Unverified50% confidenceFactExact time
DINOv2 Giant参数量约11亿，嵌入维度为1536
1
Sources
50%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
DINOv2 vs SigLIP: Why the Huge Gap in k-NN Classification? A Guide to Avoiding Vision Encoder Selection Pitfalls
redditr/MachineLearning7/8/2026
Related Claims
UnverifiedDINOv2 Giant参数量约11亿、嵌入维度1536，均显著大于SigLIP2 SO400M（约4亿参数、维度1152）和CLIP ViT-L（约3亿参数、维度768），但k-NN任务表现最差85% similarUnverifiedDINOv2 Giant输出嵌入维度高达1536维，在仅175张训练样本下k-NN面临维度灾难挑战73% similarUnverifiedLLaMA-2 70B 总参数约 700 亿均匀分布于 80 层，每层约 8.75 亿参数，FP16 精度下单层约占 1.75GB 显存67% similarPartially VerifiedVectors generated by embedding models are typically 768-dimensional or 1,536-dimensional66% similarUnverifiedRunning an open-source model at the 70-billion-parameter scale requires at least approximately 140GB of VRAM in FP16 precision, i.e., 8 H100s in parallel.65% similar
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