RecipeSnap -- a lightweight image-to-recipe model
This work addresses the deployment of image-to-recipe models on portable devices like smartphones, representing an incremental improvement in efficiency.
The paper tackles the problem of high computational cost in image-to-recipe models by introducing RecipeSnap, a lightweight model that reduces memory and computational costs by over 90% while achieving a MedR of 2.0, matching state-of-the-art performance.
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.