# Object Detection in Indian Food Platters using Transfer Learning with YOLOv4

arXiv: https://arxiv.org/abs/2205.04841 · HTML: https://mlsanity.com/paper/2205.04841/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 5 of 5)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment](https://mlsanity.com/paper/1606.05675/) (2016)
- [Simultaneous Food Localization and Recognition](https://mlsanity.com/paper/1604.07953/) (2016)
- [FoodTracker: A Real-time Food Detection Mobile Application by Deep Convolutional Neural Networks](https://mlsanity.com/paper/1909.05994/) (2019)

## Built on by (most cited first, 3 of 3)

- [Dish detection in food platters: A framework for automated diet logging and nutrition management](https://mlsanity.com/paper/2305.07552/) (2023)
- [Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition](https://mlsanity.com/paper/2503.18548/) (2025)
- [DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding](https://mlsanity.com/paper/2607.23070/) (2026)

## Abstract (arXiv)

Object detection is a well-known problem in computer vision. Despite this, its usage and pervasiveness in the traditional Indian food dishes has been limited. Particularly, recognizing Indian food dishes present in a single photo is challenging due to three reasons: 1. Lack of annotated Indian food datasets 2. Non-distinct boundaries between the dishes 3. High intra-class variation. We solve these issues by providing a comprehensively labelled Indian food dataset- IndianFood10, which contains 10 food classes that appear frequently in a staple Indian meal and using transfer learning with YOLOv4 object detector model. Our model is able to achieve an overall mAP score of 91.8% and f1-score of 0.90 for our 10 class dataset. We also provide an extension of our 10 class dataset- IndianFood20, which contains 10 more traditional Indian food classes.