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ABCNet (CVPR'20 oral)

ABCNet is an efficient end-to-end scene text spotting framework over 10x faster than previous SoTA.

Models

CTW1500 results with ABCNet.

Name inf. time e2e-hmean det-hmean download
paper reported 45.2
attn_R_50 2080ti 8.7 FPS 53.2 84.4 model

Total Text results with ABCNet.

Name inf. time e2e-hmean det-hmean download
paper reported V100 17.9 FPS 64.2
tt_attn_R_50 2080ti 11.3 FPS 67.1 86.0 model
pretrain_attn_R_50 2080ti 11.3 FPS 58.1 80.0 model

Quick Start

Inference with our trained Models

  1. Select the model and config file above, for example, configs/BAText/CTW1500/attn_R_50.yaml.
  2. Run the demo with
wget -O ctw1500_attn_R_50.pth https://universityofadelaide.box.com/shared/static/okeo5pvul5v5rxqh4yg8pcf805tzj2no.pth
python demo/demo.py \
    --config-file configs/BAText/CTW1500/attn_R_50.yaml \
    --input datasets/CTW1500/ctwtest_text_image/ \
    --opts MODEL.WEIGHTS ctw1500_attn_R_50.pth

or

wget -O tt_attn_R_50.pth https://cloudstor.aarnet.edu.au/plus/s/tYsnegjTs13MwwK/download
python demo/demo.py \
    --config-file configs/BAText/TotalText/attn_R_50.yaml \
    --input datasets/totaltext/test_images/ \
    --opts MODEL.WEIGHTS tt_attn_R_50.pth

Train Your Own Models

To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md.

You can also prepare your custom dataset following the example scripts.

Pretrainining with synthetic data:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/BAText/Pretrain/attn_R_50.yaml \
    --num-gpus 4 \
    OUTPUT_DIR text_pretraining/attn_R_50

Finetuning on Total Text:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/BAText/TotalText/attn_R_50.yaml \
    --num-gpus 4 \
    MODEL.WEIGHTS text_pretraining/attn_R_50/model_final.pth

Finetuning on CTW1500:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/BAText/CTW1500/attn_R_50.yaml \
    --num-gpus 4 \
    MODEL.WEIGHTS text_pretraining/attn_R_50/model_final.pth

Evaluate on Trained Model

Download test GT here so that the directory has the following structure:

datasets
|_ evaluation
|  |_ gt_totaltext.zip
|  |_ gt_ctw1500.zip

Producing both e2e and detection results on CTW1500:

wget -O ctw1500_attn_R_50.pth https://universityofadelaide.box.com/shared/static/okeo5pvul5v5rxqh4yg8pcf805tzj2no.pth
python tools/train_net.py \
    --config-file configs/BAText/CTW1500/attn_R_50.yaml \
    --eval-only \
    MODEL.WEIGHTS ctw1500_attn_R_50.pth

or Totaltext:

wget -O tt_attn_R_50.pth https://cloudstor.aarnet.edu.au/plus/s/tYsnegjTs13MwwK/download
python tools/train_net.py \
    --config-file configs/BAText/TotalText/attn_R_50.yaml \
    --eval-only \
    MODEL.WEIGHTS tt_attn_R_50.pth

You can also evalute the json result file offline following the evaluation_example_scripts, including an example of how to evaluate on a custom dataset. If you want to measure the inference time, please change --num-gpus to 1.

Cite

@inproceedings{liu2020abcnet,
  title     =  {{ABCNet}: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network},
  author    =  {Liu, Yuliang and Chen, Hao and Shen, Chunhua and He, Tong and Jin, Lianwen and Wang, Liangwei},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =  {2020}
}