TL;DR
This paper introduces Birkhoff, a versatile, data-free compression algorithm for SAM models that achieves high compression ratios and fast inference without fine-tuning, suitable for diverse applications.
Contribution
Birkhoff presents a novel data-free compression method using Hyper-Compression and HyperLinear, offering a universal, fast, and faithful compression solution for SAM models.
Findings
Achieves up to 5.17x compression ratio with less than 1% performance loss.
Compresses models within 60 seconds without fine-tuning.
Performs consistently across multiple datasets and SAM variants.
Abstract
Due to the excellent performance in yielding high-quality, zero-shot segmentation, Segment Anything Model (SAM) and its variants have been widely applied in diverse scenarios such as healthcare and intelligent manufacturing. Therefore, effectively compressing SAMs has become an increasingly pressing practical need. In this study, we propose Birkhoff, a novel data-free compression algorithm for SAM and its variants. Unlike quantization, pruning, distillation, and other compression methods, Birkhoff embodies versatility across model types, agility in deployment, faithfulness to the original model, and compactness in model size. Specifically, Birkhoff introduces a novel compression algorithm: Hyper-Compression, whose core principle is to find a dense trajectory to turn a high-dimensional parameter vector into a low-dimensional scalar. Furthermore, Birkhoff designs a dedicated linear layer…
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Taxonomy
MethodsSegment Anything Model · Linear Layer
