zhengchun/PaddleX: https://github.com/PaddlePaddle/PaddleX.git @ dcb1f5d133eda6e448c5ca8f5411b9dda41223f4

🌟 Features | 🌐 Online Experience｜🚀 Quick Start | 📖 Documentation | 🔥Pipelines List

🇨🇳 Simplified Chinese | 🇬🇧 English

🔍 Introduction

PaddleX 3.0 is a low-code development tool for AI models built on the PaddlePaddle framework. It i ntegrates 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	Time Series Forecasting	Time Series Anomaly Detection	Time Series Classification

🌟 Why PaddleX ?

🔥🔥《PaddleX Document Information Personalized Extraction Upgrade》，PP-ChatOCRv3 innovatively provides OCR model secondary development capabilities based on data fusion technology, with stronger model fine-tuning capabilities. Millions of high-quality general OCR text recognition data are automatically integrated into the vertical model training data at specific ratios, solving the problem of weakening general text recognition capabilities caused by industry-specific model training. Suitable for actual scenarios in industries such as automated office, financial risk control, healthcare, education and publishing, and legal party and government. October 10th (Thursday) 19:00 live broadcast to detail the data fusion technology and how to use prompt engineering to achieve better information extraction effects. 🎨 Rich Models One-click Call: Integrate over 200 PaddlePaddle models covering multiple key areas such as OCR, object detection, and time series forecasting into 13 model pipelines. Experience the model effects quickly through minimalist 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 deployment, 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

🔥🔥 9.30, 2024, PaddleX 3.0 Beta1 open source version is officially released, providing more than 200 models that can be called with a minimalist Python API; achieve model full-process development based on unified commands, and open source the basic capabilities of the PP-ChatOCRv3 featured model pipeline; support more than 100 models for high-performance inference and service-oriented deployment (iterating continuously), more than 7 key visual models for edge-side 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.

📊 What can PaddleX do？

All pipelines of PaddleX support online experience 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 deployment / serving deployment / edge deployment on the pipeline. If not satisfied, you can also second 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 graphical development interface. 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 Threshold".

Model pipeline	Online Experience	Quick Inference	High-Performance Deployment	Service Deployment	Edge Deployment	Secondary Development	Galaxy Zero-Code pipeline
General OCR	Link	✅	✅	✅	✅	✅	✅
Document Scene Information Extraction v3	Link	✅	✅	✅	🚧	✅	✅
Table Recognition	Link	✅	✅	✅	🚧	✅	✅
General Object Detection	Link	✅	✅	✅	✅	✅	✅
General Instance Segmentation	Link	✅	✅	✅	🚧	✅	✅
General Image Classification	Link	✅	✅	✅	✅	✅	✅
General Semantic Segmentation	Link	✅	✅	✅	✅	✅	✅
Time Series Forecasting	Link	✅	🚧	✅	🚧	✅	✅
Time Series Anomaly Detection	Link	✅	🚧	✅	🚧	✅	✅
Time Series Classification	Link	✅	🚧	✅	🚧	✅	✅
Small Object Detection	🚧	✅	✅	✅	🚧	✅	🚧
Image Multi-Label Classification	🚧	✅	✅	✅	🚧	✅	🚧
Image Anomaly Detection	🚧	✅	✅	✅	🚧	✅	🚧
Formula Recognition	🚧	🚧	🚧	🚧	🚧	🚧	🚧
Seal Recognition	🚧	🚧	🚧	🚧	🚧	🚧	🚧
General Image Recognition	🚧	🚧	🚧	🚧	🚧	🚧	🚧
Pedestrian Attribute Recognition	🚧	🚧	🚧	🚧	🚧	🚧	🚧
Vehicle Attribute Recognition	🚧	🚧	🚧	🚧	🚧	🚧	🚧
Face Recognition	🚧	🚧	🚧	🚧	🚧	🚧	🚧

❗Note: All the above features are implemented based on GPU/CPU. PaddleX can also perform fast inference and secondary development on mainstream hardware such as Kunlun, Ascend, Cambricon, and Hygon. The following table details the support status of the model pipeline, and for the specific list of supported models, please refer to Model List (MLU) / Model List (NPU) / Model List (XPU) / Model List DCU. We are adapting more models and promoting the implementation of high-performance and service-oriented deployment on mainstream hardware.

👉 Support for Domestic Hardware Capabilities

pipeline Name	NPU 910B	XPU R200/R300	MLU 370X8	DCU Z100
General OCR	✅	✅	✅	🚧
Table Recognition	✅	🚧	🚧	🚧
General Object Detection	✅	✅	✅	🚧
General Instance Segmentation	✅	🚧	🚧	🚧
General Image Classification	✅	✅	✅	✅
General Semantic Segmentation	✅	✅	✅	✅
Time Series Forecasting	✅	✅	✅	🚧
Time Series Anomaly Detection	✅	🚧	🚧	🚧
Time Series Classification	✅	🚧	🚧	🚧

⏭️ Quick Start

🛠️ Installation

❗Please ensure you have a basic Python runtime environment before installing PaddleX.

Installing PaddlePaddle

# cpu
python -m pip install paddlepaddle==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/

# gpu, this command is only applicable to machines with CUDA version 11.8
python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ 

# gpu, this command is only applicable to machines with CUDA version 12.3
python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu123/

❗For more PaddlePaddle Wheel 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

```bash {'img_path': '/root/.paddlex/predict_input/general_ocr_002.png', 'dt_polys': [[[5, 12], [88, 10], [88, 29], [5, 31]], [[208, 14], [249, 14], [249, 22], [208, 22]], [[695, 15], [824, 15], [824, 60], [695, 60]], [[158, 27], [355, 23], [356, 70], [159, 73]], [[421, 25], [659, 19], [660, 59], [422, 64]], [[337, 104], [460, 102], [460, 127], [337, 129]], [[486, 103], [650, 100], [650, 125], [486, 128]], [[675, 98], [835, 94], [835, 119], [675, 124]], [[64, 114], [192, 110], [192, 131], [64, 134]], [[210, 108], [318, 106], [318, 128], [210, 130]], [[82, 140], [214, 138], [214, 163], [82, 165]], [[226, 136], [328, 136], [328, 161], [226, 161]], [[404, 134], [432, 134], [432, 161], [404, 161]], [[509, 131], [570, 131], [570, 158], [509, 158]], [[730, 138], [771, 138], [771, 154], [730, 154]], [[806, 136], [817, 136], [817, 146], [806, 146]], [[342, 175], [470, 173], [470, 197], [342, 199]], [[486, 173], [616, 171], [616, 196], [486, 198]], [[677, 169], [813, 166], [813, 191], [677, 194]], [[65, 181], [170, 177], [171, 202], [66, 205]], [[96, 208], [171, 205], [172, 230], [97, 232]], [[336, 220], [476, 215], [476, 237], [336, 242]], [[507, 217], [554, 217], [554, 236], [507, 236]], [[87, 229], [204, 227], [204, 251], [87, 254]], [[344, 240], [483, 236], [483, 258], [344, 262]], [[66, 252], [174, 249], [174, 271], [66, 273]], [[75, 279], [264, 272], [265, 297], [76, 303]], [[459, 297], [581, 295], [581, 320], [459, 322]], [[101, 314], [210, 311], [210, 337], [101, 339]], [[68, 344], [165, 340], [166, 365], [69, 368]], [[345, 350], [662, 346], [662, 368], [345, 371]], [[100, 459], [832, 444], [832, 465], [100, 480]]], 'dt_scores': [0.8183103704439653, 0.7609575621092027, 0.8662357274035412, 0.8619508290334809, 0.8495855993183273, 0.8676840017933314, 0.8807986687956436, 0.822308525056085, 0.8686617037621976, 0.8279022169854463, 0.952332847006758, 0.8742692553015098, 0.8477013022907575, 0.8528771493227294, 0.7622965906848765, 0.8492388224448705, 0.8344203789965632, 0.8078477124353284, 0.6300434587457232, 0.8359967356998494, 0.7618617265751318, 0.9481573079350023, 0.8712182945408912, 0.837416955846334, 0.8292475059403851, 0.7860382856406026, 0.7350527486717117, 0.8701022267947695, 0.87172526903969, 0.8779847108088126, 0.7020437651809734, 0.6611684983372949], 'rec_text': ['www.997', '151', 'PASS', '登机牌', 'BOARDING', '舱位 CLASS', '序号SERIALNO.', '座位号SEATNO', '航班 FLIGHT', '日期DATE', 'MU 2379', '03DEC', 'W', '035', 'F', '1', '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GATESCLOSE1OMINUTESBEFOREDEPARTURETIME'], 'rec_score': [0.9617719054222107, 0.4199012815952301, 0.9652514457702637, 0.9978302121162415, 0.9853208661079407, 0.9445787072181702, 0.9714463949203491, 0.9841841459274292, 0.9564052224159241, 0.9959094524383545, 0.9386572241783142, 0.9825271368026733, 0.9356589317321777, 0.9985442161560059, 0.3965512812137604, 0.15236201882362366, 0.9976775050163269, 0.9547433257102966, 0.9974752068519592, 0.9646636843681335, 0.9907559156417847, 0.9895358681678772, 0.9374122023582458, 0.9909093379974365, 0.9796401262283325, 0.9899340271949768, 0.992210865020752, 0.9478569626808167, 0.9982215762138367, 0.9924325942993164, 0.9941263794898987, 0.96443772315979]} ...... ``` The visualization result is as follows: ![alt text](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png)

For other pipelines, just adjust the pipeline parameter to the corresponding name of the pipeline. Below is a list of each pipeline's corresponding parameter name and detailed usage explanation:

👉 More CLI usage and explanations for pipelines

| pipeline Name | Corresponding Parameter | Detailed Explanation | |-------------------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------| | General 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` | | General 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` | | General 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` | | General 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` | | General Image Multilabel 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` | | General OCR | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --device gpu:0` | | General Table Recognition | `paddlex --pipeline table_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --device gpu:0` | | General 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` | | General Time Series Anomaly Detection | `paddlex --pipeline ts_ad --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.cs --device gpu:0` | | General 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
OCR	`OCR`	OCR Pipeline Python Script Usage Instructions
Form Recognition	`table_recognition`	Form 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

* [📦 PaddlePaddle Installation Guide](./docs/installation/paddlepaddle_install_en.md) * [📦 PaddleX Installation Guide](./docs/installation/installation_en.md)

🔥 pipeline Usage

* [📑 PaddleX pipeline Usage Overview](./docs/pipeline_usage/pipeline_develop_guide_en.md) *

📝 Text and Image Intelligent Analysis

* [📄 Document Scene Information Extraction v3 pipeline Usage Guide](./docs/pipeline_usage/tutorials/information_extration_pipelines/document_scene_information_extraction_en.md)

🔍 OCR
- 📜 General OCR pipeline Usage Guide
- 📊 Form Recognition pipeline Usage Guide
🎥 Computer Vision
⏱️ Time Series Analysis
🔧 Related Documentation
- 🖥️ PaddleX pipeline Command Line Usage Guide
- 📝 PaddleX pipeline Python Script Usage Guide

⚙️ Single Function Module Usage

🔍 OCR

* [📝 Text Detection Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/text_detection_en.md) * [🔖 Seal Text Detection Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/seal_text_detection_en.md) * [🔠 Text Recognition Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/text_recognition_en.md) * [🗺️ Layout Area Detection Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/layout_detection_en.md) * [📊 Table Structure Recognition Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/table_structure_recognition_en.md) * [📄 Document Image Orientation Classification Usage Guide](./docs/module_usage/tutorials/ocr_modules/doc_img_orientation_classification_en.md) * [🔧 Document Image Correction Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/text_image_unwarping_en.md) * [📐 Formula Recognition Module Usage Guide](./docs/module_usage/tutorials/ocr_modules/formula_recognition_en.md)

🖼️ Image Classification

🏞️ Image Features
- 🔗 General Image Feature Module Usage Guide
🎯 Object Detection

🏗️ Model pipeline Deployment

* [🚀 PaddleX High-Performance Deployment Guide](./docs/pipeline_deploy/high_performance_deploy_en.md) * [🖥️ PaddleX Service Deployment Guide](./docs/pipeline_deploy/service_deploy_en.md) * [📱 PaddleX Edge Deployment Guide](./docs/pipeline_deploy/lite_deploy_en.md)

🖥️ Multi-Hardware Usage

* [⚙️ Multi-Hardware Usage Guide](./docs/other_devices_support/installation_other_devices_en.md) * [⚙️ DCU Paddle Installation Guide](./docs/other_devices_support/installation_other_devices_en.md) * [⚙️ MLU Paddle Installation Guide](./docs/other_devices_support/installation_other_devices_en.md) * [⚙️ NPU Paddle Installation Guide](./docs/other_devices_support/installation_other_devices_en.md) * [⚙️ XPU Paddle Installation Guide](./docs/other_devices_support/installation_other_devices_en.md)

📝 Tutorials & Examples

* [🖼️ General Image Classification Model Line —— Garbage Classification Tutorial](./docs/practical_tutorials/image_classification_garbage_tutorial_en.md) * [🧩 General Instance Segmentation Model Line —— Remote Sensing Image Instance Segmentation Tutorial](./docs/practical_tutorials/instance_segmentation_remote_sensing_tutorial_en.md) * [👥 General Object Detection Model Line —— Pedestrian Fall Detection Tutorial](./docs/practical_tutorials/object_detection_fall_tutorial_en.md) * [👗 General Object Detection Model Line —— Fashion Element Detection Tutorial](./docs/practical_tutorials/object_detection_fashion_pedia_tutorial_en.md) * [🚗 General OCR Model Line —— License Plate Recognition Tutorial](./docs/practical_tutorials/ocr_det_license_tutorial_en.md) * [✍️ General OCR Model Line —— Handwritten Chinese Character Recognition Tutorial](./docs/practical_tutorials/ocr_rec_chinese_tutorial_en.md) * [🗣️ General Semantic Segmentation Model Line —— Road Line Segmentation Tutorial](./docs/practical_tutorials/semantic_segmentation_road_tutorial_en.md) * [🛠️ Time Series Anomaly Detection Model Line —— Equipment Anomaly Detection Application Tutorial](./docs/practical_tutorials/ts_anomaly_detection_en.md) * [🎢 Time Series Classification Model Line —— Heartbeat Monitoring Time Series Data Classification Application Tutorial](./docs/practical_tutorials/ts_classification_en.md) * [🔋 Time Series Forecasting Model Line —— Long-term Electricity Consumption Forecasting Application Tutorial](./docs/practical_tutorials/ts_forecast_en.md)

🤔 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.

README_en.md 40 KB History Raw