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https://github.com/wenmuzhou/psenet.pytorch

A pytorch re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network
https://github.com/wenmuzhou/psenet.pytorch

ocr python3 pytorch text-detection

Last synced: 6 days ago
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A pytorch re-implementation of PSENet: Shape Robust Text Detection with Progressive Scale Expansion Network

Host: GitHub
URL: https://github.com/wenmuzhou/psenet.pytorch
Owner: WenmuZhou
License: gpl-3.0
Created: 2019-02-20T03:03:06.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2021-03-04T09:03:07.000Z (over 3 years ago)
Last Synced: 2023-11-07T19:05:31.490Z (about 1 year ago)
Topics: ocr, python3, pytorch, text-detection
Language: C++
Size: 7.93 MB
Stars: 460
Watchers: 14
Forks: 137
Open Issues: 26
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # 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](#Performance)

### Download

resnet50 and resnet152 model on icdar 2015: 

1. ~~[bauduyun](https://pan.baidu.com/s/1rN0oGBRsdUYmcQUayMZUOA) extract code: rxjf~~

2. ~~[google drive](https://drive.google.com/drive/folders/1r3Q1GJ5990WYrwXKT29aHvfQNW92QkXv?usp=sharing)~~

## Data Preparation

follow icdar15 dataset format

```

img

│   1.jpg

│   2.jpg   

│		...

gt

│   gt_1.txt

│   gt_2.txt

|		...

```

## Train

1. config the `trainroot`,`testroot`in [config.py](config.py)

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`, `data_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`, `gt_path`, `save_path` in [predict.py](predict.py)

2. use following script to predict

```sh

python3 predict.py

```

Performance


### [ICDAR 2015](http://rrc.cvc.uab.es/?ch=4)

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

![](imgs/img_31.jpg)

![](imgs/img_73.jpg)

![](imgs/img_83.jpg)

![](imgs/img_98.jpg)

![](imgs/img_125.jpg)

### reference

1. https://github.com/liuheng92/tensorflow_PSENet

2. https://github.com/whai362/PSENet