Transfer Learning Using Teachable Machine For Classification Of Glassware In Chemistry Lab

Trupti Satpute; Dr. R. V. Kulkarni; Prakash Bansode

doi:10.46947/joaasr632024942

Authors

Trupti Satpute
Dr. R. V. Kulkarni
Prakash Bansode

DOI:

https://doi.org/10.46947/joaasr632024942

Keywords:

Image classification,Transfer learning,Deep learning,CNN,Teachable Machine

Abstract

Image classification is an important use case of deep learning algorithms. Convolution Nural networks, CNNs, have evolved to an extent where pretrained models can be used to train new models. The technique used for this type of model building activity is called as Transfer lerning. We have developed an image classification model using transfer lerning to classify lab glassware used in Chemistry lab. This model can be used for training purpose for the students in high schools who are not much aware about the practical implementation of laboratory experiments. We have used subset of Labpics dataset developed by Eppel et.al. We have used teachable machine as a platform to build this model with very limited computational resource. With transfer learning mechanism used by teachable machine platform we were able to achieve ~83% accurate image classification model

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References

Fukushima, K. and Miyake, S., 1982. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets (pp. 267–285). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-46466-9_18

LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp.2278–2324 DOI: https://doi.org/10.1109/5.726791

Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), pp.84–90. DOI: https://doi.org/10.1145/3065386

Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

C. Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9, doi: 10.1109/CVPR.2015.7298594. DOI: https://doi.org/10.1109/CVPR.2015.7298594

He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778). DOI: https://doi.org/10.1109/CVPR.2016.90

Fei-Fei Li, Justin Johnson and Serena Yeung, CS231n course, Stanford, Spring 2019

Carney, Michelle, et al. "Teachable machine: Approachable Web-based tool for exploring machine learning classification." Extended abstracts of the 2020 CHI conference on human factors in computing systems. 2020. DOI: https://doi.org/10.1145/3334480.3382839

Gu, Jiuxiang, et al. "Recent advances in convolutional neural networks." Pattern recognition 77 (2018): 354-377. DOI: https://doi.org/10.1016/j.patcog.2017.10.013

Li, Zewen, et al. "A survey of convolutional neural networks: analysis, applications, and prospects." IEEE transactions on neural networks and learning systems (2021).

Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. DOI: https://doi.org/10.1109/CVPR.2018.00474

Xiang, Qian, et al. "Fruit image classification based on Mobilenetv2 with transfer learning technique." Proceedings of the 3rd international conference on computer science and application engineering. 2019. DOI: https://doi.org/10.1145/3331453.3361658

Eppel, Sagi, et al. "Computer vision for recognition of materials and vessels in chemistry lab settings and the vector-labpics data set." ACS central science 6.10 (2020): 1743-1752. DOI: https://doi.org/10.1021/acscentsci.0c00460