Unverified50% confidenceBenchmarkExact time
在同一细粒度汽车分类任务中，CLIP ViT-L的k-NN准确率约为59%
1
Sources
50%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
DINOv2 vs SigLIP：k-NN分类为何差距悬殊？视觉编码器选型避坑指南
redditr/MachineLearning7/8/2026
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Unverified在细粒度汽车分类的k-NN实验中，SigLIP2 SO400M达到约92%准确率，CLIP ViT-L约59%，DINOv2 Giant约41%78% similarUnverified在同一细粒度汽车分类任务中，DINOv2 Giant的k-NN准确率约为41%，表现垫底78% similarUnverifiedK-Quant是llama.cpp生态中的混合精度量化方案，根据每层权重敏感度分配不同量化bit数，可在相同平均bit数下将精度损失降低40-60%61% similarUnverifiedtop-1%准确率是评估量化模型与原始全精度模型输出一致性的指标，关注模型置信度最高的1%的token是否与全精度版本一致61% similarUnverified引导模式下平均端到端准确率从28.6%提升到33.7%，歧义检测F1值从45.3%提升到64.9%59% similar
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