config_en.md 13 KB
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 |
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] | 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 ppocr, 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,LMDBDateSet |
| 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 LMDBDateSet |
| 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 |