3rd Place Solution to Large-scale Fine-grained Food Recognition
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 24 Oct 2025.
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References · 14
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2 more
- 2021Large Scale Visual Food Recognitioncited by 75
- 2021Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classificationcited by 16
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