Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Requirements

• pytorch 1.1+
• torchvision 0.3+
• pyclipper
• opencv3
• gcc 4.9+

Data Preparation

train: prepare a text in the following format, use '\t' as a separator

/path/to/img.jpg path/to/label.txt
...

val: use a folder

img/ store img
gt/ store gt file

Train

1. config the train_data_path,val_data_pathin config.json
2. use following script to run

python3 train.py

Test

eval.py is used to test model on test dataset

1. config model_path, img_path, gt_path, save_path in eval.py
2. use following script to test

python3 eval.py

Predict

predict.py is used to inference on single image

1. config model_path, img_path, in predict.py
2. use following script to predict

python3 predict.py

The project is still under development.

Performance

ICDAR 2015

only train on ICDAR2015 dataset

Method	image size (short size)	learning rate	Precision (%)	Recall (%)	F-measure (%)	FPS
paper(resnet18)	736	x	x	x	80.4	26.1
my (resnet18+FPEM_FFM+pse扩张)	736	1e-3	84.24	74.14	78.87	21.31 (P100)
my (resnet50+FPEM_FFM+pse扩张)	736	1e-3	69.04	66.66	67.83	14.22 (P100)
my (resnet18+FPEM_FFM+pse扩张)	736	1e-4	62.93	62.41	62.61	21.31 (P100)
my (resnet50+FPEM_FFM+pse扩张)	736	1e-4	61.19	69.18	64.94	14.22 (P100)
my (resnet18+FPN+pse扩张)	736	1e-3	76.50	74.70	75.59	14.47 (P100)
my (resnet50+FPN+pse扩张)	736	1e-3	71.82	75.73	73.72	10.67 (P100)
my (resnet18+FPN+pse扩张)	736	1e-4	74.19	72.34	73.25	14.47 (P100)
my (resnet50+FPN+pse扩张)	736	1e-4	78.96	76.27	77.59	10.67 (P100)

examples

TBD

reference

1. https://arxiv.org/pdf/1908.05900.pdf
2. https://github.com/WenmuZhou/PSENet.pytorch

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