Quick Start

🛠️ Installation¶

❗Before installing PaddleX, please ensure you have a basic Python environment (Note: Currently supports Python 3.8 to Python 3.10, with more Python versions being adapted).

Installing PaddlePaddle

# cpu
python -m pip install paddlepaddle==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

# gpu，该命令仅适用于 CUDA 版本为 11.8 的机器环境
python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

# gpu，该命令仅适用于 CUDA 版本为 12.3 的机器环境
python -m pip install paddlepaddle-gpu==3.0.0b2 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/

❗For more PaddlePaddle versions, please refer to the PaddlePaddle official website.

Installing PaddleX

pip install https://paddle-model-ecology.bj.bcebos.com/paddlex/whl/paddlex-3.0.0b2-py3-none-any.whl

❗For more installation methods, refer to the PaddleX Installation Guide.

💻 CLI Usage¶

One command can quickly experience the pipeline effect, the unified CLI format is:

paddlex --pipeline [Pipeline Name] --input [Input Image] --device [Running Device]

You only need to specify three parameters: * pipeline: The name of the pipeline * input: The local path or URL of the input image to be processed * device: The GPU number used (for example, gpu:0 means using the 0^th GPU), you can also choose to use the CPU (cpu)

For example, using the OCR pipeline:

paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png  --device gpu:0

{
'input_path': '/root/.paddlex/predict_input/general_ocr_002.png',
'dt_polys': [array([[161,  27],
       [353,  22],
       [354,  69],
       [162,  74]], dtype=int16), array([[426,  26],
       [657,  21],
       [657,  58],
       [426,  62]], dtype=int16), array([[702,  18],
       [822,  13],
       [824,  57],
       [704,  62]], dtype=int16), array([[341, 106],
       [405, 106],
       [405, 128],
       [341, 128]], dtype=int16)
       ...],
'dt_scores': [0.758478200014338, 0.7021546472698513, 0.8536622648391111, 0.8619181462164781, 0.8321051217096188, 0.8868756173427551, 0.7982964727675609, 0.8289939036796322, 0.8289428877522524, 0.8587063317632897, 0.7786755892491615, 0.8502032769081344, 0.8703346500042997, 0.834490931790065, 0.908291103353393, 0.7614978661708064, 0.8325774055997542, 0.7843421347676149, 0.8680889482955594, 0.8788859304537682, 0.8963341277518075, 0.9364654810069546, 0.8092413027028257, 0.8503743089091863, 0.7920740420391101, 0.7592224394793805, 0.7920547400069311, 0.6641757962457888, 0.8650289477605955, 0.8079483304467047, 0.8532207681055275, 0.8913377034754717],
'rec_text': ['登机牌', 'BOARDING', 'PASS', '舱位', 'CLASS', '序号 SERIALNO.', '座位号', '日期 DATE', 'SEAT NO', '航班 FLIGHW', '035', 'MU2379', '始发地', 'FROM', '登机口', 'GATE', '登机时间BDT', '目的地TO', '福州', 'TAIYUAN', 'G11', 'FUZHOU', '身份识别IDNO', '姓名NAME', 'ZHANGQIWEI', 票号TKTNO', '张祺伟', '票价FARE', 'ETKT7813699238489/1', '登机口于起飞前10分钟关闭GATESCLOSE10MINUTESBEFOREDEPARTURETIME'],
'rec_score': [0.9985831379890442, 0.999696917533874512, 0.9985735416412354, 0.9842517971992493, 0.9383274912834167, 0.9943678975105286, 0.9419361352920532, 0.9221674799919128, 0.9555020928382874, 0.9870321154594421, 0.9664073586463928, 0.9988052248954773, 0.9979352355003357, 0.9985110759735107, 0.9943482875823975, 0.9991195797920227, 0.9936401844024658, 0.9974591135978699, 0.9743705987930298, 0.9980487823486328, 0.9874696135520935, 0.9900962710380554, 0.9952947497367859, 0.9950481653213501, 0.989926815032959, 0.9915552139282227, 0.9938777685165405, 0.997239887714386, 0.9963340759277344, 0.9936134815216064, 0.97223961353302]}

The visualization result is as follows:

To use the command line for other pipelines, simply adjust the pipeline parameter to the name of the corresponding pipeline. Below are the commands for each pipeline:

Pipeline Name	Command
Image Classification	`paddlex --pipeline image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0`
Object Detection	`paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --device gpu:0`
Instance Segmentation	`paddlex --pipeline instance_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_instance_segmentation_004.png --device gpu:0`
Semantic Segmentation	`paddlex --pipeline semantic_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/PaddleX3.0/application/semantic_segmentation/makassaridn-road_demo.png --device gpu:0`
Image Multi-label Classification	`paddlex --pipeline multi_label_image_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --device gpu:0`
Small Object Detection	`paddlex --pipeline small_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/small_object_detection.jpg --device gpu:0`
Image Anomaly Detection	`paddlex --pipeline anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/uad_grid.png --device gpu:0`
OCR	`paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0`
Table Recognition	`paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0`
Layout Parsing	`paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --device gpu:0`
Formula Recognition	`paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --device gpu:0`
Seal Recognition	`paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --device gpu:0`
Time Series Forecasting	`paddlex --pipeline ts_fc --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0`
Time Series Anomaly Detection	`paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0`
Time Series Classification	`paddlex --pipeline ts_cls --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0`

📝 Python Script Usage¶

With just a few lines of code, you can quickly perform inference on a production line. The unified Python script format is as follows:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline=[PipelineName])
output = pipeline.predict([InputImageName])
for res in output:
    res.print()
    res.save_to_img("./output/")
    res.save_to_json("./output/")

The following steps are executed:

create_pipeline() instantiates the production line object.
An image is passed in and the predict method of the production line object is called for inference and prediction.
The prediction results are processed.

For other production lines using the Python script, you only need to adjust the pipeline parameter of the create_pipeline() method to the corresponding production line name. Below is a list of each production line's corresponding parameter name and detailed usage explanation:

Production Line Name	Corresponding Parameter	Detailed Explanation
Document Scene Information Extraction v3	`PP-ChatOCRv3-doc`	Document Scene Information Extraction v3 Python Script Instructions
General Image Classification	`image_classification`	General Image Classification Python Script Instructions
General Object Detection	`object_detection`	General Object Detection Python Script Instructions
General Instance Segmentation	`instance_segmentation`	General Instance Segmentation Python Script Instructions
General Semantic Segmentation	`semantic_segmentation`	General Semantic Segmentation Python Script Instructions
Image Multi-label Classification	`multi_label_image_classification`	Image Multi-label Classification Python Script Instructions
Small Object Detection	`small_object_detection`	Small Object Detection Python Script Instructions
Image Anomaly Detection	`anomaly_detection`	Image Anomaly Detection Python Script Instructions
General OCR	`OCR`	General OCR Python Script Instructions
General Table Recognition	`table_recognition`	General Table Recognition Python Script Instructions
General Layout Parsing	`layout_parsing`	General Layout Parsing Python Script Instructions
Formula Recognition	`formula_recognition`	Formula Recognition Python Script Instructions
Seal Text Recognition	`seal_recognition`	Seal Text Recognition Python Script Instructions
Time Series Forecasting	`ts_fc`	Time Series Forecasting Python Script Instructions
Time Series Anomaly Detection	`ts_ad`	Time Series Anomaly Detection Python Script Instructions
Time Series Classification	`ts_cls`	Time Series Classification Python Script Instructions

Quick Start

🛠️ Installation¶

💻 CLI Usage¶

📝 Python Script Usage¶

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