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WenmuZhou / PAN.pytorch

A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Apache License 2.0
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Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Requirements

Download

PAN_resnet18_FPEM_FFM and PAN_resnet18_FPEM_FFM on icdar2015：

the updated model(resnet18:78.8,shufflenetv2: 72.4,lr:le-3) is not the best model

google drive

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 (ShuffleNetV2+FPEM_FFM+pse扩张) 736 1e-3 81.72 66.73 73.47 24.71 (P100)
my (resnet18+FPEM_FFM+pse扩张) 736 1e-3 84.93 74.09 79.14 21.31 (P100)
my (resnet50+FPEM_FFM+pse扩张) 736 1e-3 84.23 76.12 79.96 14.22 (P100)
my (ShuffleNetV2+FPEM_FFM+pse扩张) 736 1e-4 75.14 57.34 65.04 24.71 (P100)
my (resnet18+FPEM_FFM+pse扩张) 736 1e-4 83.89 69.23 75.86 21.31 (P100)
my (resnet50+FPEM_FFM+pse扩张) 736 1e-4 85.29 75.1 79.87 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

todo

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