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

Source: [Object Detection in Indian Food Platters using Transfer Learning with YOLOv4](<https://www.sota2.com/research/paper/68fa6c871e209131cea34800>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:12:46.566Z

Authors: Deepanshu Pandey, Purva Parmar, Gauri Toshniwal, Mansi Goel, Vishesh Agrawal, Shivangi Dhiman, Lavanya Gupta, Ganesh Bagler

Published: 2022-05-10T12:28:01.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.04841>)

[PDF](<https://arxiv.org/pdf/2205.04841.pdf>)