diff --git a/docs/en/datasets/det.md b/docs/en/datasets/det.md index 1ced15f9..ba15d205 100644 --- a/docs/en/datasets/det.md +++ b/docs/en/datasets/det.md @@ -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]( 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)