扩充了训练样本，训练出的SVM模型将所有图片都判别为非车牌
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- C++
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Description
Hi你好，我在原有样本集基础上扩充了自己的一些样本，如下图，然而训练出的SVM模型反而分类不正确了，基本上将所有图片都判别为非车牌，即使是拿训练正样本里的图片给它判别，也判为非车牌。请问可能的原因是什么？应如何做？求教
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
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Research direction
Start by reviewing the expanded training samples and the SVM training and prediction setup described in the report. Compare the sample labels and the processing used for training and inference; done means identifying why positive samples are classified as non-plates and documenting a reproducible correction.
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Assessment
- Tech stack
- cpp, opencv
- Domain
- computer-vision, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100