| picodet_lcnet_x1_0_fgd_layout | The layout analysis English model trained on the PubLayNet dataset based on PicoDet LCNet_x1_0 and FGD . the model can recognition 5 types of areas such as **Text, Title, Table, Picture and List** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout.pdparams) | [PubLayNet dict](../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt) |
| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221M | [inference_moel]](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | sme as above |
| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis English model trained on the PubLayNet dataset based on PP-YOLOv2 | 221M | [inference_moel](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) / [trained model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet_pretrained.pdparams) | sme as above |
| picodet_lcnet_x1_0_fgd_layout_cdla | The layout analysis Chinese model trained on the CDLA dataset, the model can recognition 10 types of areas such as **Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation** | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla.pdparams) | [CDLA dict](../../ppocr/utils/dict/layout_dict/layout_cdla_dict.txt) |
| picodet_lcnet_x1_0_fgd_layout_table | The layout analysis model trained on the table dataset, the model can detect tables in Chinese and English documents | 9.7M | [inference model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_table.pdparams) | [Table dict](../../ppocr/utils/dict/layout_dict/layout_table_dict.txt) |
| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset based on PP-YOLOv2, the model can detect tables in English documents | 221M | [inference model](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) | same as above |
-[5.2 Test layout analysis results](#52-Test layout analysis results)
-[6 Model export and inference](#6-Model export and inference)
-[6.1 Model export](#61-Model export)
-[6.2 Model inference](#62-Model inference)
# Layout analysis
## 1. Introduction
Layout analysis refers to the regional division of documents in the form of pictures and the positioning of key areas, such as text, title, table, picture, etc. The layout analysis algorithm is based on the lightweight model PP-picodet of [PaddeDetection](https://github.com/PaddlePaddle/PaddleDetection)
| `train.json` | Training set annotation files | - |
| `val.json` | Validation set dimension files | - |
**标注格式：**
The JSON file contains the annotations of all images, and the data is stored in a dictionary nested manner.Contains the following keys：
- info，represents the dimension file info。
- licenses，represents the dimension file licenses。
- images，represents the list of image information in the annotation file，each element is the information of an image。The information of one of the images is as follows:
```
{
'file_name': 'PMC4055390_00006.jpg', # file_name
'height': 601, # image height
'width': 792, # image width
'id': 341427 # image id
}
```
- annotations， represents the list of annotation information of the target object in the annotation file，each element is the annotation information of a target object。The following is the annotation information of one of the target objects:
```
{
'segmentation': # Segmentation annotation of objects
We provide CDLA(Chinese layout analysis)、TableBank(Table layout analysis)etc. data set download links，process to the JSON format of the above annotation file，that is, the training can be conducted in the same way。
| [cTDaR2019_cTDaR](https://cndplab-founder.github.io/cTDaR2019/) | For form detection (TRACKA) and form identification (TRACKB).Image types include historical data sets (beginning with cTDaR_t0, such as CTDAR_T00872.jpg) and modern data sets (beginning with cTDaR_t1, CTDAR_T10482.jpg). |
| [IIIT-AR-13K](http://cvit.iiit.ac.in/usodi/iiitar13k.php) | Data sets constructed by manually annotating figures or pages from publicly available annual reports, containing 5 categories:table, figure, natural image, logo, and signature. |
| [TableBank](https://github.com/doc-analysis/TableBank) | For table detection and recognition of large datasets, including Word and Latex document formats |
| [CDLA](https://github.com/buptlihang/CDLA) | Chinese document layout analysis data set, for Chinese literature (paper) scenarios, including 10 categories:Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation |
Training scripts, evaluation scripts, and prediction scripts are provided, and the PubLayNet pre-training model is used as an example in this section.
If you do not want training and directly experience the following process of model evaluation, prediction, motion to static, and inference, you can download the provided pre-trained model (PubLayNet dataset) and skip this part.
```
mkdir pretrained_model
cd pretrained_model
# Download PubLayNet pre-training model（Direct experience model evaluates, predicts, and turns static）
If the test image is Chinese, the pre-trained model of Chinese CDLA dataset can be downloaded to identify 10 types of document regions：Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation，Download the training model and inference model of Model 'picodet_lcnet_x1_0_fgd_layout_cdla' in [layout analysis model](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/docs/models_list.md)。If only the table area in the image is detected, you can download the pre-trained model of the table dataset, and download the training model and inference model of the 'picodet_LCnet_x1_0_FGd_layout_table' model in [Layout Analysis model](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppstructure/docs/models_list.md)
### 4.1. Train
Train:
* Modify Profile
If you want to train your own data set, you need to modify the data configuration and the number of categories in the configuration file.
Using 'configs/picodet/legacy_model/application/layout_analysis/picodet_lcnet_x1_0_layout.yml' as an example, the change is as follows:
**Attention：**If the video memory is out during training, adjust Batch_size in TrainReader and base_LR in LearningRate. The published config is obtained by 8-card training. If the number of GPU cards is changed to 1, then the base_LR needs to be reduced by 8 times.
After starting training normally, you will see the following log output:
```
[08/15 04:02:30] ppdet.utils.checkpoint INFO: Finish loading model weights: /root/.cache/paddle/weights/LCNet_x1_0_pretrained.pdparams
-`--eval` indicates that the best model is saved as `output/picodet_lcnet_x1_0_layout/best_accuracy` by default during the evaluation process 。
**Note that the configuration file for prediction / evaluation must be consistent with the training.**
### 4.2. FGD Distillation Training
PaddleDetection supports FGD-based [Focal and Global Knowledge Distillation for Detectors](https://arxiv.org/abs/2111.11837v1) The training process of the target detection model of distillation, FGD distillation is divided into two parts `Focal` and `Global`. `Focal` Distillation separates the foreground and background of the image, allowing the student model to focus on the key pixels of the foreground and background features of the teacher model respectively;` Global`Distillation section reconstructs the relationships between different pixels and transfers them from the teacher to the student to compensate for the global information lost in `Focal`Distillation.
Change the dataset and modify the data configuration and number of categories in the [TODO] configuration, referring to 4.1. Start training:
-`--slim_config`: Specify the compression policy profile.
## 5. Model evaluation and prediction
### 5.1. Indicator evaluation
Model parameters in training are saved by default in `output/picodet_ Lcnet_ X1_ 0_ Under the layout` directory. When evaluating indicators, you need to set `weights` to point to the saved parameter file.Assessment datasets can be accessed via `configs/picodet/legacy_ Model/application/layout_ Analysis/picodet_ Lcnet_ X1_ 0_ Layout. Yml` . Modify `EvalDataset` : `img_dir`,`anno_ Path`and`dataset_dir` setting.
```bash
# GPU evaluation, weights as weights to be measured
If you use the provided pre-training model for evaluation or the FGD distillation training model, replace the `weights` model path and execute the following command for evaluation:
-`--slim_config`: Specify the distillation policy profile.
-`-o weights`: Specify the model path trained by the distillation algorithm.
### 5.2. Test Layout Analysis Results
The profile predicted to be used must be consistent with the training, for example, if you pass `python3 tools/train'. Py-c configs/picodet/legacy_ Model/application/layout_ Analysis/picodet_ Lcnet_ X1_ 0_ Layout. Yml` completed the training process for the model.
With trained PaddleDetection model, you can use the following commands to make model predictions.
-`--infer_img`: Reasoning for a single picture can also be done via `--infer_ Dir`Inform all pictures in the file.
-`--output_dir`: Specify the path to save the visualization results.
-`--draw_threshold`:Specify the NMS threshold for drawing the result box.
If you use the provided pre-training model for prediction or the FGD distillation training model, change the `weights` model path and execute the following command to make the prediction:
The inference model (the model saved by `paddle.jit.save`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
Compared with the checkpoints model, the inference model will additionally save the structural information of the model. Therefore, it is easier to deploy because the model structure and model parameters are already solidified in the inference model file, and is suitable for integration with actual systems.
Layout analysis model to inference model steps are as follows：
* If no post-export processing is required, specify：`-o export.benchmark=True`（If -o already exists, delete -o here）
* If you do not need to export NMS, specify：`-o export.nms=False`
After successful conversion, there are three files in the directory:
```
output_inference/picodet_lcnet_x1_0_layout/
├── model.pdiparams # inference Parameter file for model
├── model.pdiparams.info # inference Model parameter information, ignorable
└── model.pdmodel # inference Model Structure File for Model
```
If you change the `weights` model path using the provided pre-training model to the Inference model, or using the FGD distillation training model, the model to inference model steps are as follows:
Replace model_with the provided inference training model for inference or the FGD distillation training `model_dir`Inference model path, execute the following commands for inference:
- class_id、confidence、left_top、right_bottom：Indicates category id, confidence level, upper left coordinate, lower right coordinate, respectively
- save result to：Save path of visual layout analysis results, default save to ./output folder
- inference time info：Inference time, where preprocess_time represents the preprocessing time, Inference_time represents the model prediction time, and postprocess_time represents the post-processing time
The result of visualization layout is shown in the following figure
The layout restoration is exported as docx and PDF files, so python-docx and docx2pdf API need to be installed, and fitz and PyMuPDF apis need to be installed to process the input files in pdf format.
The layout restoration is exported as docx and PDF files, so python-docx and docx2pdf API need to be installed, and PyMuPDF api([requires Python >= 3.7](https://pypi.org/project/PyMuPDF/)) need to be installed to process the input files in pdf format.
