For more installation tutorials, please refer to： [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
<aname="Data_preparation"></a>
## 2. Data preparation
Download the [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) dataset
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@@ -80,8 +65,6 @@ PubLayNet directory structure after decompressing ：
For other datasets，please refer to [the PrepareDataSet]((https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md) )
<aname="Configuration"></a>
## 3. Configuration
We use the `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml` configuration for training，the configuration file is as follows
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@@ -113,8 +96,6 @@ The `ppyolov2_r50vd_dcn_365e_coco.yml` configuration depends on other configurat
Modify the preceding files, such as the dataset path and batch size etc.
<aname="Training"></a>
## 4. Training
PaddleDetection provides single-card/multi-card training mode to meet various training needs of users:
`--draw_threshold` is an optional parameter. According to the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659), different threshold will produce different results, ` keep_top_k ` represent the maximum amount of output target, the default value is 10. You can set different value according to your own actual situation。
<aname="Deployment"></a>
## 6. Deployment
Use your trained model in Layout Parser
<aname="Export_model"></a>
### 6.1 Export model
n the process of model training, the model file saved contains the process of forward prediction and back propagation. In the actual industrial deployment, there is no need for back propagation. Therefore, the model should be translated into the model format required by the deployment. The `tools/export_model.py` script is provided in PaddleDetection to export the model.
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@@ -183,8 +158,6 @@ The prediction model is exported to `inference/ppyolov2_r50vd_dcn_365e_coco` ,in
More model export tutorials, please refer to：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
<aname="Inference"></a>
### 6.2 Inference
`model_path` represent the trained model path, and layoutparser is used to predict:
More PaddleDetection training tutorials，please reference：[PaddleDetection Training](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
@@ -51,10 +61,10 @@ After running, the excel sheet of each picture will be saved in the directory sp
In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
#### data preparation
* data preparation
The training data uses public data set [PubTabNet](https://arxiv.org/abs/1911.10683), Can be downloaded from the official [website](https://github.com/ibm-aur-nlp/PubTabNet) 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。
#### Start training
* Start training
*If you are installing the cpu version of paddle, please modify the `use_gpu` field in the configuration file to false*
In the above instruction, use `-c` to select the training to use the `configs/table/table_mv3.yml` configuration file.
For a detailed explanation of the configuration file, please refer to [config](../../doc/doc_en/config_en.md).
#### load trained model and continue training
* load trained model and continue training
If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
