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About Wood Classification With Transfer Learning Method and Bottleneck Features - ICOIACT 2019 PDF
This document discusses using deep convolutional neural networks (D-CNN) with transfer learning and bottleneck features for wood classification. It begins by explaining that wood industries need to carefully classify wood quality but current manual methods only achieve 55% accuracy. The document then introduces CNN and D-CNN models for image classification and discusses using pre-trained CNN models via transfer learning and bottleneck features to improve classification accuracy for small wood datasets. It aims to classify 5 wood classes and achieves 95.69% accuracy using these techniques.
Detailed Information
| Author: | ['Interpreter Dream'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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