  In this paper we want to address the problem of automation for recognition of
photographed cooking dishes and generating the corresponding food recipes.
Current image-to-recipe models are computation expensive and require powerful
GPUs for model training and implementation. High computational cost prevents
those existing models from being deployed on portable devices, like smart
phones. To solve this issue we introduce a lightweight image-to-recipe
prediction model, RecipeSnap, that reduces memory cost and computational cost
by more than 90% while still achieving 2.0 MedR, which is in line with the
state-of-the-art model. A pre-trained recipe encoder was used to compute recipe
embeddings. Recipes from Recipe1M dataset and corresponding recipe embeddings
are collected as a recipe library, which are used for image encoder training
and image query later. We use MobileNet-V2 as image encoder backbone, which
makes our model suitable to portable devices. This model can be further
developed into an application for smart phones with a few effort. A comparison
of the performance between this lightweight model to other heavy models are
presented in this paper. Code, data and models are publicly accessible on
github.
