Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model

• URL: http://arxiv.org/abs/2505.04861v1
• Date: Thu, 08 May 2025 00:08:31 GMT
• Title: Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model
• Authors: Navin Ranjan, Andreas Savakis,
• Abstract summary: Mix-QSAM is a mixed-precision Post-Training Quantization (PTQ) framework for the Segment Anything Model (SAM)<n>We introduce a layer-wise importance score, derived using Kullback-Leibler (KL) divergence, to quantify each layer's contribution to the model's output.<n>We also introduce cross-layer synergy, a novel metric based on causal mutual information, to capture dependencies between adjacent layers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Segment Anything Model (SAM) is a popular vision foundation model; however, its high computational and memory demands make deployment on resource-constrained devices challenging. While Post-Training Quantization (PTQ) is a practical approach for reducing computational overhead, existing PTQ methods rely on fixed bit-width quantization, leading to suboptimal accuracy and efficiency. To address this limitation, we propose Mix-QSAM, a mixed-precision PTQ framework for SAM. First, we introduce a layer-wise importance score, derived using Kullback-Leibler (KL) divergence, to quantify each layer's contribution to the model's output. Second, we introduce cross-layer synergy, a novel metric based on causal mutual information, to capture dependencies between adjacent layers. This ensures that highly interdependent layers maintain similar bit-widths, preventing abrupt precision mismatches that degrade feature propagation and numerical stability. Using these metrics, we formulate an Integer Quadratic Programming (IQP) problem to determine optimal bit-width allocation under model size and bit-operation constraints, assigning higher precision to critical layers while minimizing bit-width in less influential layers. Experimental results demonstrate that Mix-QSAM consistently outperforms existing PTQ methods on instance segmentation and object detection tasks, achieving up to 20% higher average precision under 6-bit and 4-bit mixed-precision settings, while maintaining computational efficiency.

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