The formidable model size and demanding computational requirements of Segment
Anything Model (SAM) have rendered it cumbersome for deployment on
resource-constrained devices. Existing approaches for SAM compression typically
involve training a new network from scratch, posing a challenging trade-off
between compression costs and model performance. To address this issue, this
paper introduces SlimSAM, a novel SAM compression method that achieves superior
performance with remarkably low training costs. This is achieved by the
efficient reuse of pre-trained SAMs through a unified pruning-distillation
framework. To enhance knowledge inheritance from the original SAM, we employ an
innovative alternate slimming strategy that partitions the compression process
into a progressive procedure. Diverging from prior pruning techniques, we
meticulously prune and distill decoupled model structures in an alternating
fashion. Furthermore, a novel label-free pruning criterion is also proposed to
align the pruning objective with the optimization target, thereby boosting the
post-distillation after pruning. SlimSAM yields significant performance
improvements while demanding over 10 times less training costs than any other
existing methods. Even when compared to the original SAM-H, SlimSAM achieves
approaching performance while reducing parameter counts to merely 0.9% (5.7M),
MACs to 0.8% (21G), and requiring only 0.1% (10k) of the SAM training data.

SlimSAM 是一种新型的 SAM 压缩方法，通过高效地重用预训练的 SAM，采用统一的修剪 - 蒸馏框架，创新的交替瘦身策略以及新颖的无标签修剪准则，以显著降低训练成本并在性能上取得重大改进。