  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 effectively enhances knowledge inheritance under severely
limited training data availability and exceptional pruning ratio. Diverging
from prior techniques, our framework progressively compresses the model by
alternately pruning and distilling distinct, decoupled sub-structures.
Disturbed Taylor pruning is also proposed to address the misalignment between
the pruning objective and training target, thereby boosting the
post-distillation after pruning. SlimSAM yields significant performance
improvements while demanding over 10 times less training data than any other
existing compression methods. Even when compared to the original SAM, SlimSAM
achieves approaching performance while reducing parameter counts to merely 1.4%
(9.1M), MACs to 0.8% (23G), and requiring only 0.1% (10k) of the SAM training
data. The code is available at http://github.com/czg1225/SlimSAM.
