而且最大的问题是一直作为消费电子市场主导力量的智能手机产业，正在丧失其对优秀供应链资源的绝对把控。
GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
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when adding it to that array, then you can have unref clean up recursively: