# Classifying cooking object's state using a tuned VGG convolutional neural network

arXiv: https://arxiv.org/abs/1805.09391 · HTML: https://mlsanity.com/paper/1805.09391/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2018.

## Built on by (most cited first, 5 of 5)

- [Rethinking Cooking State Recognition with Vision Transformers](https://mlsanity.com/paper/2212.08586/) (2022)
- [Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-box Optimization](https://mlsanity.com/paper/2403.08239/) (2024)
- [Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDL](https://mlsanity.com/paper/2410.02874/) (2024)
- [Cooking Object's State Identification Without Using Pretrained Model](https://mlsanity.com/paper/2103.02305/) (2021)
- [Classifying States of Cooking Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2105.14196/) (2021)

## Abstract (arXiv)

In robotics, knowing the object states and recognizing the desired states are very important. Objects at different states would require different grasping. To achieve different states, different manipulations would be required, as well as different grasping. To analyze the objects at different states, a dataset of cooking objects was created. Cooking consists of various cutting techniques needed for different dishes (e.g. diced, julienne etc.). Identifying each of this state of cooking objects by the human can be difficult sometimes too. In this paper, we have analyzed seven different cooking object states by tuning a convolutional neural network (CNN). For this task, images were downloaded and annotated by students and they are divided into training and a completely different test set. By tuning the vgg-16 CNN 77% accuracy was obtained. The work presented in this paper focuses on classification between various object states rather than task recognition or recipe prediction. This framework can be easily adapted in any other object state classification activity.