Startsida Utforska Hjälp
Registrera dig Logga in
zhengchun
/
PaddleX
1
0
Fork 0
Filer Ärenden 0 Pull-förfrågningar 0 Wiki
Träd: 55be4b4f43
Grenar Taggar
PaddleX
/
README_en.md

README_en.md 66 KB
Historik Rå

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.

PaddleX

🌟 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 33 pipelines. Experience the model effects quickly through easy Python API calls. Also supports 39 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, serving, 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

🔥🔥 2025.5.20: PaddleX v3.0.0 Released

Core upgrades are as follows:

  • Major Capability Releases:

    • Launch of the groundbreaking text recognition model PP-OCRv5: Achieves a 13% improvement in OCR accuracy across all scenarios. A single model now supports 5 types of text (Simplified Chinese, Traditional Chinese, Chinese Pinyin, English, and Japanese), with significant enhancements in recognizing handwritten fonts, vertical text, and rare characters in both Chinese and English. You can experience it immediately in the online demo.

    • Launch of the groundbreaking document parsing solution PP-StructureV3: Enhanced capabilities in layout area detection, table recognition, Chinese and English formula recognition, and restoration of multi-column reading order, with added abilities for chart understanding. PP-StructureV3 achieves state-of-the-art (SOTA) levels in both Chinese and English editing distances on the OmniDocBench leaderboard. Experience it in the online demo.

    • Optimization of PP-ChatOCRv4: Supports the Ernie 4.5T. Combined with PP-DocBee2, it shows a 15.7 percentage point improvement in key information extraction accuracy compared to the previous generation. Experience it in the online demo.

  • Inference Capability Optimization:

    • The general OCR, PP-StructureV3, formula recognition, seal text recognition, and document image preprocessing pipelines support setting batch size >1, allowing multiple pages to be processed at once.

    • 17 pipelines, including general OCR and PP-StructureV3, now support multi-GPU parallel inference. Sample code for multi-process parallel inference has been added.

🔥 2025.4.22, PaddleX v3.0.0rc1 major upgrade. This version fully adapts to PaddlePaddle 3.0.0, with the following core upgrades:

  • Adapts to New Features of PaddlePaddle 3.0: Supports compiler training, which can be enabled by appending -o Global.dy2st=True to the training command. On GPUs, the training speed of most models can be improved by over 10%, and for a few models, the improvement can exceed 30%. For inference, the models are fully adapted to PaddlePaddle 3.0's Intermediate Representation (PIR) technology, offering more flexible extensibility and compatibility. The file names for inference model have been changed from xxx.pdmodel to xxx.json.
  • Newly Added Self-developed MLLM for Document Image Understanding, PP-DocBee: PP-DocBee has achieved SOTA performance among models with similar parameter sizes on academic and internal business scenario document understanding evaluation benchmarks. It can be applied to document QA scenarios such as financial reports, research reports, contracts, manuals, and legal regulations.
  • Full Support for ONNX Format Models, with Support for Model Format Conversion via the Paddle2ONNX Plugin.

  • Enhanced High-Performance Inference:

    • Added Support for ONNX and OM Format Models: PaddleX can intelligently select the model format based on needs;
    • Expanded Supported Pipelines and Modules: All single modules and pipelines for inference model can use the high-performance inference plugin to improve inference performance;
    • Support for 3 Configuration Methods: CLI, API, and Configuration Files: Enables more granular configuration, allowing users to enable and disable the high-performance inference plugin at the sub-pipeline and sub-module level.

  • Expanded Multi-Hardware Support:

    • NPU: The number of models fully validated on Ascend NPU has increased to 200. Additionally, common pipelines such as general OCR, image classification, and object detection support OM model format inference, with inference speed improvements ranging from 113.8% to 226.4%. Inference deployment is supported on Atlas 200 and Atlas 300 series products.
    • GCU: Enflame has been officially integrated into the PaddlePaddle regular release system, completing the adaptation of the PaddleX ecosystem. Supports the training and inference of 90 models.

🔠 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 / 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 Serving Edge Deployment Custom Development Zero-Code Development On AI Studio
OCR Link ✅ ✅ ✅ ✅ ✅ ✅
PP-ChatOCRv3 Link ✅ ✅ ✅ 🚧 ✅ ✅
PP-ChatOCRv4 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 Link ✅ ✅ ✅ 🚧 ✅ ✅
Multi-label Image Classification Link ✅ ✅ ✅ 🚧 ✅ ✅
Pedestrian Attribute Recognition Link ✅ ✅ ✅ 🚧 ✅ ✅
Vehicle Attribute Recognition Link ✅ ✅ ✅ 🚧 ✅ ✅
Formula Recognition Link ✅ ✅ ✅ 🚧 ✅ ✅
Seal Recognition Link ✅ ✅ ✅ 🚧 ✅ ✅
Image Anomaly Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Human Keypoint Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Open Vocabulary Detection 🚧 ✅ ✅ ✅ 🚧 🚧 🚧
Open Vocabulary Segmentation 🚧 ✅ ✅ ✅ 🚧 🚧 🚧
Rotated Object Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
3D Bev Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Table Recognition v2 Link ✅ ✅ ✅ 🚧 ✅ ✅
Layout Parsing 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
PP-StructureV3 Link ✅ ✅ ✅ 🚧 🚧 ✅
Document Image Preprocessing 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Image Recognition 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Face Recognition 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Multilingual Speech Recognition 🚧 ✅ 🚧 ✅ 🚧 🚧 🚧
Video Classification 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Video Detection 🚧 ✅ ✅ ✅ 🚧 ✅ 🚧
Document Understanding 🚧 ✅ 🚧 ✅ 🚧 🚧 🚧

❗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 HYGON. 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 (HYGON DCU). We are continuously adapting more models and promoting the implementation of high-performance and serving on mainstream hardware.

🔥🔥 Support for Domestic Hardware Capabilities

Pipeline Ascend 910B Kunlunxin R200/R300 Cambricon MLU370X8 HYGON Z100
OCR ✅ ✅ ✅ 🚧
Table Recognition ✅ 🚧 🚧 🚧
Object Detection ✅ ✅ ✅ 🚧
Instance Segmentation ✅ 🚧 ✅ 🚧
Image Classification ✅ ✅ ✅ ✅
Semantic Segmentation ✅ ✅ ✅ ✅
Time Series Forecasting ✅ ✅ ✅ 🚧
Time Series Anomaly Detection ✅ 🚧 🚧 🚧
Time Series Classification ✅ 🚧 🚧 🚧
Multi-label Image Classification ✅ 🚧 🚧 ✅
Pedestrian Attribute Recognition ✅ 🚧 🚧 🚧
Vehicle Attribute Recognition ✅ 🚧 🚧 🚧
Image Recognition ✅ 🚧 ✅ ✅
Seal Recognition ✅ 🚧 🚧 🚧
Image Anomaly Detection ✅ ✅ ✅ ✅
Face Recognition ✅ ✅ ✅ ✅

⏭️ Quick Start

🛠️ Installation

❗Before installing PaddleX, please ensure that you have a basic Python runtime environment (Note: Currently supports Python 3.8 to Python 3.12). The PaddleX 3.0-rc1 version depends on PaddlePaddle version 3.0.0 and above. Please make sure the version compatibility is maintained before use.

  • Installing PaddlePaddle

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

# gpu，requires GPU driver version ≥450.80.02 (Linux) or ≥452.39 (Windows)
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/

# gpu，requires GPU driver version ≥550.54.14 (Linux) or ≥550.54.14 (Windows)
python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/

❗No need to focus on the CUDA version on the physical machine, only the GPU driver version needs attention. For more information on PaddlePaddle Wheel versions, please refer to the PaddlePaddle Official Website.

  • Installing PaddleX

    pip install paddlex==3.0.0[base]
# You can also install the sub-package for specific pipeline, such as:
# pip install paddlex==3.0.0[ocr]

❗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]

Each Pipeline in PaddleX corresponds to specific parameters, which you can view in the respective Pipeline documentation for detailed explanations. Each Pipeline requires specifying three necessary parameters:

  • pipeline: The name of the Pipeline or the configuration file of the Pipeline
  • input: The local path, directory, or URL of the input file (e.g., an image) to be processed
  • device: The hardware device and its index to use (e.g., gpu:0 indicates using the 0th GPU), or you can choose to use NPU (npu:0), XPU (xpu:0), CPU (cpu), etc.

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 \
        --use_doc_orientation_classify False \
        --use_doc_unwarping False \
        --use_textline_orientation False \
        --save_path ./output \
        --device gpu:0
👉 Click to view the running result ```bash {'res': {'input_path': 'general_ocr_002.png', 'page_index': None, 'model_settings': {'use_doc_preprocessor': False, 'use_textline_orientation': False}, 'doc_preprocessor_res': {'input_path': None, 'model_settings': {'use_doc_orientation_classify': True, 'use_doc_unwarping': False}, 'angle': 0},'dt_polys': [array([[ 3, 10], [82, 10], [82, 33], [ 3, 33]], dtype=int16), ...], 'text_det_params': {'limit_side_len': 960, 'limit_type': 'max', 'thresh': 0.3, 'box_thresh': 0.6, 'unclip_ratio': 2.0}, 'text_type': 'general', 'textline_orientation_angles': [-1, ...], 'text_rec_score_thresh': 0.0, 'rec_texts': ['www.99*', ...], 'rec_scores': [0.8980069160461426, ...], 'rec_polys': [array([[ 3, 10], [82, 10], [82, 33], [ 3, 33]], dtype=int16), ...], 'rec_boxes': array([[ 3, 10, 82, 33], ...], dtype=int16)}} ``` The visualization result is as follows: ![alt text](https://raw.githubusercontent.com/cuicheng01/PaddleX_doc_images/main/images/boardingpass.png)

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

👉 More CLI usage for pipelines | Pipeline Name | Command | |------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 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 --save_path ./output/` | | General Object Detection | `paddlex --pipeline object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_object_detection_002.png --threshold 0.5 --save_path ./output/ --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 --threshold 0.5 --save_path ./output --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 --target_size -1 --save_path ./output --device gpu:0` | | Image Multi-label Classification | `paddlex --pipeline image_multilabel_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg --save_path ./output --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 --threshold 0.5 --save_path ./output --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 --save_path ./output --device gpu:0` | | Pedestrian Attribute Recognition | `paddlex --pipeline pedestrian_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pedestrian_attribute_002.jpg --save_path ./output/ --device gpu:0` | | Vehicle Attribute Recognition | `paddlex --pipeline vehicle_attribute_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/vehicle_attribute_002.jpg --save_path ./output/ --device gpu:0` | | 3D Multi-modal Fusion Detection | `paddlex --pipeline 3d_bev_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/det_3d/demo_det_3d/nuscenes_demo_infer.tar --device gpu:0 --save_path ./output/` | | Human Keypoint Detection | `paddlex --pipeline human_keypoint_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/keypoint_detection_001.jpg --det_threshold 0.5 --save_path ./output/ --device gpu:0` | | Open Vocabulary Detection | `paddlex --pipeline open_vocabulary_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_detection.jpg --prompt "bus . walking man . rearview mirror ." --thresholds "{'text_threshold': 0.25, 'box_threshold': 0.3}" --save_path ./output --device gpu:0` | | Open Vocabulary Segmentation | `paddlex --pipeline open_vocabulary_segmentation --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/open_vocabulary_segmentation.jpg --prompt_type box --prompt "[[112.9,118.4,513.8,382.1],[4.6,263.6,92.2,336.6],[592.4,260.9,607.2,294.2]]" --save_path ./output --device gpu:0` | | Rotated Object Detection | `paddlex --pipeline rotated_object_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png --threshold 0.5 --save_path ./output --device gpu:0` | | General OCR | `paddlex --pipeline OCR --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_002.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` | | Document Image Preprocessor | `paddlex --pipeline doc_preprocessor --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/doc_test_rotated.jpg --use_doc_orientation_classify True --use_doc_unwarping True --save_path ./output --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 --save_path ./output --device gpu:0` | | General Table Recognition v2 | `paddlex --pipeline table_recognition_v2 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/table_recognition.jpg --save_path ./output --device gpu:0` | | General Layout Parsing | `paddlex --pipeline layout_parsing --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/demo_paper.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` | | General Layout Parsing v2 | `paddlex --pipeline PP-StrucutrV3 --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_structure_v3_demo.png --use_doc_orientation_classify False --use_doc_unwarping False --use_textline_orientation False --save_path ./output --device gpu:0` | | Formula Recognition | `paddlex --pipeline formula_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/demo_image/general_formula_recognition.png --use_layout_detection True --use_doc_orientation_classify False --use_doc_unwarping False --layout_threshold 0.5 --layout_nms True --layout_unclip_ratio 1.0 --layout_merge_bboxes_mode large --save_path ./output --device gpu:0` | | Seal Text Recognition | `paddlex --pipeline seal_recognition --input https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/seal_text_det.png --use_doc_orientation_classify False --use_doc_unwarping False --device gpu:0 --save_path ./output` | | Time Series Forecasting | `paddlex --pipeline ts_forecast --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_fc.csv --device gpu:0 --save_path ./output` | | Time Series Anomaly Detection | `paddlex --pipeline ts_anomaly_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_ad.csv --device gpu:0 --save_path ./output` | | Time Series Classification | `paddlex --pipeline ts_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/ts/demo_ts/ts_cls.csv --device gpu:0 --save_path ./output` | | Multilingual Speech Recognition | `paddlex --pipeline multilingual_speech_recognition --input https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav --save_path ./output --device gpu:0` | | General Video Classification | `paddlex --pipeline video_classification --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/general_video_classification_001.mp4 --topk 5 --save_path ./output --device gpu:0` | | General Video Detection | `paddlex --pipeline video_detection --input https://paddle-model-ecology.bj.bcebos.com/paddlex/videos/demo_video/HorseRiding.avi --device gpu:0 --save_path ./output` |

📝 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

To use the Python script for other pipelines, simply adjust the pipeline parameter in the create_pipeline() method to the name of the corresponding pipeline and modify the parameters accordingly. Below are the parameter names and detailed usage explanations for each pipeline:

👉 More Python script usage for pipelines | pipeline Name | Corresponding Parameter | Detailed Explanation | |-------------------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------| | PP-ChatOCRv4-doc | `PP-ChatOCRv4-doc` | [PP-ChatOCRv4-doc Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v4.html) | | PP-ChatOCRv3-doc | `PP-ChatOCRv3-doc` | [PP-ChatOCRv3-doc Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction_v3.html) | | Image Classification | `image_classification` | [ Image Classification Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_classification.html) | | Object Detection | `object_detection` | [ Object Detection Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/object_detection.html) | | Instance Segmentation | `instance_segmentation` | [ Instance Segmentation Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/instance_segmentation.html) | | Semantic Segmentation | `semantic_segmentation` | [ Semantic Segmentation Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/semantic_segmentation.html) | | Image Multi-Label Classification | `multilabel_classification` | [ Image Multi-Label Classification Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_multi_label_classification.html) | | Small Object Detection | `small_object_detection` | [Small Object Detection Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/small_object_detection.html) | | Image Anomaly Detection | `image_classification` | [Image Anomaly Detection Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/image_anomaly_detection.html) | | Image Recognition | `PP-ShiTuV2` | [Image Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/general_image_recognition.html) | | Face Recognition | `face_recognition` | [Face Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/face_recognition.html) | | Pedestrian Attribute Recognition | `pedestrian_attribute` | [Pedestrian Attribute Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/pedestrian_attribute_recognition.html) | |Vehicle Attribute Recognition | `vehicle_attribute` | [Vehicle Attribute Recognition Pipeline Python Script Usage Instructions](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/vehicle_attribute_recognition.html) | | 3D Multi-modal Fusion Detection | `3d_bev_detection` | [Instructions for Using the 3D Multi-modal Fusion Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/3d_bev_detection.html#222-python-script-integration) | | Human Keypoint Detection | `human_keypoint_detection` | [Instructions for Using the Human Keypoint Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/human_keypoint_detection.html#222-python-script-integration) | | Open Vocabulary Detection | `open_vocabulary_detection` | [Instructions for Using the Open Vocabulary Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_detection.html#212-python-script-integration) | | Open Vocabulary Segmentation | `open_vocabulary_segmentation` | [Instructions for Using the Open Vocabulary Segmentation Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/open_vocabulary_segmentation.html#212-python-script-integration) | | Rotated Object Detection | `rotated_object_detection` | [Instructions for Using the Rotated Object Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/cv_pipelines/rotated_object_detection.html#212-python-script-integration) | | OCR | `OCR` | [Instructions for Using the General OCR Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/OCR.html#222-python-script-integration) | | Document Image Preprocessing | `doc_preprocessor` | [Instructions for Using the Document Image Preprocessing Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/doc_preprocessor.html#212-python-script-integration) | | General Table Recognition | `table_recognition` | [Instructions for Using the General Table Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition.html#22-python-script-integration) | | General Table Recognition v2 | `table_recognition_v2` | [Instructions for Using the General Table Recognition v2 Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/table_recognition_v2.html#22-python-script-integration) | | General Layout Parsing | `layout_parsing` | [Instructions for Using the General Layout Parsing Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/layout_parsing.html#22-python-script-integration) | | PP-StructureV3 | `PP-StructureV3` | [Instructions for Using the General Layout Parsing v2 Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/PP-StructureV3.html#22-python-script-integration) | | Formula Recognition | `formula_recognition` | [Instructions for Using the Formula Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/formula_recognition.html#22-python-script-integration) | | Seal Text Recognition | `seal_recognition` | [Instructions for Using the Seal Text Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/ocr_pipelines/seal_recognition.html#22-python-script-integration) | | Time Series Forecasting | `ts_forecast` | [Instructions for Using the Time Series Forecasting Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_forecasting.html#222-python-script-integration) | | Time Series Anomaly Detection | `ts_anomaly_detection` | [Instructions for Using the Time Series Anomaly Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_anomaly_detection.html#222-python-script-integration) | | Time Series Classification | `ts_classification` | [Instructions for Using the Time Series Classification Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/time_series_classification.html#222-python-script-integration) | | Multilingual Speech Recognition | `multilingual_speech_recognition` | [Instructions for Using the Multilingual Speech Recognition Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/multilingual_speech_recognition.html#212-python-script-integration) | | General Video Classification | `video_classification` | [Instructions for Using the General Video Classification Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/video_classification.html#22-python-script-integration) | | General Video Detection | `video_detection` | [Instructions for Using the General Video Detection Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/time_series_pipelines/video_detection.html#212-python-script-integration) | | Document Understanding | `doc_understanding` | [Instructions for Using the Document Understanding Pipeline Python Script](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/vlm_pipelines/doc_understanding.html#211-python-script-integration) |

📖 Documentation

⬇️ Installation * [📦 PaddlePaddle Installation](https://paddlepaddle.github.io/PaddleX/latest/en/installation/paddlepaddle_install.html) * [📦 PaddleX Installation](https://paddlepaddle.github.io/PaddleX/latest/en/installation/installation.html)
🔥 Pipeline Usage * [📑 PaddleX Pipeline Usage Overview](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/pipeline_develop_guide.html) *
📝 Information Extraction * [📄 PP-ChatOCRv3 Pipeline Tutorial](https://paddlepaddle.github.io/PaddleX/latest/en/pipeline_usage/tutorials/information_extraction_pipelines/document_scene_information_extraction.html)