  Food recognition has received more and more attention in the multimedia
community for its various real-world applications, such as diet management and
self-service restaurants. A large-scale ontology of food images is urgently
needed for developing advanced large-scale food recognition algorithms, as well
as for providing the benchmark dataset for such algorithms. To encourage
further progress in food recognition, we introduce the dataset ISIA Food- 500
with 500 categories from the list in the Wikipedia and 399,726 images, a more
comprehensive food dataset that surpasses existing popular benchmark datasets
by category coverage and data volume. Furthermore, we propose a stacked
global-local attention network, which consists of two sub-networks for food
recognition. One subnetwork first utilizes hybrid spatial-channel attention to
extract more discriminative features, and then aggregates these multi-scale
discriminative features from multiple layers into global-level representation
(e.g., texture and shape information about food). The other one generates
attentional regions (e.g., ingredient relevant regions) from different regions
via cascaded spatial transformers, and further aggregates these multi-scale
regional features from different layers into local-level representation. These
two types of features are finally fused as comprehensive representation for
food recognition. Extensive experiments on ISIA Food-500 and other two popular
benchmark datasets demonstrate the effectiveness of our proposed method, and
thus can be considered as one strong baseline. The dataset, code and models can
be found at http://123.57.42.89/FoodComputing-Dataset/ISIA-Food500.html.
