# FoodNet: Recognizing Foods Using Ensemble of Deep Networks

arXiv: https://arxiv.org/abs/1709.09429 · HTML: https://mlsanity.com/paper/1709.09429/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2017.

## Built on by (most cited first, 10 of 10)

- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [An End-to-End Food Image Analysis System](https://mlsanity.com/paper/2102.00645/) (2021)
- [Improving Dietary Assessment Via Integrated Hierarchy Food Classification](https://mlsanity.com/paper/2109.02736/) (2021)
- [Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks](https://mlsanity.com/paper/2205.12396/) (2022)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [A Mobile Food Recognition System for Dietary Assessment](https://mlsanity.com/paper/2204.09432/) (2022)
- [Dish detection in food platters: A framework for automated diet logging and nutrition management](https://mlsanity.com/paper/2305.07552/) (2023)
- [Deep Neural Networks Fused with Textures for Image Classification](https://mlsanity.com/paper/2308.01813/) (2023)
- [Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble](https://mlsanity.com/paper/2206.05853/) (2022)

## Abstract (arXiv)

In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food. We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency. Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features. Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy. Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.