Animal pattern recognition from nighttime grayscale images is crucial for wildlife protection and ecological monitoring. Most of the current models suffer from a large parameter scale, making them unsuitable for deployment in resource-constrained environments. To address this challenge, this study proposes a multi-layer knowledge distillation approach based on the teacher Convnext model to improve the lightweight student model's classification performance effectively. Experimental results show that the parameters of the distilled CifarResNet20 model are only 0.27M, and the accuracy is 88.76%, which is superior to the traditional single-layer distillation and another tiny student model. The study confirms the efficiency and practical value of the proposed method in practical applications such as ecological monitoring.

ABSTRACT
1. Introduction
2. Related Work
3. Materials and Methods
    3.1 Datasets
    3.2 ConvNeXt Model
    3.3 Distillation scheme design
4. Result
    4.1 Comparison of results of different distillation schemes
    4.2 Accuracy vs. Parameters
5. Conclusion
참고문헌
<저자소개>

• Dae-Won Park(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 박대원
• Changyu-AO(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 아오창유 Corresponding author
• Seung-Eon Jeong(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 정승언
• Soo-Kyung Moon(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 문수경
• Youn-Mo Soung(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 성윤모
• Man-Sung Kwen(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 권만성
• Uk Cho(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 조욱
• Dae-In Kang(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 강대인
• Sung-Ho Jung(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 정성호
• Gwang-Jun Kim(Department of Computer Engineering, Chonnam National University, Yeosu, Seoul, Korea) | 김광준