# Cooking State Recognition from Images Using Inception Architecture

arXiv: https://arxiv.org/abs/1805.09967 · HTML: https://mlsanity.com/paper/1805.09967/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2018.

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

- [Rethinking Cooking State Recognition with Vision Transformers](https://mlsanity.com/paper/2212.08586/) (2022)
- [Learning Multimodal Attention for Manipulating Deformable Objects with Changing States](https://mlsanity.com/paper/2309.14837/) (2023)
- [Classifying States of Cooking Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2105.14196/) (2021)

## Abstract (arXiv)

A kitchen robot properly needs to understand the cooking environment to continue any cooking activities. But object's state detection has not been researched well so far as like object detection. In this paper, we propose a deep learning approach to identify different cooking states from images for a kitchen robot. In our research, we investigate particularly the performance of Inception architecture and propose a modified architecture based on Inception model to classify different cooking states. The model is analyzed robustly in terms of different layers, and optimizers. Experimental results on a cooking datasets demonstrate that proposed model can be a potential solution to the cooking state recognition problem.