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The nature of data has changed significantly as a result of the quick development of technology. Text-based datasets have given way to visual data, such as photos and videos. This shift necessitates the development of cutting-edge technologies that can effectively process and analyze visual input, allowing the creation of intelligent systems that can precisely extract insightful information. Convolutional neural network (CNN) models that have already been trained are now essential resources for this project. In this paper, the effectiveness of three well-known CNN models—AlexNet, GoogleNet, and SqueezeNet—in picture classification tasks is thoroughly compared. Our assessment concentrates on object detection performance on Dog’s dataset obtained from the Stanford Dogs Breed dataset, offering important information about the advantages and disadvantages of each model.

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Pre-trained CNN Models Based Dog Video Summarization: A Comparative Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Tejas Chauhan,
  • Bhagyesha Pandhi,
  • Krishna Jariwala,
  • Mitesh Patel,
  • Divya Kavathiya

摘要

The nature of data has changed significantly as a result of the quick development of technology. Text-based datasets have given way to visual data, such as photos and videos. This shift necessitates the development of cutting-edge technologies that can effectively process and analyze visual input, allowing the creation of intelligent systems that can precisely extract insightful information. Convolutional neural network (CNN) models that have already been trained are now essential resources for this project. In this paper, the effectiveness of three well-known CNN models—AlexNet, GoogleNet, and SqueezeNet—in picture classification tasks is thoroughly compared. Our assessment concentrates on object detection performance on Dog’s dataset obtained from the Stanford Dogs Breed dataset, offering important information about the advantages and disadvantages of each model.