# Cooking Object's State Identification Without Using Pretrained Model

arXiv: https://arxiv.org/abs/2103.02305 · HTML: https://mlsanity.com/paper/2103.02305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Functional Object-Oriented Network for Manipulation Learning](https://mlsanity.com/paper/1902.01537/) (2019)
- [A Survey of Knowledge Representation in Service Robotics](https://mlsanity.com/paper/1807.02192/) (2018)
- [Functional Object-Oriented Network: Construction & Expansion](https://mlsanity.com/paper/1807.02189/) (2018)
- [Joint Object and State Recognition using Language Knowledge](https://mlsanity.com/paper/1905.08843/) (2019)
- [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)
- [AI Meets Physical World -- Exploring Robot Cooking](https://mlsanity.com/paper/1804.07974/) (2018)

## Built on by (most cited first, 1 of 1)

- [Continuous Object State Recognition for Cooking Robots Using Pre-Trained Vision-Language Models and Black-box Optimization](https://mlsanity.com/paper/2403.08239/) (2024)

## Abstract (arXiv)

Recently, Robotic Cooking has been a very promising field. To execute a recipe, a robot has to recognize different objects and their states. Contrary to object recognition, state identification has not been explored that much. But it is very important because different recipe might require different state of an object. Moreover, robotic grasping depends on the state. Pretrained model usually perform very well in this type of tests. Our challenge was to handle this problem without using any pretrained model. In this paper, we have proposed a CNN and trained it from scratch. The model is trained and tested on the dataset from cooking state recognition challenge. We have also evaluated the performance of our network from various perspective. Our model achieves 65.8% accuracy on the unseen test dataset.