{"id":36461,"date":"2024-11-01T09:48:40","date_gmt":"2024-11-01T09:48:40","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36461"},"modified":"2024-11-01T11:53:05","modified_gmt":"2024-11-01T11:53:05","slug":"deep-learning-pytorch-course-r-cnn","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36461\/","title":{"rendered":"Deep Learning PyTorch Course, R-CNN"},"content":{"rendered":"

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As deep learning has established itself as a significant field of artificial intelligence, object detection technology is also receiving considerable attention. Among these, Region-based Convolutional Neural Networks (R-CNN) is regarded as an innovative approach to object detection. In this course, we will explore the concept of R-CNN, its working principle, and how to implement it using PyTorch.<\/p>\n

1. Overview of R-CNN<\/h2>\n

R-CNN is a model proposed by Ross Girshick in 2014, focusing on recognizing objects in images and accurately finding their boundaries. Compared to traditional methods that perform recognition based on the entire image, R-CNN uses a selective approach to examine specific regions, enhancing efficiency.<\/p>\n

1.1 Structure of R-CNN<\/h3>\n

R-CNN consists of three main steps:<\/p>\n

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  1. Region Proposal:<\/strong> It generates candidate regions (Region Proposals) that can identify the location of objects in the image. In this step, algorithms like Selective Search are used to extract hundreds of candidate regions.<\/li>\n
  2. Feature Extraction:<\/strong> For each candidate region, features are extracted using a Convolutional Neural Network (CNN). This is used to recognize what object each candidate region contains.<\/li>\n
  3. Classification & Bounding Box Regression:<\/strong> Finally, classification is performed for each candidate region, and the bounding boxes are adjusted to accurately set the boundaries of the objects.<\/li>\n<\/ol>\n

    1.2 Advantages of R-CNN<\/h3>\n

    The main advantages of R-CNN include:<\/p>\n