/PSENet.pytorch

A pytorch re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network

Primary LanguageC++GNU General Public License v3.0GPL-3.0

Shape Robust Text Detection with Progressive Scale Expansion Network

Requirements

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

Update

20190401

  1. add author loss, the results are compared in Performance

Download

resnet50 and resnet152 model on icdar 2015:

  1. bauduyun extract code: rxjf

  2. google drive

Data Preparation

follow icdar15 dataset format

img
│   1.jpg
│   2.jpg   
│		...
gt
│   gt_1.txt
│   gt_2.txt
|		...

Train

  1. config the trainroot,testrootin config.py
  2. use following script to run
python3 train.py

Test

eval.py is used to test model on test dataset

  1. config model_path, data_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, gt_path, save_path in predict.py
  2. use following script to predict
python3 predict.py

Performance

only train on ICDAR2015 dataset with single NVIDIA 1080Ti

my implementation with my loss use adam and warm_up

Method Precision (%) Recall (%) F-measure (%) FPS(1080Ti)
PSENet-1s with resnet50 batch 8 81.13 77.03 79.03 1.76
PSENet-2s with resnet50 batch 8 81.36 77.13 79.18 3.55
PSENet-4s with resnet50 batch 8 81.00 76.55 78.71 4.43
PSENet-1s with resnet152 batch 4 85.45 80.06 82.67 1.48
PSENet-2s with resnet152 batch 4 85.42 80.11 82.68 2.56
PSENet-4s with resnet152 batch 4 83.93 79.00 81.39 2.99

my implementation with my loss use adam and MultiStepLR

Method Precision (%) Recall (%) F-measure (%) FPS(1080Ti)
PSENet-1s with resnet50 batch 8 83.39 79.29 81.29 1.76
PSENet-2s with resnet50 batch 8 83.22 79.05 81.08 3.55
PSENet-4s with resnet50 batch 8 82.57 78.23 80.34 4.43
PSENet-1s with resnet152 batch 4 85.33 79.87 82.51 1.48
PSENet-2s with resnet152 batch 4 85.36 79.73 82.45 2.56
PSENet-4s with resnet152 batch 4 83.95 78.86 81.33 2.99

my implementation with author loss use adam and warm_up

Method Precision (%) Recall (%) F-measure (%) FPS(1080Ti)
PSENet-1s with resnet50 batch 8 83.33 77.75 80.44 1.76
PSENet-2s with resnet50 batch 8 83.01 77.66 80.24 3.55
PSENet-4s with resnet50 batch 8 82.38 76.98 79.59 4.43
PSENet-1s with resnet152 batch 4 85.16 79.87 82.43 1.48
PSENet-2s with resnet152 batch 4 85.03 79.63 82.24 2.56
PSENet-4s with resnet152 batch 4 84.53S 79.20 81.77 2.99

my implementation with author loss use adam and MultiStepLR

Method Precision (%) Recall (%) F-measure (%) FPS(1080Ti)
PSENet-1s with resnet50 batch 8 83.93 79.48 81.65 1.76
PSENet-2s with resnet50 batch 8 84.17 79.63 81.84 3.55
PSENet-4s with resnet50 batch 8 83.50 78.71 81.04 4.43
PSENet-1s with resnet152 batch 4 85.16 79.58 82.28 1.48
PSENet-2s with resnet152 batch 4 85.13 79.15 82.03 2.56
PSENet-4s with resnet152 batch 4 84.40 78.71 81.46 2.99

official implementation use SGD and StepLR

Method Precision (%) Recall (%) F-measure (%) FPS(1080Ti)
PSENet-1s with resnet50 batch 8 84.15 80.26 82.16 1.76
PSENet-2s with resnet50 batch 8 83.61 79.82 81.67 3.72
PSENet-4s with resnet50 batch 8 81.90 78.23 80.03 4.51
PSENet-1s with resnet152 batch 4 82.87 78.76 80.77 1.53
PSENet-2s with resnet152 batch 4 82.33 78.33 80.28 2.61
PSENet-4s with resnet152 batch 4 81.19 77.13 79.11 3.00

examples

reference

  1. https://github.com/liuheng92/tensorflow_PSENet
  2. https://github.com/whai362/PSENet