A study on the generalization of DINOv2 features for food recognition tasks: A unified evaluation framework

Self-supervised learning has recently gained increasing attention in computer vision, enabling the extraction of rich and general-purpose feature representations without requiring large annotated datasets. In this paper we aim to build a unified approach capable of deploying robust and effective analysis systems, replacing the need for multiple task-specific models trained end-to-end. Rather than introducing new architectures or training strategies, our goal is to systematically assess whether a single frozen self-supervised representation can support heterogeneous food-related tasks under realistic operating conditions. To this end, we performed an extensive analysis of DINOv2 features across multiple benchmark datasets and tasks, including food classification, segmentation, aesthetic assessment, and robustness to image distortions. In addition, we explore its capacity for continual learning by applying it to incremental food classification scenarios. Our findings reveal that DINOv2 features excel in many food-related applications. Their shared representations across tasks reduce the need for training separate models, while their strong generalization, high accuracy, and ability to handle complex multi-task scenarios make them a strong candidate for a unified food recognition approach. Specifically, DINOv2 features match or surpass state-of-the-art supervised methods in several food recognition tasks, while offering a simpler and more unified deployment strategy. Furthermore, they outperform end-to-end models in cross-dataset scenarios by up to +19.4% Top-1 accuracy and exhibits strong resilience to common image distortions by up to +48.0% robustness in Top-1 accuracy percentual difference, ensuring reliable performance in real-world applications. On average across all considered tasks, the DINOv2-based unified evaluation outperforms the state of the art by approximately 2.8% and 5.4%, depending on the chosen model size, while using only 6.2% and 23.9% of the total number of model parameters, respectively.