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---
license: apache-2.0
---
# PicoDet_layout_1x_table
## Introduction
A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting table regions. The key metrics are as follow:
| Model| mAP(0.5) (%) |
| --- | --- |
|PicoDet_layout_1x_table | 97.5 |
## 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](https://www.paddlepaddle.org.cn/en/install/quick).
2. 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_layout_1x_table \
-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_layout_1x_table")
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': 0, 'label': 'Table', 'score': 0.9617661237716675, 'coordinate': [435.82446, 106.01748, 665.04346, 316.21014]}, {'cls_id': 0, 'label': 'Table', 'score': 0.9583022594451904, 'coordinate': [72.52834, 106.46287, 322.751, 301.454]}]}}
```
The visualized image is as follows:
For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/layout_detection.html#iii-quick-integration).
### 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.
```bash
paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/tuY1zoUdZsL6-9yGG0MpU.jpeg \
--layout_detection_model_name PicoDet_layout_1x_table \
--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:
```python
from paddleocr import TableRecognitionPipelineV2
pipeline = TableRecognitionPipelineV2(
layout_detection_model_name=PicoDet_layout_1x_table,
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_layout_1x_table` 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](https://paddlepaddle.github.io/PaddleOCR/main/en/version3.x/pipeline_usage/table_recognition_v2.html#2-quick-start).
## Links
[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr)
[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)
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