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 |
Take rec_chinese_lite_train_v2.0.yml as an example
| 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] | runing 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 |
| infer_img | Set inference image path or folder path | ./infer_img | | |
| character_dict_path | Set dictionary path | ./ppocr/utils/ppocr_keys_v1.txt | \ |
| max_text_length | Set the maximum length of text | 25 | \ |
| character_type | Set character type | ch | en/ch, the default dict will be used for en, and the custom dict will be used for ch |
| use_space_char | Set whether to recognize spaces | True | Only support in character_type=ch mode |
| 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 | Learning rate decay coefficient | 0.00004 | \ |
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 | \ |
PaddleOCR currently supports 80 (except Chinese) language recognition. 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:：
- Automatically generated by 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.
-
Manually modify the configuration file
You can also manually modify the following fields in the template:
Global: use_gpu: True epoch_num: 500 ... character_type: it # language 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 | character_type |
|---|---|---|---|---|---|---|---|
| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | chinese traditional | chinese_cht |
| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English(Case sensitive) | EN |
| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French | french |
| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German | german |
| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese | japan |
| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean | korean |
| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Latin | latin |
| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | arabic | ar |
| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | cyrillic | cyrillic |
| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | devanagari | 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.
- Baidu Netdisk,Extraction code:frgi.
- Google drive