Food analysis is becoming a hot topic in health area, in which fine-grained food recognition task plays an important role. In this paper, we describe the details of our solution to the LargeFineFoodAI-ICCV Workshop-Recognition challenge held on Kaggle. We find a proper combination of Arcface loss[1] and Circle loss[9] can bring improvement to the performance. With Arcface and the combined loss, model was trained with carefully tuned configurations and ensembled to get the final results. Our solution won the 3rd place in the competition.
@article{arxiv.2510.21199,
title = {3rd Place Solution to Large-scale Fine-grained Food Recognition},
author = {Yang Zhong and Yifan Yao and Tong Luo and Youcai Zhang and Yaqian Li},
journal= {arXiv preprint arXiv:2510.21199},
year = {2025}
}