# State Classification of Cooking Objects Using a VGG CNN

Source: [State Classification of Cooking Objects Using a VGG CNN](<https://www.sota2.com/research/paper/68fa65a71e209131cea0b9d4>)

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

Data updated: 2025-10-25T21:32:37.817Z

Authors: Kyle Mott

Published: 2019-04-21T23:32:53.000Z

## Abstract

In machine learning, it is very important for a robot to know the state of an object and recognize particular desired states. This is an image classification problem that can be solved using a convolutional neural network. In this paper, we will discuss the use of a VGG convolutional neural network to recognize those states of cooking objects. We will discuss the uses of activation functions, optimizers, data augmentation, layer additions, and other different versions of architectures. The results of this paper will be used to identify alternatives to the VGG convolutional neural network to improve accuracy.

## 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/1904.12613>)

[PDF](<https://arxiv.org/pdf/1904.12613.pdf>)