DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

News | Main Results | Usage | Citation | Acknowledgement

This is the official repo for the papers:

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Text Spotting

News

2023.06.2 Update the pre-trained and fine-tuned Chinese scene text spotting model (78.3% 1-NED on ICDAR 2019 ReCTS).

2023.05.31 The extension paper (DeepSolo++) is submitted to ArXiv. The code and models will be released soon.

2023.02.28 DeepSolo is accepted by CVPR 2023. 🎉🎉

Relevant Project:

DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer | Code

Other applications of ViTAE inlcude: ViTPose | Remote Sensing | Matting | VSA | Video Object Segmentation

Main Results

Total-Text

Backbone	External Data	Det-P	Det-R	Det-F1	E2E-None	E2E-Full	Weights
Res-50	Synth150K	93.9	82.1	87.6	78.8	86.2	OneDrive
Res-50	Synth150K+MLT17+IC13+IC15	93.1	82.1	87.3	79.7	87.0	OneDrive
Res-50	Synth150K+MLT17+IC13+IC15+TextOCR	93.2	84.6	88.7	$\underline{\text{82.5}}$	$\underline{\text{88.7}}$	OneDrive
Res-101	Synth150K+MLT17+IC13+IC15	93.2	83.5	88.1	80.1	87.1	OneDrive
Swin-T	Synth150K+MLT17+IC13+IC15	92.8	83.5	87.9	79.7	87.1	OneDrive
Swin-S	Synth150K+MLT17+IC13 +C15	93.7	84.2	88.7	81.3	87.8	OneDrive
ViTAEv2-S	Synth150K+MLT17+IC13+IC15	92.6	85.5	$\underline{\text{88.9}}$	81.8	88.4	OneDrive
ViTAEv2-S	Synth150K+MLT17+IC13+IC15+TextOCR	92.9	87.4	90.0	83.6	89.6	OneDrive

ICDAR 2015 (IC15)

Backbone	External Data	Det-P	Det-R	Det-F1	E2E-S	E2E-W	E2E-G	Weights
Res-50	Synth150K+Total-Text+MLT17+IC13	92.8	87.4	90.0	86.8	81.9	76.9	OneDrive
Res-50	Synth150K+Total-Text+MLT17+IC13+TextOCR	92.5	87.2	89.8	$\underline{\text{88.0}}$	$\underline{\text{83.5}}$	$\underline{\text{79.1}}$	OneDrive
ViTAEv2-S	Synth150K+Total-Text+MLT17+IC13	93.7	87.3	90.4	87.5	82.8	77.7	OneDrive
ViTAEv2-S	Synth150K+Total-Text+MLT17+IC13+TextOCR	92.4	87.9	$\underline{\text{90.1}}$	88.1	83.9	79.5	OneDrive

CTW1500

Backbone	External Data	Det-P	Det-R	Det-F1	E2E-None	E2E-Full	Weights
Res-50	Synth150K+Total-Text+MLT17+IC13+IC15	93.2	85.0	88.9	64.2	81.4	OneDrive

ICDAR 2019 ReCTS

Backbone	External Data	Det-P	Det-R	Det-H	1-NED	Weights
Res-50	SynChinese130K+ArT+LSVT	92.6	89.0	90.7	78.3	OneDrive
ViTAEv2-S	SynChinese130K+ArT+LSVT	92.6	89.9	91.2	79.6	OneDrive

Pre-trained Models for Total-Text & ICDAR 2015

Backbone	Training Data	Weights
Res-50	Synth150K+Total-Text	OneDrive
Res-50	Synth150K+Total-Text+MLT17+IC13+IC15	OneDrive
Res-50	Synth150K+Total-Text+MLT17+IC13+IC15+TextOCR	OneDrive
Res-101	Synth150K+Total-Text+MLT17+IC13+IC15	OneDrive
Swin-T	Synth150K+Total-Text+MLT17+IC13+IC15	OneDrive
Swin-S	Synth150K+Total-Text+MLT17+IC13+IC15	OneDrive
ViTAEv2-S	Synth150K+Total-Text+MLT17+IC13+IC15	OneDrive
ViTAEv2-S	Synth150K+Total-Text+MLT17+IC13+IC15+TextOCR	OneDrive

Pre-trained Model for CTW1500

Backbone	Training Data	Weights
Res-50	Synth150K+Total-Text+MLT17+IC13+IC15	OneDrive

Pre-trained Model for ReCTS

Backbone	Training Data	Weights
Res-50	SynChinese130K+ArT+LSVT+ReCTS	OneDrive
ViTAEv2-S	SynChinese130K+ArT+LSVT+ReCTS	OneDrive

Usage

Installation

Python 3.8 + PyTorch 1.9.0 + CUDA 11.1 + Detectron2 (v0.6)

git clone https://github.com/ViTAE-Transformer/DeepSolo.git
cd DeepSolo
conda create -n deepsolo python=3.8 -y
conda activate deepsolo
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.9/index.html
python setup.py build develop

Preparation

Datasets

[SynthText150K (CurvedSynText150K)] images | annotations(Part1) | annotations(Part2)

[MLT] images | annotations

[ICDAR2013] images | annotations

[ICDAR2015] images | annotations

[Total-Text] images | annotations

[CTW1500] images | annotations

[TextOCR] images | annotations

[Inverse-Text] images | annotations

[SynChinese130K] images | annotations

[ArT] images | annotations

[LSVT] images | annotations

[ReCTS] images | annotations

[Evaluation ground-truth] Link

Some image files need to be renamed. Organize them as follows (lexicon files are not listed here):

|- ./datasets
   |- syntext1
   |  |- train_images
   |  └  annotations
   |       |- train_37voc.json
   |       └  train_96voc.json
   |- syntext2
   |  |- train_images
   |  └  annotations
   |       |- train_37voc.json
   |       └  train_96voc.json
   |- mlt2017
   |  |- train_images
   |  |- train_37voc.json
   |  └  train_96voc.json
   |- totaltext
   |  |- train_images
   |  |- test_images
   |  |- train_37voc.json
   |  |- train_96voc.json
   |  └  test.json
   |- ic13
   |  |- train_images
   |  |- train_37voc.json
   |  └  train_96voc.json
   |- ic15
   |  |- train_images
   |  |- test_images
   |  |- train_37voc.json
   |  |- train_96voc.json
   |  └  test.json
   |- ctw1500
   |  |- train_images
   |  |- test_images
   |  |- train_96voc.json
   |  └  test.json
   |- textocr
   |  |- train_images
   |  |- train_37voc_1.json
   |  └  train_37voc_2.json
   |- inversetext
   |  |- test_images
   |  └  test.json
   |- chnsyntext
   |  |- syn_130k_images
   |  └  chn_syntext.json
   |- ArT
   |  |- rename_artimg_train
   |  └  art_train.json
   |- LSVT
   |  |- rename_lsvtimg_train
   |  └  lsvt_train.json
   |- ReCTS
   |  |- ReCTS_train_images  # 18,000 images
   |  |- ReCTS_val_images  # 2,000 images
   |  |- ReCTS_test_images  # 5,000 images
   |  |- rects_train.json
   |  |- rects_val.json
   |  └  rects_test.json
   |- evaluation
   |  |- gt_*.zip

ImageNet Pre-trained Backbone

If you want to pre-train DeepSolo with ResNet-101, ViTAEv2-S or SwinTransformer , you can download the converted backbone weights and put them under pretrained_backbone for initialization: Swin-T | ViTAEv2-S | Res101 | Swin-S. You can also refer to the python files in pretrained_backbone and convert the backbones by yourself.

If you want to use the model trained on Chinese data, please download the font (simsun.ttc) and Chinese character list (chn_cls_list, a binary file) first.

wget https://drive.google.com/file/d/1dcR__ZgV_JOfpp8Vde4FR3bSR-QnrHVo/view?usp=sharing -O simsun.ttc
wget https://drive.google.com/file/d/1wqkX2VAy48yte19q1Yn5IVjdMVpLzYVo/view?usp=sharing -O chn_cls_list

Training

Total-Text & ICDAR2015

1. Pre-train

For example, pre-train DeepSolo with Synth150K+Total-Text+MLT17+IC13+IC15:

python tools/train_net.py --config-file configs/R_50/pretrain/150k_tt_mlt_13_15.yaml --num-gpus 4

2. Fine-tune

Fine-tune on Total-Text or ICDAR2015:

python tools/train_net.py --config-file configs/R_50/TotalText/finetune_150k_tt_mlt_13_15.yaml --num-gpus 4
python tools/train_net.py --config-file configs/R_50/IC15/finetune_150k_tt_mlt_13_15.yaml --num-gpus 4

CTW1500

1. Pre-train

python tools/train_net.py --config-file configs/R_50/CTW1500/pretrain_96voc_50maxlen.yaml --num-gpus 4

2. Fine-tune

python tools/train_net.py --config-file configs/R_50/CTW1500/finetune_96voc_50maxlen.yaml --num-gpus 4

ReCTS

1. Pre-train

python tools/train_net.py --config-file configs/R_50/ReCTS/pretrain.yaml --num-gpus 8

2. Fine-tune

python tools/train_net.py --config-file configs/R_50/ReCTS/finetune.yaml --num-gpus 8

Evaluation

python tools/train_net.py --config-file ${CONFIG_FILE} --eval-only MODEL.WEIGHTS ${MODEL_PATH}

Note: To conduct evaluation on ICDAR 2019 ReCTS, you can directly submit the saved file output/R50/rects/finetune/inference/rects_submit.txt to the official website for evaluation.

Visualization Demo

python demo/demo.py --config-file ${CONFIG_FILE} --input ${IMAGES_FOLDER_OR_ONE_IMAGE_PATH} --output ${OUTPUT_PATH} --opts MODEL.WEIGHTS <MODEL_PATH>

Citation

If you find DeepSolo helpful, please consider giving this repo a star:star: and citing:

@inproceedings{ye2023deepsolo,
  title={DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting},
  author={Ye, Maoyuan and Zhang, Jing and Zhao, Shanshan and Liu, Juhua and Liu, Tongliang and Du, Bo and Tao, Dacheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19348--19357},
  year={2023}
}

@article{ye2023deepsolo++,
  title={DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Text Spotting},
  author={Ye, Maoyuan and Zhang, Jing and Zhao, Shanshan and Liu, Juhua and Liu, Tongliang and Du, Bo and Tao, Dacheng},
  booktitle={arxiv preprint arXiv:2305.19957},
  year={2023}
}

Acknowledgement

This project is based on Adelaidet. For academic use, this project is licensed under the 2-clause BSD License.

xiaoming-master/DeepSolo-source

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

News

Main Results

Usage

Installation

Preparation

Training

Evaluation

Visualization Demo

Citation

Acknowledgement