# Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

arXiv: https://arxiv.org/abs/2010.08727 · HTML: https://mlsanity.com/paper/2010.08727/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning](https://mlsanity.com/paper/2103.13061/) (2021)
- [MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval](https://mlsanity.com/paper/2305.11327/) (2023)

## Abstract (arXiv)

Increased awareness of the impact of food consumption on health and lifestyle today has given rise to novel data-driven food analysis systems. Although these systems may recognize the ingredients, a detailed analysis of their amounts in the meal, which is paramount for estimating the correct nutrition, is usually ignored. In this paper, we study the novel and challenging problem of predicting the relative amount of each ingredient from a food image. We propose PITA, the Picture-to-Amount deep learning architecture to solve the problem. More specifically, we predict the ingredient amounts using a domain-driven Wasserstein loss from image-to-recipe cross-modal embeddings learned to align the two views of food data. Experiments on a dataset of recipes collected from the Internet show the model generates promising results and improves the baselines on this challenging task. A demo of our system and our data is availableat: foodai.cs.rutgers.edu.