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Poster Session I : Next Generation Computing Applications I

Development of dog breed classification technology using YOLOv8 model.

원문정보

Jeonghyun Choi, Daeyong Kim, Chinchol Kim, Seungwoo Choi, Jaeyoo Lee, Seongjoon Yoo

한국차세대컴퓨팅학회 한국차세대컴퓨팅학회 학술대회 The 10th International Conference on Next Generation Computing 2024 2024.11 pp.113-116
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초록

영어

In this study, we analyzed animal registration data to identify the most popular dog breeds raised in South Korea. And then, a dataset was collected for the identified dog breeds and used to perform transfer learning on the YOLOv8 model to develop a breed classification model, and the classification accuracy was measured for each dog breed. The accuracy of classifying dog breeds by breed was confirmed to be at least 84% and up to 100%.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
A. Dog Breed Classification Dataset
B. Related works
C. YOLOv8
III. METHOD
A. Data Preparation
B. Collect data
C. Refine and process data
IV. TRAINING MODEL
A. YOLO model and hyperparameter
B. Results
V. IMPLEMENTATION
VI. CONCLUSION
A. Analysis result
B. Future work
ACKNOWLEDGMENT
REFERENCES

키워드

저자정보

  • Jeonghyun Choi AI Convergence Research Center Sejong University
  • Daeyong Kim The Little Cat The Little Cat Seoul, Republic of Korea
  • Chinchol Kim AI Convergence Research Center Sejong University
  • Seungwoo Choi AI Convergence Research Center Sejong University
  • Jaeyoo Lee AI Convergence Research Center Sejong University
  • Seongjoon Yoo AI Convergence Research Center Sejong University

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자료제공 : 네이버학술정보

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