239 lines
18 KiB
Markdown
239 lines
18 KiB
Markdown
# Configuration
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- [1. Optional Parameter List](#1-optional-parameter-list)
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- [2. Intorduction to Global Parameters of Configuration File](#2-intorduction-to-global-parameters-of-configuration-file)
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- [3. Multilingual Config File Generation](#3-multilingual-config-file-generation)
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<a name="1-optional-parameter-list"></a>
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## 1. Optional Parameter List
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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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<a name="2-intorduction-to-global-parameters-of-configuration-file"></a>
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## 2. Intorduction to Global Parameters of Configuration File
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Take rec_chinese_lite_train_v2.0.yml as an example
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### Global
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| Parameter | Use | Defaults | Note |
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| :----------------------: | :---------------------: | :--------------: | :--------------------: |
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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 | Log queue length, the median value in the queue each time will be printed | 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/{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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| 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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| clip_norm | The maximum norm value | - | \ |
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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 PaddleOCR, 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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### PostProcess ([ppocr/postprocess](../../ppocr/postprocess))
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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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### Dataset ([ppocr/data](../../ppocr/data))
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| Parameter | Use | Defaults | Note |
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| :---------------------: | :---------------------: | :--------------: | :--------------------: |
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| **dataset** | Return one sample per iteration | - | - |
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| name | dataset class name | SimpleDataSet | Currently support`SimpleDataSet`,`LMDBDataSet` |
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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 LMDBDataSet |
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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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<a name="3-multilingual-config-file-generation"></a>
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## 3. Multilingual Config File Generation
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PaddleOCR currently supports 80 (except Chinese) language recognition. A multi-language configuration file template is
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provided under the path `configs/rec/multi_languages`: [rec_multi_language_lite_train.yml](../../configs/rec/multi_language/rec_multi_language_lite_train.yml)。
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There are two ways to create the required configuration file:：
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1. Automatically generated by script
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[generate_multi_language_configs.py](../../configs/rec/multi_language/generate_multi_language_configs.py) Can help you generate configuration files for multi-language models
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- Take Italian as an example, if your data is prepared in the following format:
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```
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|-train_data
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|- it_train.txt # train_set label
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|- it_val.txt # val_set label
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|- data
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|- word_001.jpg
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|- word_002.jpg
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|- word_003.jpg
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| ...
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```
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You can use the default parameters to generate a configuration file:
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```bash
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# The code needs to be run in the specified directory
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cd PaddleOCR/configs/rec/multi_language/
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# Set the configuration file of the language to be generated through the -l or --language parameter.
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# This command will write the default parameters into the configuration file
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python3 generate_multi_language_configs.py -l it
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```
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- 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:
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```bash
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# -l or --language field is required
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# --train to modify the training set
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# --val to modify the validation set
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# --data_dir to modify the data set directory
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# --dict to modify the dict path
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# -o to modify the corresponding default parameters
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cd PaddleOCR/configs/rec/multi_language/
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python3 generate_multi_language_configs.py -l it \ # language
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--train {path/of/train_label.txt} \ # path of train_label
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--val {path/of/val_label.txt} \ # path of val_label
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--data_dir {train_data/path} \ # root directory of training data
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--dict {path/of/dict} \ # path of dict
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-o Global.use_gpu=False # whether to use gpu
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...
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```
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Italian is made up of Latin letters, so after executing the command, you will get the rec_latin_lite_train.yml.
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2. Manually modify the configuration file
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You can also manually modify the following fields in the template:
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```
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Global:
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use_gpu: True
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epoch_num: 500
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...
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character_type: it # language
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character_dict_path: {path/of/dict} # path of dict
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ # root directory of training data
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label_file_list: ["./train_data/train_list.txt"] # train label path
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...
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: train_data/ # root directory of val data
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label_file_list: ["./train_data/val_list.txt"] # val label path
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...
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```
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Currently, the multi-language algorithms supported by PaddleOCR are:
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| Configuration file | Algorithm name | backbone | trans | seq | pred | language | character_type |
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| :--------: | :-------: | :-------: | :-------: | :-----: | :-----: | :-----: | :-----: |
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| rec_chinese_cht_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | chinese traditional | chinese_cht|
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| rec_en_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | English(Case sensitive) | EN |
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| rec_french_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | French | french |
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| rec_ger_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | German | german |
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| rec_japan_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Japanese | japan |
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| rec_korean_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Korean | korean |
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| rec_latin_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | Latin | latin |
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| rec_arabic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | arabic | ar |
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| rec_cyrillic_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | cyrillic | cyrillic |
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| rec_devanagari_lite_train.yml | CRNN | Mobilenet_v3 small 0.5 | None | BiLSTM | ctc | devanagari | devanagari |
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For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
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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.
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* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi.
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* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
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