# A Framework of Transfer Learning in Object Detection for Embedded Systems

arXiv: https://arxiv.org/abs/1811.04863 · HTML: https://mlsanity.com/paper/1811.04863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Deep Learning for Iris Recognition: A Review](https://mlsanity.com/paper/2303.08514/) (2023)
- [Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8](https://mlsanity.com/paper/2306.03537/) (2023)

## Abstract (arXiv)

Transfer learning is one of the subjects undergoing intense study in the area of machine learning. In object recognition and object detection there are known experiments for the transferability of parameters, but not for neural networks which are suitable for object detection in real time embedded applications, such as the SqueezeDet neural network. We use transfer learning to accelerate the training of SqueezeDet to a new group of classes. Also, experiments are conducted to study the transferability and co-adaptation phenomena introduced by the transfer learning process. To accelerate training, we propose a new implementation of the SqueezeDet training which provides a faster pipeline for data processing and achieves 1.8 times speedup compared to the initial implementation. Finally, we created a mechanism for automatic hyperparameter optimization using an empirical method.