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# PaddleX Pipelines (DCU)

## 1. Basic Pipelines

<table>
  <tr>
    <th width="10%">Pipeline Name</th>
    <th width="10%">Pipeline Modules</th>
    <th width="10%">Baidu AIStudio Community Experience URL</th>
    <th width="50%">Pipeline Introduction</th>
    <th width="20%">Applicable Scenarios</th>
  </tr>
  <tr>
    <td>General Image Classification</td>
    <td>Image Classification</td>
    <td><a href="https://aistudio.baidu.com/community/app/100061/webUI">Online Experience</a></td>
    <td>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.</td>
    <td>
      <ul>
        <li>Automatic classification and recognition of product images</li>
        <li>Real-time monitoring of defective products on pipelines</li>
        <li>Personnel recognition in security surveillance</li>
      </ul>
    </td>
  </tr>
  <tr>
    <td>General Semantic Segmentation</td>
    <td>Semantic Segmentation</td>
    <td><a href="https://aistudio.baidu.com/community/app/100062/webUI?source=appCenter">Online Experience</a></td>
    <td>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.</td>
    <td>
      <ul>
        <li>Analysis of satellite images in Geographic Information Systems</li>
        <li>Segmentation of obstacles and passable areas in robot vision</li>
        <li>Separation of foreground and background in film production</li>
      </ul>
    </td>
  </tr>
</table>

## 2. Featured Pipelines
Not supported yet, please stay tuned!
