融合Arcface与Circle损失，提升细粒度食物识别准确率
3rd Place Solution to Large-scale Fine-grained Food Recognition
- 采用Arcface与Circle损失的组合策略优化特征表示
- 通过调优训练配置与模型集成，实现竞赛第三名成绩
- 适合对细粒度图像分类和损失函数设计感兴趣的读者
食物分析正成为健康领域的重要课题，其中细粒度食物识别任务尤为关键。本文详细描述了我们在Kaggle举办的LargeFineFoodAI-ICCV Workshop-Recognition挑战赛中获得第三名的解决方案。研究发现，合理结合Arcface损失[1]与Circle损失[9]可有效提升模型性能。通过精心调整训练配置并进行模型集成，最终取得优异结果。该方法在细粒度食物识别任务上展现出较强泛化能力。
原文摘要 · Abstract (English)
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.
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