# Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

arXiv: https://arxiv.org/abs/1910.00100 · HTML: https://mlsanity.com/paper/1910.00100/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer](https://mlsanity.com/paper/2210.10628/) (2022)

## Abstract (arXiv)

In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction-based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results.