Optimisation of spatial-exploitation CNN models through hyperparameter-tuning and human-in-the-loop combination

Luke Beveridge, Keshav Dahal

Research output: Contribution to journalArticlepeer-review


Spatial-exploitation convolutional neural networks (CNNs) have a simplified architecture compared to other CNN models. However, devices with limited computational resources could struggle with processing spatial-exploitation CNNs. To address this, we investigate two methods to optimise spatial-exploitation CNN models for time efficiency and classification accuracy: hyperparameter-tuning, and human-in-the-loop (HITL). We apply grid-search to optimise the hyperparameter space, whilst HITL is used to identify whether the time-to-accuracy relationship of the optimised model can be improved. To show the versatility of combining the two methods, CIFAR-10, MNIST, and Imagenette are used as model input. This paper contributes to spatial-exploitation CNN optimisation by combining hyperparameter-tuning and HITL. Results show that this combination improves classification accuracy by 1.47%-2.34% and reduces the time taken to conduct this task by 27%-28%, depending on dataset. We conclude that combining hyperparameter-tuning and HITL are a viable approach to optimise spatial-exploitation CNNs for devices with limited computational resources.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalInternational Journal of Artificial Intelligence and Soft Computing
Issue number2
Publication statusPublished - 4 Jul 2024


  • deep learning
  • convolutional neural network (CNN)
  • image classification
  • hyperparameter-tuning
  • human-in-the-loop (HITL)


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