SlimSAM: 0.1% Data Makes Segment Anything Slim
Zigeng Chen, Gongfan Fang, Xinyin Ma, Xinchao Wang

TL;DR
SlimSAM is a novel, data-efficient compression method for the Segment Anything Model that significantly reduces training data and model size while maintaining near-original performance through iterative pruning and distillation.
Contribution
Introduces SlimSAM, a new progressive pruning and distillation framework that achieves high compression with minimal training data for SAM.
Findings
Reduces training data requirement to 0.1% of original
Compresses model to 1.4% of parameters while maintaining performance
Achieves over 10x less training data than existing methods
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 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…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Anomaly Detection Techniques and Applications · Data Stream Mining Techniques
MethodsSegment Anything Model · Pruning · ALIGN
