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Code Issues Projects Releases Wiki Activity

[Docs] Reorganize the directory structure section in det.md (#894)

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* [Docs] Reorganize the directory structure section in det.md

* improve

* fix indentation

* Fix structure

* sync dataset order to the overview

* format det doc

* fix |
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@ -3,51 +3,6 @@
## Overview
The structure of the text detection dataset directory is organized as follows.
```text
├── ctw1500
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
├── icdar2015
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
├── icdar2017
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json
├── synthtext
│   ├── imgs
│   └── instances_training.lmdb
│   ├── data.mdb
│   └── lock.mdb
├── textocr
│   ├── train
│   ├── instances_training.json
│   └── instances_val.json
├── totaltext
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
├── CurvedSynText150k
│   ├── syntext_word_eng
│   ├── emcs_imgs
│   └── instances_training.json
|── funsd
|   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
|── lv
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
│   └── instances_val.json
```
| Dataset | Images | | Annotation Files | | |
| :---------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :---: |
| | | training | validation | testing | |
@ -74,7 +29,6 @@ The structure of the text detection dataset directory is organized as follows.
| VinText | [homepage](https://github.com/VinAIResearch/dict-guided) | - | - | - |
| BID | [homepage](https://github.com/ricardobnjunior/Brazilian-Identity-Document-Dataset) | - | - | - |
## Important Note
:::{note}
@ -83,58 +37,48 @@ backend used in MMCV would read them and apply the rotation on the images. Howe
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)
:::
## Preparation Steps
### ICDAR 2015
- Step0: Read [Important Note](#important-note)
- Step1: Download `ch4_training_images.zip`, `ch4_test_images.zip`, `ch4_training_localization_transcription_gt.zip`, `Challenge4_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads)
- Step2:
```bash
mkdir icdar2015 && cd icdar2015
mkdir imgs && mkdir annotations
# For images,
mv ch4_training_images imgs/training
mv ch4_test_images imgs/test
# For annotations,
mv ch4_training_localization_transcription_gt annotations/training
mv Challenge4_Test_Task1_GT annotations/test
```
- Step3: Download [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) and [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) and move them to `icdar2015`
- Or, generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test
```
## CTW1500
### ICDAR 2017
- Follow similar steps as [ICDAR 2015](#icdar-2015).
### CTW1500
- Step0: Read [Important Note](#important-note)
- Step1: Download `train_images.zip`, `test_images.zip`, `train_labels.zip`, `test_labels.zip` from [github](https://github.com/Yuliang-Liu/Curve-Text-Detector)
```bash
mkdir ctw1500 && cd ctw1500
mkdir imgs && mkdir annotations
# For annotations
cd annotations
wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip
wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download
unzip train_labels.zip && mv ctw1500_train_labels training
unzip test_labels.zip -d test
cd ..
# For images
cd imgs
wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip
wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip
unzip train_images.zip && mv train_images training
unzip test_images.zip && mv test_images test
```
```bash
mkdir ctw1500 && cd ctw1500
mkdir imgs && mkdir annotations
# For annotations
cd annotations
wget -O train_labels.zip https://universityofadelaide.box.com/shared/static/jikuazluzyj4lq6umzei7m2ppmt3afyw.zip
wget -O test_labels.zip https://cloudstor.aarnet.edu.au/plus/s/uoeFl0pCN9BOCN5/download
unzip train_labels.zip && mv ctw1500_train_labels training
unzip test_labels.zip -d test
cd ..
# For images
cd imgs
wget -O train_images.zip https://universityofadelaide.box.com/shared/static/py5uwlfyyytbb2pxzq9czvu6fuqbjdh8.zip
wget -O test_images.zip https://universityofadelaide.box.com/shared/static/t4w48ofnqkdw7jyc4t11nsukoeqk9c3d.zip
unzip train_images.zip && mv train_images training
unzip test_images.zip && mv test_images test
```
- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
```
```bash
python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1500 --split-list training test
```
- The resulting directory structure looks like the following:
```text
├── ctw1500
│   ├── imgs
│   ├── annotations
│   ├── instances_training.json
│   └── instances_val.json
```
## ICDAR 2011 (Born-Digital Images)
### 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
@ -166,13 +110,14 @@ python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1
- After running the above codes, the directory structure should be as follows:
```text
|── icdar2011
│── icdar2011
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
```
### ICDAR 2013 (Focused Scene Text)
## ICDAR 2013 (Focused Scene Text)
- Step1: Download `Challenge2_Training_Task12_Images.zip`, `Challenge2_Test_Task12_Images.zip`, `Challenge2_Training_Task1_GT.zip`, and `Challenge2_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads) `Task 2.1: Text Localization (2013 edition)`.
```bash
@ -204,111 +149,217 @@ python tools/data/textdet/ctw1500_converter.py /path/to/ctw1500 -o /path/to/ctw1
- After running the above codes, the directory structure should be as follows:
```text
|── icdar2013
│── icdar2013
│ ├── imgs
│ ├── instances_test.json
│ └── instances_training.json
```
### SynthText
## ICDAR 2015
- Download [data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb) and [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb) to `synthtext/instances_training.lmdb/`.
- Step0: Read [Important Note](#important-note)
- Step1: Download `ch4_training_images.zip`, `ch4_test_images.zip`, `ch4_training_localization_transcription_gt.zip`, `Challenge4_Test_Task1_GT.zip` from [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads)
- Step2:
```bash
mkdir icdar2015 && cd icdar2015
mkdir imgs && mkdir annotations
# For images,
mv ch4_training_images imgs/training
mv ch4_test_images imgs/test
# For annotations,
mv ch4_training_localization_transcription_gt annotations/training
mv Challenge4_Test_Task1_GT annotations/test
```
- Step3: Download [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) and [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) and move them to `icdar2015`
- Or, generate `instances_training.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/icdar_converter.py /path/to/icdar2015 -o /path/to/icdar2015 -d icdar2015 --split-list training test
```
- The resulting directory structure looks like the following:
```text
├── icdar2015
│   ├── imgs
│   ├── annotations
│   ├── 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
```
## SynthText
- Step1: Download SynthText.zip from [homepage](<https://www.robots.ox.ac.uk/~vgg/data/scenetext/> and extract its content to `synthtext/img`.
- Step2: Download [data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb) and [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb) to `synthtext/instances_training.lmdb/`.
- The resulting directory structure looks like the following:
```text
├── synthtext
│   ├── imgs
│   └── instances_training.lmdb
│   ├── data.mdb
│   └── lock.mdb
```
## TextOCR
### TextOCR
- Step1: Download [train_val_images.zip](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip), [TextOCR_0.1_train.json](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json) and [TextOCR_0.1_val.json](https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json) to `textocr/`.
```bash
mkdir textocr && cd textocr
# Download TextOCR dataset
wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json
```bash
mkdir textocr && cd textocr
# Download TextOCR dataset
wget https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_train.json
wget https://dl.fbaipublicfiles.com/textvqa/data/textocr/TextOCR_0.1_val.json
# For images
unzip -q train_val_images.zip
mv train_images train
```
# For images
unzip -q train_val_images.zip
mv train_images train
```
- Step2: Generate `instances_training.json` and `instances_val.json` with the following command:
```bash
python tools/data/textdet/textocr_converter.py /path/to/textocr
```
### Totaltext
```bash
python tools/data/textdet/textocr_converter.py /path/to/textocr
```
- The resulting directory structure looks like the following:
```text
├── textocr
│   ├── train
│   ├── instances_training.json
│   └── instances_val.json
```
## Totaltext
- Step0: Read [Important Note](#important-note)
- Step1: Download `totaltext.zip` from [github dataset](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Dataset) and `groundtruth_text.zip` from [github Groundtruth](https://github.com/cs-chan/Total-Text-Dataset/tree/master/Groundtruth/Text) (Our totaltext_converter.py supports groundtruth with both .mat and .txt format).
```bash
mkdir totaltext && cd totaltext
mkdir imgs && mkdir annotations
# For images
# in ./totaltext
unzip totaltext.zip
mv Images/Train imgs/training
mv Images/Test imgs/test
```bash
mkdir totaltext && cd totaltext
mkdir imgs && mkdir annotations
# For annotations
unzip groundtruth_text.zip
cd Groundtruth
mv Polygon/Train ../annotations/training
mv Polygon/Test ../annotations/test
# For images
# in ./totaltext
unzip totaltext.zip
mv Images/Train imgs/training
mv Images/Test imgs/test
# For annotations
unzip groundtruth_text.zip
cd Groundtruth
mv Polygon/Train ../annotations/training
mv Polygon/Test ../annotations/test
```
```
- Step2: Generate `instances_training.json` and `instances_test.json` with the following command:
```bash
python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
### CurvedSynText150k
```bash
python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
```
- The resulting directory structure looks like the following:
```text
├── totaltext
│   ├── imgs
│   ├── annotations
│   ├── instances_test.json
│   └── instances_training.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
```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
```
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/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4
```
```bash
python tools/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4
```
### FUNSD
- The resulting directory structure looks like the following:
```text
├── CurvedSynText150k
│   ├── syntext_word_eng
│   ├── emcs_imgs
│   └── instances_training.json
```
## FUNSD
- Step1: Download [dataset.zip](https://guillaumejaume.github.io/FUNSD/dataset.zip) to `funsd/`.
```bash
mkdir funsd && cd funsd
```bash
mkdir funsd && cd funsd
# Download FUNSD dataset
wget https://guillaumejaume.github.io/FUNSD/dataset.zip
unzip -q dataset.zip
# Download FUNSD dataset
wget https://guillaumejaume.github.io/FUNSD/dataset.zip
unzip -q dataset.zip
# For images
mv dataset/training_data/images imgs && mv dataset/testing_data/images/* imgs/
# For images
mv dataset/training_data/images imgs && mv dataset/testing_data/images/* imgs/
# For annotations
mkdir annotations
mv dataset/training_data/annotations annotations/training && mv dataset/testing_data/annotations annotations/test
# For annotations
mkdir annotations
mv dataset/training_data/annotations annotations/training && mv dataset/testing_data/annotations annotations/test
rm dataset.zip && rm -rf dataset
```
rm dataset.zip && rm -rf dataset
```
- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
```
```bash
python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
```
### DeText
- The resulting directory structure looks like the following:
```text
│── funsd
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── 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).
@ -338,14 +389,14 @@ python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
- After running the above codes, the directory structure should be as follows:
```text
|── detext
|   ├── annotations
│── detext
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
```
### NAF
## NAF
- Step1: Download [labeled_images.tar.gz](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) to `naf/`.
@ -375,14 +426,15 @@ python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
- After running the above codes, the directory structure should be as follows:
```text
|── naf
|   ├── annotations
│── naf
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   ├── instances_val.json
│   └── instances_training.json
```
### SROIE
## SROIE
- Step1: Download `0325updated.task1train(626p).zip`, `task1&2_test(361p).zip`, and `text.task1&2-test（361p).zip` from [homepage](https://rrc.cvc.uab.es/?ch=13&com=downloads) to `sroie/`
@ -421,29 +473,40 @@ python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
│   ├── instances_test.json
│   └── instances_training.json
```
### Lecture Video DB
## 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
```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
# 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
mv IIIT-CVid/Frames imgs
rm IIIT-CVid.zip
```
rm IIIT-CVid.zip
```
- Step2: Generate `instances_training.json`, `instances_val.json`, and `instances_test.json` with following command:
```bash
python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
```
```bash
python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
```
### IMGUR
- The resulting directory structure looks like the following:
```text
│── lv
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
│   └── instances_val.json
```
## 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.
@ -471,15 +534,15 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
- After running the above codes, the directory structure should be as follows:
```
|── imgur
| ├── annotations
│── imgur
│ ├── annotations
│ ├── imgs
│ ├── instances_test.json
│ ├── instances_training.json
│ └── instances_val.json
```
### KAIST
## KAIST
- Step1: Complete download [KAIST_all.zip](http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database) to `kaist/`.
@ -510,14 +573,14 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
- After running the above codes, the directory structure should be as follows:
```text
|── kaist
| ├── annotations
│── kaist
│ ├── annotations
│ ├── imgs
│ ├── instances_training.json
│ └── instances_val.json (optional)
```
### MTWI
## MTWI
- Step1: Download `mtwi_2018_train.zip` from [homepage](https://tianchi.aliyun.com/competition/entrance/231685/information?lang=en-us).
@ -541,14 +604,14 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
- After running the above codes, the directory structure should be as follows:
```text
|── mtwi
| ├── annotations
│── mtwi
│ ├── annotations
│ ├── imgs
│ ├── instances_training.json
│ └── instances_val.json (optional)
```
### COCO Text v2
## COCO Text v2
- Step1: Download image [train2014.zip](http://images.cocodataset.org/zips/train2014.zip) and annotation [cocotext.v2.zip](https://github.com/bgshih/cocotext/releases/download/dl/cocotext.v2.zip) to `coco_textv2/`.
@ -575,14 +638,14 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
- After running the above codes, the directory structure should be as follows:
```text
|── coco_textv2
| ├── annotations
│── coco_textv2
│ ├── annotations
│ ├── imgs
│ ├── instances_training.json
│ └── instances_val.json
```
### ReCTS
## 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).
@ -612,18 +675,19 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
- After running the above codes, the directory structure should be as follows:
```text
|── rects
| ├── annotations
│── rects
│ ├── annotations
│ ├── imgs
│ ├── instances_val.json (optional)
│ └── instances_training.json
```
### ILST
## 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
@ -650,22 +714,24 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── IIIT-ILST
|   ├── annotations
│── IIIT-ILST
│   ├── annotations
│   ├── imgs
│   ├── instances_val.json (optional)
│   └── instances_training.json
```
### VinText
## 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=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1UUQhNvzgpZy7zXBFQp0Qox-BBjunZ0ml" -O vintext.zip && rm -rf /tmp/cookies.txt
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(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
@ -679,22 +745,23 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
```
- Step2: Generate `instances_training.json`, `instances_test.json` and `instances_unseen_test.json`
```bash
python tools/data/textdet/vintext_converter.py PATH/TO/vintext --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── vintext
|   ├── annotations
│── vintext
│   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   ├── instances_unseen_test.json
│   └── instances_training.json
```
### BID
## BID
- Step1: Download [BID Dataset.zip](https://drive.google.com/file/d/1Oi88TRcpdjZmJ79WDLb9qFlBNG8q2De6/view)
@ -735,9 +802,10 @@ python tools/data/textdet/lv_converter.py PATH/TO/lv --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── BID
|   ├── annotations
│── BID
│   ├── annotations
│   ├── imgs
│   ├── instances_training.json
│   └── instances_val.json (optional)