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config_en.md

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Configuration

1. Optional Parameter List

The following list can be viewed through --help

FLAG Supported script Use Defaults Note
-c ALL Specify configuration file to use None Please refer to the parameter introduction for configuration file usage
-o ALL set configuration options None Configuration using -o has higher priority than the configuration file selected with -c. E.g: -o Global.use_gpu=false

2. Introduction to Global Parameters of Configuration File

Take rec_chinese_lite_train_v2.0.yml as an example

Global

Parameter Use Defaults Note
use_gpu Set using GPU or not true
epoch_num Maximum training epoch number 500
log_smooth_window Log queue length, the median value in the queue each time will be printed 20
print_batch_step Set print log interval 10
save_model_dir Set model save path output/{算法名称}
save_epoch_step Set model save interval 3
eval_batch_step Set the model evaluation interval 2000 or [1000, 2000] running evaluation every 2000 iters or evaluation is run every 2000 iterations after the 1000th iteration
cal_metric_during_train Set whether to evaluate the metric during the training process. At this time, the metric of the model under the current batch is evaluated true
load_static_weights Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) true
pretrained_model Set the path of the pre-trained model ./pretrain_models/CRNN/best_accuracy
checkpoints set model parameter path None Used to load parameters after interruption to continue training
use_visualdl Set whether to enable visualdl for visual log display False Tutorial
use_wandb Set whether to enable W&B for visual log display False Documentation
infer_img Set inference image path or folder path ./infer_img |
character_dict_path Set dictionary path ./ppocr/utils/ppocr_keys_v1.txt If the character_dict_path is None, model can only recognize number and lower letters
max_text_length Set the maximum length of text 25
use_space_char Set whether to recognize spaces True |
label_list Set the angle supported by the direction classifier ['0','180'] Only valid in angle classifier model
save_res_path Set the save address of the test model results ./output/det_db/predicts_db.txt Only valid in the text detection model

Optimizer (ppocr/optimizer)

Parameter Use Defaults Note
name Optimizer class name Adam Currently supportsMomentum,Adam,RMSProp, see ppocr/optimizer/optimizer.py
beta1 Set the exponential decay rate for the 1st moment estimates 0.9
beta2 Set the exponential decay rate for the 2nd moment estimates 0.999
clip_norm The maximum norm value -
lr Set the learning rate decay method -
name Learning rate decay class name Cosine Currently supportsLinear,Cosine,Step,Piecewise, seeppocr/optimizer/learning_rate.py
learning_rate Set the base learning rate 0.001
regularizer Set network regularization method -
name Regularizer class name L2 Currently supportL1,L2, seeppocr/optimizer/regularizer.py
factor Regularizer coefficient 0.00001

Architecture (ppocr/modeling)

In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck and Head

Parameter Use Defaults Note
model_type Network Type rec Currently supportrec,det,cls
algorithm Model name CRNN See algorithm_overview for the support list
Transform Set the transformation method - Currently only recognition algorithms are supported, see ppocr/modeling/transform for details
name Transformation class name TPS Currently supports TPS
num_fiducial Number of TPS control points 20 Ten on the top and bottom
loc_lr Localization network learning rate 0.1
model_name Localization network size small Currently supportsmall,large
Backbone Set the network backbone class name - see ppocr/modeling/backbones
name backbone class name ResNet Currently supportMobileNetV3,ResNet
layers resnet layers 34 Currently support18,34,50,101,152,200
model_name MobileNetV3 network size small Currently supportsmall,large
Neck Set network neck - seeppocr/modeling/necks
name neck class name SequenceEncoder Currently supportSequenceEncoder,DBFPN
encoder_type SequenceEncoder encoder type rnn Currently supportreshape,fc,rnn
hidden_size rnn number of internal units 48
out_channels Number of DBFPN output channels 256
Head Set the network head - seeppocr/modeling/heads
name head class name CTCHead Currently supportCTCHead,DBHead,ClsHead
fc_decay CTCHead regularization coefficient 0.0004
k DBHead binarization coefficient 50
class_dim ClsHead output category number 2

Loss (ppocr/losses)

Parameter Use Defaults Note
name loss class name CTCLoss Currently supportCTCLoss,DBLoss,ClsLoss
balance_loss Whether to balance the number of positive and negative samples in DBLossloss (using OHEM) True
ohem_ratio The negative and positive sample ratio of OHEM in DBLossloss 3
main_loss_type The loss used by shrink_map in DBLossloss DiceLoss Currently supportDiceLoss,BCELoss
alpha The coefficient of shrink_map_loss in DBLossloss 5
beta The coefficient of threshold_map_loss in DBLossloss 10

PostProcess (ppocr/postprocess)

Parameter Use Defaults Note
name Post-processing class name CTCLabelDecode Currently supportCTCLoss,AttnLabelDecode,DBPostProcess,ClsPostProcess
thresh The threshold for binarization of the segmentation map in DBPostProcess 0.3
box_thresh The threshold for filtering output boxes in DBPostProcess. Boxes below this threshold will not be output 0.7
max_candidates The maximum number of text boxes output in DBPostProcess 1000
unclip_ratio The unclip ratio of the text box in DBPostProcess 2.0

Metric (ppocr/metrics)

Parameter Use Defaults Note
name Metric method name CTCLabelDecode Currently supportDetMetric,RecMetric,ClsMetric
main_indicator Main indicators, used to select the best model acc For the detection method is hmean, the recognition and classification method is acc

Dataset (ppocr/data)

Parameter Use Defaults Note
dataset Return one sample per iteration - -
name dataset class name SimpleDataSet Currently supportSimpleDataSet,LMDBDataSet
data_dir Image folder path ./train_data
label_file_list Groundtruth file path ["./train_data/train_list.txt"] This parameter is not required when dataset is LMDBDataSet
ratio_list Ratio of data set [1.0] If there are two train_lists in label_file_list and ratio_list is [0.4,0.6], 40% will be sampled from train_list1, and 60% will be sampled from train_list2 to combine the entire dataset
transforms List of methods to transform images and labels [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] seeppocr/data/imaug
loader dataloader related -
shuffle Does each epoch disrupt the order of the data set True
batch_size_per_card Single card batch size during training 256
drop_last Whether to discard the last incomplete mini-batch because the number of samples in the data set cannot be divisible by batch_size True
num_workers The number of sub-processes used to load data, if it is 0, the sub-process is not started, and the data is loaded in the main process 8

Weights & Biases (W&B)

Parameter Use Defaults Note
project Project to which the run is to be logged uncategorized
name Alias/Name of the run Randomly generated by wandb
id ID of the run Randomly generated by wandb
entity User or team to which the run is being logged The logged in user
save_dir local directory in which all the models and other data is saved wandb
config model configuration None

3. Multilingual Config File Generation

PaddleOCR currently supports recognition for 80 languages (besides Chinese). A multi-language configuration file template is provided under the path configs/rec/multi_languages: rec_multi_language_lite_train.yml。

There are two ways to create the required configuration file:

  1. Automatically generated by script

Script generate_multi_language_configs.py can help you generate configuration files for multi-language models.

  • Take Italian as an example, if your data is prepared in the following format:

    |-train_data
    |- it_train.txt # train_set label
    |- it_val.txt # val_set label
    |- data
        |- word_001.jpg
        |- word_002.jpg
        |- word_003.jpg
        | ...

    You can use the default parameters to generate a configuration file:

    # The code needs to be run in the specified directory
cd PaddleOCR/configs/rec/multi_language/
# Set the configuration file of the language to be generated through the -l or --language parameter.
# This command will write the default parameters into the configuration file
python3 generate_multi_language_configs.py -l it

  • If your data is placed in another location, or you want to use your own dictionary, you can generate the configuration file by specifying the relevant parameters:

    # -l or --language field is required
# --train to modify the training set
# --val to modify the validation set
# --data_dir to modify the data set directory
# --dict to modify the dict path
# -o to modify the corresponding default parameters
cd PaddleOCR/configs/rec/multi_language/
python3 generate_multi_language_configs.py -l it \  # language
--train {path/of/train_label.txt} \ # path of train_label
--val {path/of/val_label.txt} \     # path of val_label
--data_dir {train_data/path} \      # root directory of training data
--dict {path/of/dict} \             # path of dict
-o Global.use_gpu=False             # whether to use gpu
...

Italian is made up of Latin letters, so after executing the command, you will get the rec_latin_lite_train.yml.

  1. Manually modify the configuration file

You can also manually modify the following fields in the template:

    Global:
      use_gpu: True
      epoch_num: 500
      ...
      character_dict_path:  {path/of/dict} # path of dict

   Train:
      dataset:
        name: SimpleDataSet
        data_dir: train_data/ # root directory of training data
        label_file_list: ["./train_data/train_list.txt"] # train label path
      ...

   Eval:
      dataset:
        name: SimpleDataSet
        data_dir: train_data/ # root directory of val data
        label_file_list: ["./train_data/val_list.txt"] # val label path
      ...

Currently, the multi-language algorithms supported by PaddleOCR are:

Configuration file Algorithm name backbone trans seq pred language
rec_chinese_cht_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc chinese traditional
rec_en_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc English(Case sensitive)
rec_french_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc French
rec_ger_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc German
rec_japan_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc Japanese
rec_korean_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc Korean
rec_latin_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc Latin
rec_arabic_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc arabic
rec_cyrillic_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc cyrillic
rec_devanagari_lite_train.yml CRNN Mobilenet_v3 small 0.5 None BiLSTM ctc devanagari

For more supported languages, please refer to : Multi-language model

The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.