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 |
| 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 |
\ |
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 |
\ |
| 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 |
\ |
| 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 |
\ |
| 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 |
| 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 |
\ |
