SlimSAM: 0.1% Data Makes Segment Anything Slim

arXiv:2312.05284v3 Announce Type: replace
Abstract: Current approaches for compressing the Segment Anything Model (SAM) yield commendable results, yet necessitate extensive data to train a new network from scratch. Employing conventional pruning techniques can remarkably reduce data requirements but would suffer from a degradation in performance. To address this challenging trade-off, we introduce SlimSAM, a novel data-efficient SAM compression method that achieves superior performance with extremely less training data. The essence of SlimSAM is encapsulated in the alternate slimming framework which …

arxiv cs.cv data segment segment anything type