# PAN.pytorch

**Repository Path**: soleso/PAN.pytorch

## Basic Information

- **Project Name**: PAN.pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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

![](imgs/paper/PAN.jpg)

## 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

[google drive](https://drive.google.com/drive/folders/1bKPQEEOJ5kgSSRMpnDB8HIRecnD_s4bR?usp=sharing)

## Data Preparation

train: prepare a text in the following format, use '\t' as a separator
```bash
/path/to/img.jpg path/to/label.txt
...
```
val:
use a folder
```bash
img/ store img
gt/ store gt file
```

## Train
1. config the `train_data_path`,`val_data_path`in [config.json](config.json)
2. use following script to run
```sh
python3 train.py
```

## Test

[eval.py](eval.py) is used to test model on test dataset

1. config `model_path`, `img_path`, `gt_path`, `save_path` in [eval.py](eval.py)
2. use following script to test
```sh
python3 eval.py
```

## Predict 
[predict.py](predict.py) is used to inference on single image

1. config `model_path`, `img_path`, in [predict.py](predict.py)
2. use following script to predict
```sh
python3 predict.py
```

The project is still under development.

<h2 id="Performance">Performance</h2>

### [ICDAR 2015](http://rrc.cvc.uab.es/?ch=4)
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
![](imgs/example/img_2.jpg)

![](imgs/example/img_10.jpg)

![](imgs/example/img_29.jpg)

![](imgs/example/img_75.jpg)

![](imgs/example/img_91.jpg)

### todo
- [ ] MobileNet backbone

- [x] ShuffleNet backbone
### reference
1. https://arxiv.org/pdf/1908.05900.pdf
2. https://github.com/WenmuZhou/PSENet.pytorch

**If this repository helps you，please star it. Thanks.**