Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Requirements
- pytorch 1.1+
- torchvision 0.3+
- pyclipper
- opencv3
- gcc 4.9+
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
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
- config the
train_data_path,val_data_pathin config.json - use following script to run
python3 train.py
Test
eval.py is used to test model on test dataset
- config
model_path,img_path,gt_path,save_pathin eval.py - use following script to test
python3 eval.py
Predict
predict.py is used to inference on single image
- config
model_path,img_path, in predict.py - 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
- MobileNet backbone
- ShuffleNet backbone
reference
If this repository helps you，please star it. Thanks.
GitHub - WenmuZhou/PAN.pytorch: A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
A unofficial pytorch implementation of PAN(PSENet2): Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network - GitHub - WenmuZhou/PAN.pytorch: A unofficial pytorch imp...
WenmuZhou
