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# OPTIONAL PARAMETERS LIST
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## Optional parameter list
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The following list can be viewed via `--help`
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The following list can be viewed through `--help`
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| FLAG | Supported script | Use | Defaults | Note |
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| :----------------------: | :------------: | :---------------: | :--------------: | :-----------------: |
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| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
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| -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` |
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| -c | ALL | Specify configuration file to use | None | **Please refer to the parameter introduction for configuration file usage** |
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| -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 |
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## INTRODUCTION TO GLOBAL PARAMETERS OF CONFIGURATION FILE
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Take `rec_chinese_lite_train_v1.1.yml` as an example
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Take rec_chinese_lite_train_v1.1.yml as an example
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### Global
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| Parameter | Use | Default | Note |
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| Parameter | Use | Defaults | Note |
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| :----------------------: | :---------------------: | :--------------: | :--------------------: |
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| algorithm | Select algorithm to use | Synchronize with configuration file | For selecting model, please refer to the supported model [list](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/README_en.md) |
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| use_gpu | Set using GPU or not | true | \ |
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| epoch_num | Maximum training epoch number | 3000 | \ |
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| use_gpu | Set using GPU or not | true | \ |
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| epoch_num | Maximum training epoch number | 500 | \ |
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| log_smooth_window | Sliding window size | 20 | \ |
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| print_batch_step | Set print log interval | 10 | \ |
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| save_model_dir | Set model save path | output/{model_name} | \ |
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| save_model_dir | Set model save path | output/{算法名称} | \ |
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| save_epoch_step | Set model save interval | 3 | \ |
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| 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 |
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|train_batch_size_per_card | Set the batch size during training | 256 | \ |
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| test_batch_size_per_card | Set the batch size during testing | 256 | \ |
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| image_shape | Set input image size | [3, 32, 100] | \ |
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| max_text_length | Set the maximum text length | 25 | \ |
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| 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|
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| character_dict_path | Set dictionary path | ./ppocr/utils/ic15_dict.txt | \ |
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| loss_type | Set loss type | ctc | Supports two types of loss: ctc / attention |
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| distort | Set use distort | false | Support distort type ,read [img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py) |
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| use_space_char | Wether to recognize space | false | Only support in character_type=ch mode |
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label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in the direction classifier |
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| reader_yml | Set the reader configuration file | ./configs/rec/rec_icdar15_reader.yml | \ |
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| pretrain_weights | Load pre-trained model path | ./pretrain_models/CRNN/best_accuracy | \ |
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| checkpoints | Load saved model path | None | Used to load saved parameters to continue training after interruption |
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| save_inference_dir | path to save model for inference | None | Use to save inference model |
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## INTRODUCTION TO READER PARAMETERS OF CONFIGURATION FILE
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Take `rec_chinese_reader.yml` as an example:
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| Parameter | Use | Default | Note |
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| :----------------------: | :---------------------: | :--------------: | :--------------------: |
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| reader_function | Select data reading method | ppocr.data.rec.dataset_traversal,SimpleReader | Support two data reading methods: SimpleReader / LMDBReader |
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| num_workers | Set the number of data reading threads | 8 | \ |
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| img_set_dir | Image folder path | ./train_data | \ |
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| label_file_path | Groundtruth file path | ./train_data/rec_gt_train.txt| \ |
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| infer_img | Result folder path | ./infer_img | \|
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| 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 |
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| 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 | \ |
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| load_static_weights | Set whether the pre-training model is saved in static graph mode (currently only required by the detection algorithm) | true | \ |
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| pretrained_model | Set the path of the pre-trained model | ./pretrain_models/CRNN/best_accuracy | \ |
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| checkpoints | set model parameter path | None | Used to load parameters after interruption to continue training|
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| use_visualdl | Set whether to enable visualdl for visual log display | False | [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
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| infer_img | Set inference image path or folder path | ./infer_img | \|
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| character_dict_path | Set dictionary path | ./ppocr/utils/ppocr_keys_v1.txt | \ |
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| max_text_length | Set the maximum length of text | 25 | \ |
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| 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 |
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| use_space_char | Set whether to recognize spaces | True | Only support in character_type=ch mode |
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| label_list | Set the angle supported by the direction classifier | ['0','180'] | Only valid in angle classifier model |
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| 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 |
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### Optimizer ([ppocr/optimizer](../../ppocr/optimizer))
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| name | Optimizer class name | Adam | Currently supports`Momentum`,`Adam`,`RMSProp`, see [ppocr/optimizer/optimizer.py](../../ppocr/optimizer/optimizer.py) |
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| beta1 | Set the exponential decay rate for the 1st moment estimates | 0.9 | \ |
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| beta2 | Set the exponential decay rate for the 2nd moment estimates | 0.999 | \ |
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| **lr** | Set the learning rate decay method | - | \ |
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| name | Learning rate decay class name | Cosine | Currently supports`Linear`,`Cosine`,`Step`,`Piecewise`, see[ppocr/optimizer/learning_rate.py](../../ppocr/optimizer/learning_rate.py) |
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| learning_rate | Set the base learning rate | 0.001 | \ |
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| **regularizer** | Set network regularization method | - | \ |
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| name | Regularizer class name | L2 | Currently support`L1`,`L2`, see[ppocr/optimizer/regularizer.py](../../ppocr/optimizer/regularizer.py) |
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| factor | Learning rate decay coefficient | 0.00004 | \ |
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### Architecture ([ppocr/modeling](../../ppocr/modeling))
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In ppocr, the network is divided into four stages: Transform, Backbone, Neck and Head
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| model_type | Network Type | rec | Currently support`rec`,`det`,`cls` |
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| algorithm | Model name | CRNN | See [algorithm_overview](./algorithm_overview.md) for the support list |
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| **Transform** | Set the transformation method | - | Currently only recognition algorithms are supported, see [ppocr/modeling/transform](../../ppocr/modeling/transform) for details |
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| name | Transformation class name | TPS | Currently supports `TPS` |
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| num_fiducial | Number of TPS control points | 20 | Ten on the top and bottom |
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| loc_lr | Localization network learning rate | 0.1 | \ |
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| model_name | Localization network size | small | Currently support`small`,`large` |
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| **Backbone** | Set the network backbone class name | - | see [ppocr/modeling/backbones](../../ppocr/modeling/backbones) |
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| name | backbone class name | ResNet | Currently support`MobileNetV3`,`ResNet` |
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| layers | resnet layers | 34 | Currently support18,34,50,101,152,200 |
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| model_name | MobileNetV3 network size | small | Currently support`small`,`large` |
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| **Neck** | Set network neck | - | see[ppocr/modeling/necks](../../ppocr/modeling/necks) |
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| name | neck class name | SequenceEncoder | Currently support`SequenceEncoder`,`DBFPN` |
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| encoder_type | SequenceEncoder encoder type | rnn | Currently support`reshape`,`fc`,`rnn` |
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| hidden_size | rnn number of internal units | 48 | \ |
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| out_channels | Number of DBFPN output channels | 256 | \ |
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| **Head** | Set the network head | - | see[ppocr/modeling/heads](../../ppocr/modeling/heads) |
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| name | head class name | CTCHead | Currently support`CTCHead`,`DBHead`,`ClsHead` |
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| fc_decay | CTCHead regularization coefficient | 0.0004 | \ |
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| k | DBHead binarization coefficient | 50 | \ |
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| class_dim | ClsHead output category number | 2 | \ |
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### Loss ([ppocr/losses](../../ppocr/losses))
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| name | loss class name | CTCLoss | Currently support`CTCLoss`,`DBLoss`,`ClsLoss` |
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| balance_loss | Whether to balance the number of positive and negative samples in DBLossloss (using OHEM) | True | \ |
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| ohem_ratio | The negative and positive sample ratio of OHEM in DBLossloss | 3 | \ |
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| main_loss_type | The loss used by shrink_map in DBLossloss | DiceLoss | Currently support`DiceLoss`,`BCELoss` |
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| alpha | The coefficient of shrink_map_loss in DBLossloss | 5 | \ |
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| beta | The coefficient of threshold_map_loss in DBLossloss | 10 | \ |
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## INTRODUCTION TO OPTIMIZER PARAMETERS OF CONFIGURATION FILE
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### PostProcess ([ppocr/postprocess](../../ppocr/postprocess))
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Take `rec_icdar15_train.yml` as an example:
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| name | Post-processing class name | CTCLabelDecode | Currently support`CTCLoss`,`AttnLabelDecode`,`DBPostProcess`,`ClsPostProcess` |
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| thresh | The threshold for binarization of the segmentation map in DBPostProcess | 0.3 | \ |
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| box_thresh | The threshold for filtering output boxes in DBPostProcess. Boxes below this threshold will not be output | 0.7 | \ |
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| max_candidates | The maximum number of text boxes output in DBPostProcess | 1000 | |
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| unclip_ratio | The unclip ratio of the text box in DBPostProcess | 2.0 | \ |
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### Metric ([ppocr/metrics](../../ppocr/metrics))
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| name | Metric method name | CTCLabelDecode | Currently support`DetMetric`,`RecMetric`,`ClsMetric` |
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| main_indicator | Main indicators, used to select the best model | acc | For the detection method is hmean, the recognition and classification method is acc |
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| Parameter | Use | Default | None |
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### Dataset ([ppocr/data](../../ppocr/data))
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| function | Select Optimizer function | pocr.optimizer,AdamDecay | Only support Adam |
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| base_lr | Set the base lr | 0.0005 | \ |
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| beta1 | Set the exponential decay rate for the 1st moment estimates | 0.9 | \ |
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| beta2 | Set the exponential decay rate for the 2nd moment estimates | 0.999 | \ |
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| decay | Whether to use decay | \ | \ |
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| function(decay) | Set the decay function | cosine_decay | Support cosine_decay, cosine_decay_warmup and piecewise_decay |
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| step_each_epoch | The number of steps in an epoch. Used in cosine_decay/cosine_decay_warmup | 20 | Calculation: total_image_num / (batch_size_per_card * card_size) |
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| total_epoch | The number of epochs. Used in cosine_decay/cosine_decay_warmup | 1000 | Consistent with Global.epoch_num |
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| warmup_minibatch | Number of steps for linear warmup. Used in cosine_decay_warmup | 1000 | \ |
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| boundaries | The step intervals to reduce learning rate. Used in piecewise_decay | - | The format is list |
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| decay_rate | Learning rate decay rate. Used in piecewise_decay | - | \ |
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| **dataset** | Return one sample per iteration | - | - |
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| name | dataset class name | SimpleDataSet | Currently support`SimpleDataSet`,`LMDBDateSet` |
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| data_dir | Image folder path | ./train_data | \ |
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| label_file_list | Groundtruth file path | ["./train_data/train_list.txt"] | This parameter is not required when dataset is LMDBDateSet |
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| 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 |
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| transforms | List of methods to transform images and labels | [DecodeImage,CTCLabelEncode,RecResizeImg,KeepKeys] | see[ppocr/data/imaug](../../ppocr/data/imaug) |
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| **loader** | dataloader related | - | |
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| shuffle | Does each epoch disrupt the order of the data set | True | \ |
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| batch_size_per_card | Single card batch size during training | 256 | \ |
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| 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 | \ |
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| 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 | \ |
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