Personalized machine learning on the smartphone
The aim of the project is to provide an end-to-end solution for on-device training, inference and data collection for activity recongition based on TFlite Transfer Learning Pipeline. The corresponding blog post is available here.
If you are interested in contributing to this project, please submit a pull request or reach out at: [email protected].
The Heterogeneity Activity Recognition dataset is used for model pretraining. If you use this in your research, please cite their work and check the license.
If you find this project usefuly, please cite it as:
@misc{saeed2020recognition, author = {Saeed, Aaqib}, title = {On-device Learning of Activity Recognition Networks}, year = {2020}, journal = {aqibsaeed.github.io}, url = {\url{https://gitHub.com/aqibsaeed/on-device-activity-recognition}} }