  Food recognition plays an important role in food choice and intake, which is
essential to the health and well-being of humans. It is thus of importance to
the computer vision community, and can further support many food-oriented
vision and multimodal tasks. Unfortunately, we have witnessed remarkable
advancements in generic visual recognition for released large-scale datasets,
yet largely lags in the food domain. In this paper, we introduce Food2K, which
is the largest food recognition dataset with 2,000 categories and over 1
million images.Compared with existing food recognition datasets, Food2K
bypasses them in both categories and images by one order of magnitude, and thus
establishes a new challenging benchmark to develop advanced models for food
visual representation learning. Furthermore, we propose a deep progressive
region enhancement network for food recognition, which mainly consists of two
components, namely progressive local feature learning and region feature
enhancement. The former adopts improved progressive training to learn diverse
and complementary local features, while the latter utilizes self-attention to
incorporate richer context with multiple scales into local features for further
local feature enhancement. Extensive experiments on Food2K demonstrate the
effectiveness of our proposed method. More importantly, we have verified better
generalization ability of Food2K in various tasks, including food recognition,
food image retrieval, cross-modal recipe retrieval, food detection and
segmentation. Food2K can be further explored to benefit more food-relevant
tasks including emerging and more complex ones (e.g., nutritional understanding
of food), and the trained models on Food2K can be expected as backbones to
improve the performance of more food-relevant tasks. We also hope Food2K can
serve as a large scale fine-grained visual recognition benchmark.
