Laboratory Glassware Identification: Supervised Machine Learning Example for Science Students

Volume 12, Issue 1 (January 2021), pp. 8–15

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@article{jocse-12-1-2,
  author={Arun K. Sharma},
  title={Laboratory Glassware Identification: Supervised Machine Learning Example for Science Students},
  journal={The Journal of Computational Science Education},
  year=2021,
  month=jan,
  volume=12,
  issue=1,
  pages={8--15},
  doi={https://doi.org/10.22369/issn.2153-4136/12/1/2}
}

This paper provides a supervised machine learning example to identify laboratory glassware. This project was implemented in an Introduction to Scientific Computing course for first-year students at our institution. The goal of the exercise was to present a typical machine learning task in the context of a chemistry laboratory to engage students with computing and its applications to scientific projects. This is an end-to-end data science experience with students creating the dataset, training a neural network, and analyzing the performance of the trained network. The students collected pictures of various glassware in a chemistry laboratory. Four pre-trained neural networks, Inception-V1, Inception-V3, ResNet-50, and ResNet-101 were trained to distinguish between the objects in the pictures. The Wolfram Language was used to carry out the training of neural networks and testing the performance of the classifier. The students received hands-on training in the Wolfram Language and an elementary introduction to image classification tasks in the machine learning domain. Students enjoyed the introduction to machine learning applications and the hands-on experience of building and testing an image classifier to identify laboratory equipment.