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

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.