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PaddlePaddle / PicoDet-S_layout_17cls
Model's Last Updated: July 22 2025
image-to-text
Introduction of PicoDet-S_layout_17cls
Model Details of PicoDet-S_layout_17cls
PicoDet-S_layout_17cls
Introduction
A high-efficiency layout area localization model trained on a self-built dataset of Chinese and English papers, magazines, and research reports using PicoDet-S. 17-Class Area Detection Model, including 17 common layout categories: Paragraph Title, Image, Text, Number, Abstract, Content, Figure Caption, Formula, Table, Table Caption, References, Document Title, Footnote, Header, Algorithm, Footer, and Seal. The key metrics are as follow:
| Model | mAP(0.5) (%) |
|---|---|
| PicoDet-S_layout_17cls | 87.4 |
Note : Paddleocr's self built layout area detection data set contains 892 common document type images such as Chinese and English papers, magazines and research papers.
Quick Start
Installation
- PaddlePaddle
Please refer to the following commands to install PaddlePaddle using pip:
# for CUDA11.8
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
# for CUDA12.6
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
# for CPU
python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
For details about PaddlePaddle installation, please refer to the PaddlePaddle official website .
- PaddleOCR
Install the latest version of the PaddleOCR inference package from PyPI:
python -m pip install paddleocr
Model Usage
You can quickly experience the functionality with a single command:
paddleocr layout_detection \
--model_name PicoDet-S_layout_17cls \
-i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/N5C68HPVAI-xQAWTxpbA6.jpeg
You can also integrate the model inference of the layout detection module into your project. Before running the following code, please download the sample image to your local machine.
from paddleocr import LayoutDetection
model = LayoutDetection(model_name="PicoDet-S_layout_17cls")
output = model.predict("N5C68HPVAI-xQAWTxpbA6.jpeg", batch_size=1, layout_nms=True)
for res in output:
res.print()
res.save_to_img(save_path="./output/")
res.save_to_json(save_path="./output/res.json")
After running, the obtained result is as follows:
{'res': {'input_path': '/root/.paddlex/predict_input/N5C68HPVAI-xQAWTxpbA6.jpeg', 'page_index': None, 'boxes': [{'cls_id': 2, 'label': 'text', 'score': 0.9770552515983582, 'coordinate': [35.47857, 350.32135, 359.99146, 607.66266]}, {'cls_id': 2, 'label': 'text', 'score': 0.9646613597869873, 'coordinate': [387.4421, 736.655, 712.7056, 850.04584]}, {'cls_id': 2, 'label': 'text', 'score': 0.9596860408782959, 'coordinate': [386.36847, 491.56995, 712.53467, 700.74225]}, {'cls_id': 2, 'label': 'text', 'score': 0.958072304725647, 'coordinate': [36.283585, 648.58374, 360.08328, 849.8185]}, {'cls_id': 8, 'label': 'table', 'score': 0.9491577744483948, 'coordinate': [62.92192, 104.4335, 330.56216, 299.23947]}, {'cls_id': 8, 'label': 'table', 'score': 0.9144826531410217, 'coordinate': [424.8911, 104.26025, 668.0119, 312.7304]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.8392149806022644, 'coordinate': [35.642235, 332.62488, 144.75916, 345.07657]}, {'cls_id': 0, 'label': 'paragraph_title', 'score': 0.796416699886322, 'coordinate': [389.69257, 717.66345, 526.9109, 729.3157]}, {'cls_id': 2, 'label': 'text', 'score': 0.6687009930610657, 'coordinate': [391.08237, 348.05475, 713.2955, 460.1097]}, {'cls_id': 2, 'label': 'text', 'score': 0.6419706344604492, 'coordinate': [35.950676, 21.344364, 361.3897, 79.71692]}, {'cls_id': 2, 'label': 'text', 'score': 0.5499911308288574, 'coordinate': [386.94125, 19.868908, 713.5697, 75.554535]}]}}
The visualized image is as follows:
For details about usage command and descriptions of parameters, please refer to the Document .
Pipeline Usage
The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenarios.
PP-TableMagic (table_recognition_v2)
The General Table Recognition v2 pipeline (PP-TableMagic) is designed to tackle table recognition tasks, identifying tables in images and outputting them in HTML format. PP-TableMagic includes the following 8 modules:
- Table Structure Recognition Module
- Table Classification Module
- Table Cell Detection Module
- Text Detection Module
- Text Recognition Module
- Layout Region Detection Module (optional)
- Document Image Orientation Classification Module (optional)
- Text Image Unwarping Module (optional)
You can quickly experience the PP-TableMagic pipeline with a single command.
paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/tuY1zoUdZsL6-9yGG0MpU.jpeg \
--layout_detection_model_name PicoDet-S_layout_17cls \
--use_doc_orientation_classify False \
--use_doc_unwarping False \
--save_path ./output \
--device gpu:0
If save_path is specified, the visualization results will be saved under
save_path
.
The command-line method is for quick experience. For project integration, also only a few codes are needed as well:
from paddleocr import TableRecognitionPipelineV2
pipeline = TableRecognitionPipelineV2(
layout_detection_model_name=PicoDet-S_layout_17cls,
use_doc_orientation_classify=False, # Use use_doc_orientation_classify to enable/disable document orientation classification model
use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
device="gpu:0", # Use device to specify GPU for model inference
)
output = pipeline.predict("tuY1zoUdZsL6-9yGG0MpU.jpeg")
for res in output:
res.print() ## Print the predicted structured output
res.save_to_img("./output/")
res.save_to_xlsx("./output/")
res.save_to_html("./output/")
res.save_to_json("./output/")
The default model used in pipeline is
PP-DocLayout-L
, so it is needed that specifing to
PicoDet-S_layout_17cls
by argument
layout_detection_model_name
. And you can also use the local model file by argument
layout_detection_model_dir
. For details about usage command and descriptions of parameters, please refer to the
Document
.
Links
Runs of PaddlePaddle PicoDet-S_layout_17cls on huggingface.co
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