github.com/google-research-datasets/hiertext) repo to get annotations ```bash mkdir HierText git clone https://github.com/google-research-datasets/hiertext.git ``` - Step3: Download `train.tgz`, `validation.tgz` from aws ```bash aws s3 --no-sign-request cp s3://open-images-dataset/ocr/train.tgz . aws s3 --no-sign-request cp s3://open-images-dataset/ocr/validation.tgz . ``` - Step4: Process raw data ```bash # process annotations mv hiertext/gt ./ rm -rf hiertext mv gt annotations gzip -d annotations/train.jsonl.gz gzip -d annotations/validation.jsonl.gz # process images mkdir imgs mv train.tgz imgs/ mv validation.tgz imgs/ tar -xzvf imgs/train.tgz tar -xzvf imgs/validation.tgz ``` - Step5: Generate `instances_training.json` and `instance_val.json`. HierText includes different levels of annotation, from paragraph, line, to word. Check the original [paper](https://arxiv.org/pdf/2203.15143.pdf) for details. E.g. set `--level paragraph` to get paragraph-level annotation. Set `--level line` to get line-level annotation. set `--level word` to get word-level annotation. ```bash # Collect word annotation from HierText --level word python tools/dataset_converters/textdet/hiertext_converter.py PATH/TO/HierText --level word --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── HierText │   ├── annotations │   ├── imgs │   ├── instances_training.json │   └── instances_val.json ``` ## ArT - Step1: Download `train_images.tar.gz`, and `train_labels.json` from the [homepage](https://rrc.cvc.uab.es/?ch=14&com=downloads) to `art/` ```bash mkdir art && cd art mkdir annotations # Download ArT dataset wget https://dataset-bj.cdn.bcebos.com/art/train_images.tar.gz --no-check-certificate wget https://dataset-bj.cdn.bcebos.com/art/train_labels.json --no-check-certificate # Extract tar -xf train_images.tar.gz mv train_images imgs mv train_labels.json annotations/ # Remove unnecessary files rm train_images.tar.gz ``` - Step2: Generate `instances_training.json` and `instances_val.json` (optional). Since the test annotations are not publicly available, you may specify `--val-ratio` to split the dataset. E.g., if val-ratio is 0.2, then 20% of the data are left out as the validation set in this example. ```bash # Annotations of ArT test split is not publicly available, split a validation set by adding --val-ratio 0.2 python tools/data/textdet/art_converter.py PATH/TO/art --nproc 4 ``` - After running the above codes, the directory structure should be as follows: ```text │── art │   ├── annotations │   ├── imgs │   ├── instances_training.json │   └── instances_val.json (optional) ```