Modelpublic

PaddlePaddle/PicoDet-S_layout_17cls

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

1likes29downloads

Model Card

PicoDet-Slayout17cls

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-Slayout17cls	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

1.PaddlePaddle

Please refer to the following commands to install PaddlePaddle using pip:

bash

# 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.

1.PaddleOCR

Install the latest version of the PaddleOCR inference package from PyPI:

bash

python -m pip install paddleocr

Model Usage

You can quickly experience the functionality with a single command:

bash

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.

python

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:

json

{'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:

image/jpeg

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 (tablerecognitionv2)

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.

bash

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 savepath is specified, the visualization results will be saved under `savepath`.

The command-line method is for quick experience. For project integration, also only a few codes are needed as well:

python

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

PaddleOCR Repo

PaddleOCR Documentation