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Usually training CNN costs us a lot of time and GPU cycles. One key technique to avoid this type of cost is "transfer learning". This example shows how we can try "transfer learning" using MATLAB. We combine pretrained model (alex net) and SVM to classify two similar flowers, "Dandelion" and "Colt's Foot".
通常CNNの学習には膨大な計算時間と計算コストがかかります。こうしたコストを避けるひとつの方法に転移学習と呼ばれる方法があります。このサンプルでは、よく似た2種類の花、タンポポとフキタンポポを学習済みのモデル(Alex Net)と SVM を組み合わせて見分けます。
Cite As
Eiji Ota (2026). CNN / Transfer Learning Example (https://nl.mathworks.com/matlabcentral/fileexchange/57280-cnn-transfer-learning-example), MATLAB Central File Exchange. Retrieved .
Categories
Find more on Recognition, Object Detection, and Semantic Segmentation in Help Center and MATLAB Answers
General Information
- Version 1.5.0.0 (6.15 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.5.0.0 | Fixed a bug related to activations function |
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| 1.4.0.0 | Changed source codes to use support package for alexnet. |
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| 1.3.0.0 | Added automatic setup script "setupScript.m".
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| 1.2.0.0 | Modified readme file. The instruction in the readme file was wrong. Sorry! Wrong : Create 2 Folders for 'Dandelion' and 'ColtsFoot' under 'ImageData'
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| 1.1.0.0 | Modified the readme file. Please check bug report, if you have troubles with this demo.
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| 1.0.0.0 |
