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PaddleX Pipelines (MLU)

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 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
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

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