https://github.com/HotaekHan/SSTDNet

Implement 'Single Shot Text Detector with Regional Attention, ICCV 2017 Spotlight'
https://github.com/HotaekHan/SSTDNet

Last synced: 10 days ago
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Implement 'Single Shot Text Detector with Regional Attention, ICCV 2017 Spotlight'

• Host: GitHub
• URL: https://github.com/HotaekHan/SSTDNet
• Owner: HotaekHan
• Created: 2018-01-19T07:18:03.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-28T08:17:40.000Z (over 6 years ago)
• Last Synced: 2024-08-02T11:14:56.917Z (3 months ago)
• Language: Python
• Size: 36.1 KB
• Stars: 83
• Watchers: 9
• Forks: 17
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# SSTDNet

Implement 'Single Shot Text Detector with Regional Attention, ICCV 2017 Spotlight' using pytorch.

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### This code is work for general object detection problem. not for (oriented) text detection problem. I will probably update to handle oriented bounding box as soon as possible :)

[How to use]

1. you need dataset.

  * dataset structure is..


    > /train/0.jpg, /train/0.txt, /valid/0.jpg, /valid/0.txt, ....

  * 0.txt contain position and label of objects like below


    
(xmin, ymin, xmax, ymax, label)

    
1273.0 935.0 1407.0 1017.0 v1

    
911.0 893.0 979.0 953.0 v1

    
984.0 889.0 1053.0 948.0 v1

  * To encode label name to integer number, you should define labels in the 'class_lable_map.xlsx"

    
v1 1

    
v2 2

    
....

    * start from 1. not from 0. 0 will be background (in the loss.py).


2. need some settings for dataset reader.

   
- see train.py. you can find some code for reading dataset

   


     'trainset = ListDataset(root="../train", gt_extension=".txt", labelmap_path="class_label_map.xlsx", is_train=True, transform=transform, input_image_size=512, num_crops=n_crops, original_img_size=2048)'

     


   - you should set the 'input_image_size' and 'original_img_size'. 'input_image_size' is size of (cropped) image for train. And 'original_img_size' is size of (original) image. I made this parameter to handle high resolution image. if you don't need crop function, -1 for num_crops.

3. Train with your dataset!

        
you should define some parameter like learning rate, which optimizer to use, size of batch etc.