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About Capsule Neural Network and Its Implementation For Object Recognition in Resource-Limited Devices
This thesis explores using a capsule neural network (CapsNet) for object recognition with small datasets and the possibility of implementing it on embedded devices. It presents background information on machine learning, deep learning, and CNNs. The thesis then designs and trains a CapsNet model on a two-class image dataset, comparing its performance to a CNN. The results show the CapsNet achieves 70% accuracy on training and validation with 35 epochs, while the CNN overfits. Finally, it discusses compressing CapsNet models for deployment on edge devices.
Detailed Information
| Author: | ['Nguyen Van Binh'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 103 |
| Language: | English |
| Format: | |
| Price: | FREE |
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