Super detailed record training and testing DBnet (pytorch) in pycharm

Paper link: https://arxiv.org/pdf/1911.08947.pdf
Project link: https://github.com/WenmuZhou/DBNet.pytorch

Project Introduction

DBnet is a text detection method based on segmentation, which can better detect text of different shapes in natural scenes

1. network structure
   

2. Import: After downloading the code, unzip it and import it into pycharm
   

3. Modify part of the code:
   Due to the author's input error, some modifications need to be made in the code
   3.1 Change '--save_resut' to '--save_result'
   3.2
   There will also be No module named 'torchvision.models.utils' error during training, so only You need to change from torchvision.models.utils import load_state_dict_from_url to from torch.hub import load_state_dict_from_url
   

4. Environment installation
   Open the README and find the following code:
   pip install -r requirement.txt
   and enter it in the terminal and press Enter. However
   
   
   , it is faster for me to enter the command one by one to install. Open
   requirement.txt and enter the corresponding command in the terminal. :
   For example: pip install addict -i https://mirrors.aliyun.com/pypi/simple/
   
   input and press Enter to install
   

5. Dataset: The article uses the icdar2015 dataset
   Link: https://rrc.cvc.uab.es/?ch=4&com=tasks
   Select Task 4.1: Text Localization and download it.
   However, you need to register with your email address
   and unzip it to corresponding folder, and put it into the project as follows:
   

6. Data processing: The author is processing it under the Ubuntu system, and I am using the windows system, so enter the following data code

import os
def get_images(img_path):
    '''
    find image files in data path
    :return: list of files found
    '''
    files = []
    exts = ['jpg', 'png', 'jpeg', 'JPG', 'PNG']
    for parent, dirnames, filenames in os.walk(img_path):
        for filename in filenames:
            for ext in exts:
                if filename.endswith(ext):
                    files.append(os.path.join(parent, filename))
                    break
    print('Find {} images'.format(len(files)))
    return sorted(files)

def get_txts(txt_path):
    '''
    find gt files in data path
    :return: list of files found
    '''
    files = []
    exts = ['txt']
    for parent, dirnames, filenames in os.walk(txt_path):
        for filename in filenames:
            for ext in exts:
                if filename.endswith(ext):
                    files.append(os.path.join(parent, filename))
                    break
    print('Find {} txts'.format(len(files)))
    return sorted(files)

if __name__ == '__main__':
    import json

    img_train_path = r'F:\apy\DBNet\datasets\train\img\ch4_training_images'
    img_test_path = r'F:\apy\DBNet\datasets\test\img\ch4_test_images'
    train_files = get_images(img_train_path)
    test_files = get_images(img_test_path)

    txt_train_path = r'F:\apy\DBNet\datasets\train\gt\ch4_training_localization_transcription_gt'
    txt_test_path = r'F:\apy\DBNet\datasets\test\gt\Challenge4_Test_Task1_GT'
    train_txts = get_txts(txt_train_path)
    test_txts = get_txts(txt_test_path)
    n_train = len(train_files)
    n_test = len(test_files)
    assert len(train_files) == len(train_txts) and len(test_files) == len(test_txts)
    # with open('train.txt', 'w') as f:
    with open('./datasets/train.txt', 'w') as f:
        for i in range(n_train):
            line = train_files[i] + '\t' + train_txts[i] + '\n'
            f.write(line)
    with open('./datasets/test.txt', 'w') as f:
        for i in range(n_test):
            line = test_files[i] + '\t' + test_txts[i] + '\n'
            f.write(line)

And modify the corresponding file path in the code

10. Training:
    First configure the corresponding parameters in config/icdar2015_resnet18_FPN_DBhead_polyLR.yaml
    
    Input in terminal:
    python tools/train.py --config_file "config/icdar2015_resnet18_FPN_DBhead_polyLR.yaml"
    About three days of training
    

11. Test: Modify the following parameters in tools/predict.py and run it.
    
    The result is as follows:
    
    
    The above is the complete training and testing process! hope its good for U.S!

Reference link:
[1]: https://bbs.huaweicloud.com/blogs/345205
[2]: https://github.com/WenmuZhou/DBNet.pytorch
[3]: https://rrc.cvc.uab .es/?ch=4&com=tasks