Due to rapid development in the field of machine learning and large increases in the capabilitiesof mobile devices, utilizing machine learning on these is becoming increasingly popular. Onemethod of deployment is to develop a machine learning model in well-established deep learningframeworks like PyTorch. However, to be able to run these models on mobile devices, specificframeworks are usually needed. In this thesis, we investigate the performance of a popular objectdetection model, YOLOv5, while being converted from PyTorch to CoreML. This includesmeasuring the performance of the model while running on different hardware. To accomplishthis, we put forward several common benchmarking metrics and compare the different stages.Our results show that CoreML greatly reduces the latency of a machine learning model and hascomparable detection accuracy of within a few percent in the metrics chosen. For iOS deviceswith the ANE-chipset, we also found that ANE (Apple Neural Engine) has significantly fasterlatency compared to running the model on GPU and CPU, while detection accuracy ismaintained. We discuss what could be the root cause for the small loss of accuracy in the modeland a foundation is laid for future work.

In the modern digital landscape, mobile devices serve as crucial data generators.Their usage spans from simple communication to various applications such as userbehavior analysis and intelligent applications. However, privacy concerns associatedwith data collection are persistent. Deep learning technologies, specifically Convo-lutional Neural Networks, have been increasingly integrated into mobile applicationsas a promising solution. In this study, we evaluated the performance of a CNN im-plemented on iOS smartphones using the CIFAR-10 data set, comparing the model’saccuracy and execution time before and after conversion for on-device deployment.The overarching objective was not to design the most accurate model but to inves-tigate the feasibility of deploying machine learning models on-device while retain-ing their accuracy. The results revealed that both on-cloud and on-device modelsyielded high accuracy (93.3% and 93.25%, respectively). However, a significantdifference was observed in the total execution time, with the on-device model re-quiring a considerably longer duration (45.64 seconds) than the cloud-based model(4.55 seconds). This study provides insights into the performance of deep learningmodels on iOS smartphones, aiding in understanding their practical applications andlimitations.

This master's thesis focuses on smartphone game for iOS, which uses recognition of face features and other information, which can be obtained from a smartphone's camera and sensors. This work describes a few approaches for real-time face detection and then introduces and compares possibilities for such task on iOS. Moreover, the thesis contains a draft of the final game and its levels. The game showcases various technologies in its levels such as object detection, processing an image color and others. Finally, the thesis introduces the final form of the game that is released and available on the App Store.