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PaddleX

🌟 Features | 🌐 Online Experience｜🚀 Quick Start | 📖 Documentation | 🔥Capabilities | 📋 Models

🇨🇳 Simplified Chinese | 🇬🇧 English

🔍 Introduction

PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePaddle framework. It integrates numerous ready-to-use pre-trained models, enabling full-process development from model training to inference, supporting a variety of mainstream hardware both domestic and international, and aiding AI developers in industrial practice.

Image Classification Multi-label Image Classification Object Detection Instance Segmentation
Semantic Segmentation Image Anomaly Detection OCR Table Recognition
PP-ChatOCRv3-doc Time Series Forecasting Time Series Anomaly Detection Time Series Classification

🌟 Why PaddleX ?

🎨 Rich Models One-click Call: Integrate over 200 PaddlePaddle models covering multiple key areas such as OCR, object detection, and time series forecasting into 19 pipelines. Experience the model effects quickly through easy Python API calls. Also supports more than 20 modules for easy model combination use by developers.

🚀 High Efficiency and Low barrier of entry: Achieve model full-process development based on graphical interfaces and unified commands, creating 8 featured model pipelines that combine large and small models, semi-supervised learning of large models, and multi-model fusion, greatly reducing the cost of iterating models.

🌐 Flexible Deployment in Various Scenarios: Support various deployment methods such as high-performance inference, service deployment, and lite deployment to ensure efficient operation and rapid response of models in different application scenarios.

🔧 Efficient Support for Mainstream Hardware: Support seamless switching of various mainstream hardware such as NVIDIA GPUs, Kunlun XPU, Ascend NPU, and Cambricon MLU to ensure efficient operation.

📣 Recent Updates

🔥🔥 "PaddleX Document Information Personalized Extraction Upgrade", PP-ChatOCRv3 innovatively provides custom development functions for OCR models based on data fusion technology, offering stronger model fine-tuning capabilities. Millions of high-quality general OCR text recognition data are automatically integrated into vertical model training data at a specific ratio, solving the problem of weakened general text recognition capabilities caused by vertical model training in the industry. Suitable for practical scenarios in industries such as automated office, financial risk control, healthcare, education and publishing, and legal and government sectors. October 24th (Thursday) 19:00 Join our live session for an in-depth analysis of the open-source version of PP-ChatOCRv3 and the outstanding advantages of PaddleX 3.0 Beta1 in terms of accuracy and speed. Registration Link

❗ Get more courses for free

🔥🔥 9.30, 2024, PaddleX 3.0 Beta1 open source version is officially released, providing more than 200 models that can be called with a simple Python API; achieve model full-process development based on unified commands, and open source the basic capabilities of the PP-ChatOCRv3 pipeline; support more than 100 models for high-performance inference and service-oriented deployment (iterating continuously), more than 7 key visual models for edge-deployment; more than 70 models have been adapted for the full development process of Ascend 910B, more than 15 models have been adapted for the full development process of Kunlun chips and Cambricon

🔥 6.27, 2024, PaddleX 3.0 Beta open source version is officially released, supporting the use of various mainstream hardware for pipeline and model development in a low-code manner on the local side.

🔥 3.25, 2024, PaddleX 3.0 cloud release, supporting the creation of pipelines in the AI Studio Galaxy Community in a zero-code manner.

🔠 Explanation of Pipeline

PaddleX is dedicated to achieving pipeline-level model training, inference, and deployment. A pipeline refers to a series of predefined development processes for specific AI tasks, which includes a combination of single models (single-function modules) capable of independently completing a certain type of task.

📊 What can PaddleX do？

All pipelines of PaddleX support online experience on AI Studio and local fast inference. You can quickly experience the effects of each pre-trained pipeline. If you are satisfied with the effects of the pre-trained pipeline, you can directly perform high-performance inference / serving deployment / edge deployment on the pipeline. If not satisfied, you can also Custom Development to improve the pipeline effect. For the complete pipeline development process, please refer to the PaddleX pipeline Development Tool Local Use Tutorial.

In addition, PaddleX provides developers with a full-process efficient model training and deployment tool based on a cloud-based GUI. Developers do not need code development, just need to prepare a dataset that meets the pipeline requirements to quickly start model training. For details, please refer to the tutorial "Developing Industrial-level AI Models with Zero Barrier".

Pipeline Online Experience Local Inference High-Performance Inference Service-Oriented Deployment Edge Deployment Custom Development Zero-Code Development On AI Studio
OCR Link ✅ ✅ ✅ ✅ ✅ ✅
PP-ChatOCRv3 Link ✅ ✅ ✅ 🚧 ✅ ✅
Table Recognition Link ✅ ✅ ✅ 🚧 ✅ ✅
Object Detection Link ✅ ✅ ✅ ✅ ✅ ✅
Instance Segmentation Link ✅ ✅ ✅ 🚧 ✅ ✅
Image Classification Link ✅ ✅ ✅ ✅ ✅ ✅
Semantic Segmentation Link ✅ ✅ ✅ ✅ ✅ ✅
Time Series Forecasting Link ✅ 🚧 ✅ 🚧 ✅ ✅
Time Series Anomaly Detection Link ✅ 🚧 ✅ 🚧 ✅ ✅
Time Series Classification Link ✅ 🚧 ✅ 🚧 ✅ ✅
Small Object Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Multi-label Image Classification 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Image Anomaly Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Layout Parsing 🚧 ✅ 🚧 ✅ 🚧 ✅ 🚧
Formula Recognition 🚧 ✅ 🚧 ✅ 🚧 ✅ 🚧
Seal Recognition 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Pedestrian Attribute Recognition 🚧 ✅ 🚧 ✅ 🚧 ✅ 🚧
Vehicle Attribute Recognition 🚧 ✅ 🚧 ✅ 🚧 ✅ 🚧
Face Recognition 🚧 ✅ 🚧 ✅ 🚧 ✅ 🚧

❗Note: The above capabilities are implemented based on GPU/CPU. PaddleX can also perform local inference and custom development on mainstream hardware such as Kunlunxin, Ascend, Cambricon, and Haiguang. The table below details the support status of the pipelines. For specific supported model lists, please refer to the Model List (Kunlunxin XPU)/Model List (Ascend NPU)/Model List (Cambricon MLU)/Model List (Haiguang DCU). We are continuously adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.

🔥🔥 Support for Domestic Hardware Capabilities

Pipeline Ascend 910B Kunlunxin R200/R300 Cambricon MLU370X8 Haiguang Z100
OCR ✅ ✅ ✅ 🚧
Table Recognition ✅ 🚧 🚧 🚧
Object Detection ✅ ✅ ✅ 🚧
Instance Segmentation ✅ 🚧 ✅ 🚧
Image Classification ✅ ✅ ✅ ✅
Semantic Segmentation ✅ ✅ ✅ ✅
Time Series Forecasting ✅ ✅ ✅ 🚧
Time Series Anomaly Detection ✅ 🚧 🚧 🚧
Time Series Classification ✅ 🚧 🚧 🚧

⏭️ 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). The PaddleX 3.0-beta2 version depends on PaddlePaddle version 3.0.0b2.

  • 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-3.0.0b1-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 0th 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
👉 Click to view the running result 
{
'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:

alt text

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:

👉 More CLI usage for pipelines
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
Pedestrian Attribute Recognition paddlex --pipeline pedestrian_attribute --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --device gpu:0
Vehicle Attribute Recognition paddlex --pipeline vehicle_attribute --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --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

A few lines of code can complete the quick inference of the pipeline, the unified Python script format is as follows:

from paddlex import create_pipeline

pipeline = create_pipeline(pipeline=[Pipeline Name])
output = pipeline.predict([Input Image Name])
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 pipeline object
  • Passes the image and calls the predict method of the pipeline object for inference prediction
  • Processes the prediction results

For other pipelines in Python scripts, just adjust the pipeline parameter of the create_pipeline() method to the corresponding name of the pipeline. Below is a list of each pipeline's corresponding parameter name and detailed usage explanation:

👉 More Python script usage for pipelines
pipeline Name Corresponding Parameter Detailed Explanation
PP-ChatOCRv3-doc PP-ChatOCRv3-doc PP-ChatOCRv3-doc Pipeline Python Script Usage Instructions
Image Classification image_classification Image Classification Pipeline Python Script Usage Instructions
Object Detection object_detection Object Detection Pipeline Python Script Usage Instructions
Instance Segmentation instance_segmentation Instance Segmentation Pipeline Python Script Usage Instructions
Semantic Segmentation semantic_segmentation Semantic Segmentation Pipeline Python Script Usage Instructions
Image Multi-Label Classification multilabel_classification Image Multi-Label Classification Pipeline Python Script Usage Instructions
Small Object Detection small_object_detection Small Object Detection Pipeline Python Script Usage Instructions
Image Anomaly Detection image_classification Image Anomaly Detection Pipeline Python Script Usage Instructions
Image Recognition PP-ShiTuV2 Image Recognition Pipeline Python Script Usage Instructions
Face Recognition face_recognition Face Recognition Pipeline Python Script Usage Instructions
Pedestrian Attribute Recognition pedestrian_attribute Pedestrian Attribute Recognition Pipeline Python Script Usage Instructions
Vehicle Attribute Recognition vehicle_attribute Vehicle Attribute Recognition Pipeline Python Script Usage Instructions
OCR OCR OCR Pipeline Python Script Usage Instructions
Table Recognition table_recognition Table Recognition Pipeline Python Script Usage Instructions
Layout Parsing layout_parsing Layout Parsing Pipeline Python Script Usage Instructions
Formula Recognition formula_recognition Formula Recognition Pipeline Python Script Usage Instructions
Seal Recognition seal_recognition Seal Recognition Pipeline Python Script Usage Instructions
Time Series Forecast ts_forecast Time Series Forecast Pipeline Python Script Usage Instructions
Time Series Anomaly Detection ts_anomaly_detection Time Series Anomaly Detection Pipeline Python Script Usage Instructions
Time Series Classification ts_cls Time Series Classification Pipeline Python Script Usage Instructions

📖 Documentation

⬇️ Installation
🔥 Pipeline Usage
⚙️ Module Usage
🏗️ Pipeline Deployment
🖥️ Multi-Hardware Usage
📝 Tutorials & Examples

🤔 FAQ

For answers to some common questions about our project, please refer to the FAQ. If your question has not been answered, please feel free to raise it in Issues.

💬 Discussion

We warmly welcome and encourage community members to raise questions, share ideas, and feedback in the Discussions section. Whether you want to report a bug, discuss a feature request, seek help, or just want to keep up with the latest project news, this is a great platform.

📄 License

The release of this project is licensed under the Apache 2.0 license.