# Classifying States of Cooking Objects Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/2105.14196 · HTML: https://mlsanity.com/paper/2105.14196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 15 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference](https://mlsanity.com/paper/1506.02158/) (2015)
- [Functional Object-Oriented Network for Manipulation Learning](https://mlsanity.com/paper/1902.01537/) (2019)
- [Towards Dropout Training for Convolutional Neural Networks](https://mlsanity.com/paper/1512.00242/) (2015)
- [Identifying Object States in Cooking-Related Images](https://mlsanity.com/paper/1805.06956/) (2018)
- [Classifying cooking object's state using a tuned VGG convolutional neural network](https://mlsanity.com/paper/1805.09391/) (2018)
- [Manipulation Motion Taxonomy and Coding for Robots](https://mlsanity.com/paper/1910.00532/) (2019)
- [Learning to Pour](https://mlsanity.com/paper/1705.09021/) (2017)
- [Cooking State Recognition from Images Using Inception Architecture](https://mlsanity.com/paper/1805.09967/) (2018)
- [VGG Fine-tuning for Cooking State Recognition](https://mlsanity.com/paper/1905.08606/) (2019)
- [State Classification with CNN](https://mlsanity.com/paper/1806.03973/) (2018)
- [State Classification of Cooking Objects Using a VGG CNN](https://mlsanity.com/paper/1904.12613/) (2019)
- [Accurate Robotic Pouring for Serving Drinks](https://mlsanity.com/paper/1906.12264/) (2019)

## Abstract (arXiv)

Automated cooking machine is a goal for the future. The main aim is to make the cooking process easier, safer, and create human welfare. To allow robots to accurately perform the cooking activities, it is important for them to understand the cooking environment and recognize the objects, especially correctly identifying the state of the cooking objects. This will significantly improve the correctness of the following cooking recipes. In this project, several parts of the experiment were conducted to design a robust deep convolutional neural network for classifying the state of the cooking objects from scratch. The model is evaluated by using various techniques, such as adjusting architecture layers, tuning key hyperparameters, and using different optimization techniques to maximize the accuracy of state classification.