Earticle

초록

영어

The performance of a deep learning model significantly improves on challenging datasets when using transfer learning. However, the pre-trained networks have certain constraints in terms of their architecture. For example, the available pre-trained models are trained for a specific input size. Therefore, require resizing the input images of different sizes. When training a model from scratch, higher resolution image offers better performance. However, our study has shown that this is not true when using pre-trained models. We have compared the pre-trained MobileNetV2 performance on CIFAR10 and CIFAR100 datasets. The pre-trained weights of MobileNetV2 are available for image resolutions of 92x92, 128x128, 160x160, 192x192 and 224x224. The performance of the model is evaluated in terms of classification accuracy. Our analysis have shown that for image resolution of 160x160, the pre-trained model has achieved better classification accuracy.

목차

Abstract
I. Introduction
II. Methods
III. RESULTS AND DISCUSSION
IV. CONCLUSION AND FUTURE WORK
Acknowledgment
REFERENCES

키워드

저자

• Ijaz Ahmad [ Department of Computer Engineering, Chosun University, Gwangju, 61452 South Korea ]
• Seokjoo Shin [ Department of Computer Engineering, Chosun University, Gwangju, 61452 South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004