Inverse Cooking: Recipe Generation from Food Images

TL;DR

This paper presents an inverse cooking system that generates detailed recipes from food images by predicting ingredients and cooking instructions, outperforming previous methods in accuracy and quality.

Contribution

Introduces a novel architecture for ingredient prediction and recipe generation from food images, improving accuracy and recipe quality over existing approaches.

Findings

Enhanced ingredient prediction accuracy

High-quality recipe generation leveraging image and ingredients

System produces more compelling recipes than retrieval-based methods

Abstract

People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction; (2) we are able to obtain high quality recipes by leveraging both image and ingredients; (3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment. We make code and models publicly available.