PaddleX Pipelines (NPU)

1. Basic Pipelines

Pipeline Name	Pipeline Modules	Baidu AIStudio Community Experience URL	Pipeline Introduction	Applicable Scenarios
General Image Classification	Image Classification	Online Experience	Image classification is a technique that assigns images to predefined categories. It is widely used in object recognition, scene understanding, and automatic annotation. Image classification can identify various objects such as animals, plants, traffic signs, etc., and categorize them based on their features. By leveraging deep learning models, image classification can automatically extract image features and perform accurate classification. The General Image Classification Pipeline is designed to solve image classification tasks for given images.	Automatic classification and recognition of product images Real-time monitoring of defective products on production lines Personnel recognition in security surveillance
General Object Detection	Object Detection	Online Experience	Object detection aims to identify the categories and locations of multiple objects in images or videos by generating bounding boxes to mark these objects. Unlike simple image classification, object detection not only recognizes what objects are in the image, such as people, cars, and animals, but also accurately determines the specific location of each object, usually represented by a rectangular box. This technology is widely used in autonomous driving, surveillance systems, and smart photo albums, relying on deep learning models (e.g., YOLO, Faster R-CNN) that efficiently extract features and perform real-time detection, significantly enhancing the computer's ability to understand image content.	Tracking moving objects in video surveillance Vehicle detection in autonomous driving Defect detection in industrial manufacturing Shelf product detection in retail
General Semantic Segmentation	Semantic Segmentation	Online Experience	Semantic segmentation is a computer vision technique that assigns each pixel in an image to a specific category, enabling detailed understanding of image content. Semantic segmentation not only identifies the types of objects in an image but also classifies each pixel, allowing entire regions of the same category to be marked. For example, in a street scene image, semantic segmentation can distinguish pedestrians, cars, sky, and roads at the pixel level, forming a detailed label map. This technology is widely used in autonomous driving, medical image analysis, and human-computer interaction, often relying on deep learning models (e.g., FCN, U-Net) that use Convolutional Neural Networks (CNNs) to extract features and achieve high-precision pixel-level classification, providing a foundation for further intelligent analysis.	Analysis of satellite images in Geographic Information Systems Segmentation of obstacles and passable areas in robot vision Separation of foreground and background in film production
General Instance Segmentation	Instance Segmentation	Online Experience	Instance segmentation is a computer vision task that identifies object categories in images and distinguishes the pixels of different instances within the same category, enabling precise segmentation of each object. Instance segmentation can separately mark each car, person, or animal in an image, ensuring they are processed independently at the pixel level. For example, in a street scene image with multiple cars and pedestrians, instance segmentation can clearly separate the contours of each car and person, forming multiple independent region labels. This technology is widely used in autonomous driving, video surveillance, and robot vision, often relying on deep learning models (e.g., Mask R-CNN) that use CNNs for efficient pixel classification and instance differentiation, providing powerful support for understanding complex scenes.	Crowd counting in malls Counting crops or fruits in agricultural intelligence Selecting and segmenting specific objects in image editing
General OCR	Text Detection	Online Experience	OCR (Optical Character Recognition) is a technology that converts text in images into editable text. It is widely used in document digitization, information extraction, and data processing. OCR can recognize printed text, handwritten text, and even certain types of fonts and symbols. The General OCR Pipeline is designed to solve text recognition tasks, extracting text information from images and outputting it in text form. PP-OCRv4 is an end-to-end OCR system that achieves millisecond-level text content prediction on CPUs, achieving state-of-the-art (SOTA) performance in general scenarios. Based on this project, developers from academia, industry, and research have quickly implemented various OCR applications covering general, manufacturing, finance, transportation.	Document digitization Information extraction Data processing
	Text Recognition
General Table Recognition	Layout Detection	Online Experience	Table recognition is a technology that automatically identifies and extracts table content and its structure from documents or images. It is widely used in data entry, information retrieval, and document analysis. By leveraging computer vision and machine learning algorithms, table recognition can convert complex table information into editable formats, facilitating further data processing and analysis by users	Processing of bank statements recognition and extraction of various indicators in medical reports extraction of tabular information from contracts
	Table Structure Recognition
	Text Detection
	Text Recognition
Time Series Forecasting	Time Series Forecasting Module	Online Experience	Time series forecasting is a technique that utilizes historical data to predict future trends by analyzing patterns in time series data. It is widely applied in financial markets, weather forecasting, and sales prediction. Time series forecasting typically employs statistical methods or deep learning models (such as LSTM, ARIMA, etc.), which can handle time dependencies in data to provide accurate predictions, assisting decision-makers in better planning and response. This technology plays a crucial role in many industries, including energy management, supply chain optimization, and market analysis	Stock prediction climate forecasting disease spread prediction energy demand forecasting traffic flow prediction product lifecycle prediction electric load forecasting
Time Series Anomaly Detection	Time Series Anomaly Detection Module	Online Experience	Time series anomaly detection is a technique that identifies abnormal patterns or behaviors in time series data. It is widely used in network security, device monitoring, and financial fraud detection. By analyzing normal trends and patterns in historical data, it discovers events that significantly differ from expected behaviors, such as sudden increases in network traffic or unusual transaction activities. Time series anomaly detection often employs statistical methods or machine learning algorithms (like Isolation Forest, LSTM, etc.), which can automatically identify anomalies in data, providing real-time alerts to enterprises and organizations to help promptly address potential risks and issues. This technology plays a vital role in ensuring system stability and security	Financial fraud detection network intrusion detection equipment failure detection industrial production anomaly detection stock market anomaly detection power system anomaly detection
Time Series Classification	Time Series Classification Module	Online Experience	Time series classification is a technique that categorizes time series data into predefined classes. It is widely applied in behavior recognition, speech recognition, and financial trend analysis. By analyzing features that vary over time, it identifies different patterns or events, such as classifying a speech signal as "greeting" or "request" or dividing stock price movements into "rising" or "falling." Time series classification typically utilizes machine learning and deep learning models, effectively capturing time dependencies and variation patterns to provide accurate classification labels for data. This technology plays a key role in intelligent monitoring, voice assistants, and market forecasting applications	Electrocardiogram Classification Stock Market Behavior Classification Electroencephalogram Classification Emotion Classification Traffic Condition Classification Network Traffic Classification Equipment Operating Condition Classification

2. Featured Pipelines

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