# PTQ4SAM: Post-Training Quantization for Segment Anything

arXiv: https://arxiv.org/abs/2405.03144 · HTML: https://mlsanity.com/paper/2405.03144/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 75 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 30 of 60)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://mlsanity.com/paper/2306.00978/) (2023)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale](https://mlsanity.com/paper/2208.07339/) (2022)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://mlsanity.com/paper/2206.01861/) (2022)
- [BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction](https://mlsanity.com/paper/2102.05426/) (2021)
- [A White Paper on Neural Network Quantization](https://mlsanity.com/paper/2106.08295/) (2021)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [Q-Diffusion: Quantizing Diffusion Models](https://mlsanity.com/paper/2302.04304/) (2023)
- [Post-training Quantization on Diffusion Models](https://mlsanity.com/paper/2211.15736/) (2022)
- [Personalize Segment Anything Model with One Shot](https://mlsanity.com/paper/2305.03048/) (2023)
- [Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm](https://mlsanity.com/paper/1808.00278/) (2018)
- [Track Anything: Segment Anything Meets Videos](https://mlsanity.com/paper/2304.11968/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Low-bit Quantization of Neural Networks for Efficient Inference](https://mlsanity.com/paper/1902.06822/) (2019)

## Built on by (most cited first, 30 of 32)

- [Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching](https://mlsanity.com/paper/2512.11130/) (2025)
- [APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers](https://mlsanity.com/paper/2504.02508/) (2025)
- [A Survey on Efficient Vision-Language Models](https://mlsanity.com/paper/2504.09724/) (2025)
- [On Efficient Variants of Segment Anything Model: A Survey](https://mlsanity.com/paper/2410.04960/) (2024)
- [LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment](https://mlsanity.com/paper/2410.21352/) (2024)
- [QVD: Post-training Quantization for Video Diffusion Models](https://mlsanity.com/paper/2407.11585/) (2024)
- [BiDM: Pushing the Limit of Quantization for Diffusion Models](https://mlsanity.com/paper/2412.05926/) (2024)
- [Low-bit Model Quantization for Deep Neural Networks: A Survey](https://mlsanity.com/paper/2505.05530/) (2025)
- [SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models](https://mlsanity.com/paper/2508.03351/) (2025)
- [LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit](https://mlsanity.com/paper/2508.09981/) (2025)
- [PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models](https://mlsanity.com/paper/2412.07268/) (2024)
- [SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation](https://mlsanity.com/paper/2405.06525/) (2024)
- [Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare](https://mlsanity.com/paper/2410.01813/) (2024)
- [Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model](https://mlsanity.com/paper/2505.04861/) (2025)
- [AHCQ-SAM: Toward Accurate and Hardware-Compatible Post-Training Segment Anything Model Quantization](https://mlsanity.com/paper/2503.03088/) (2025)
- [SAQ-SAM: Semantically-Aligned Quantization for Segment Anything Model](https://mlsanity.com/paper/2503.06515/) (2025)
- [AccuQuant: Simulating Multiple Denoising Steps for Quantizing Diffusion Models](https://mlsanity.com/paper/2510.20348/) (2025)
- [Compress Any Segment Anything Model (SAM)](https://mlsanity.com/paper/2507.08765/) (2025)
- [SLMQuant:Benchmarking Small Language Model Quantization for Practical Deployment](https://mlsanity.com/paper/2511.13023/) (2025)
- [Efficient-SAM2: Accelerating SAM2 with Object-Aware Visual Encoding and Memory Retrieval](https://mlsanity.com/paper/2602.08224/) (2026)
- [StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models](https://mlsanity.com/paper/2603.07307/) (2026)
- [Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference](https://mlsanity.com/paper/2505.14638/) (2025)
- [Q-SAM2: Accurate Quantization for Segment Anything Model 2](https://mlsanity.com/paper/2506.09782/) (2025)
- [PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation](https://mlsanity.com/paper/2603.11917/) (2026)
- [CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model](https://mlsanity.com/paper/2605.16901/) (2026)
- [SparseSAM: Structured Sparsification of Activations in Segment Anything Models](https://mlsanity.com/paper/2605.17633/) (2026)
- [Memory-efficient Continual Learning with Prototypical Exemplar Condensation](https://mlsanity.com/paper/2603.13804/) (2026)
- [When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization](https://mlsanity.com/paper/2604.16855/) (2026)
- [Lean-SAM2: Target-Anchored Memory and Encoder Acceleration for SAM2](https://mlsanity.com/paper/2607.19811/) (2026)
- [MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers](https://mlsanity.com/paper/2607.28589/) (2026)

## Abstract (arXiv)

Segment Anything Model (SAM) has achieved impressive performance in many computer vision tasks. However, as a large-scale model, the immense memory and computation costs hinder its practical deployment. In this paper, we propose a post-training quantization (PTQ) framework for Segment Anything Model, namely PTQ4SAM. First, we investigate the inherent bottleneck of SAM quantization attributed to the bimodal distribution in post-Key-Linear activations. We analyze its characteristics from both per-tensor and per-channel perspectives, and propose a Bimodal Integration strategy, which utilizes a mathematically equivalent sign operation to transform the bimodal distribution into a relatively easy-quantized normal distribution offline. Second, SAM encompasses diverse attention mechanisms (i.e., self-attention and two-way cross-attention), resulting in substantial variations in the post-Softmax distributions. Therefore, we introduce an Adaptive Granularity Quantization for Softmax through searching the optimal power-of-two base, which is hardware-friendly. Extensive experimental results across various vision tasks (instance segmentation, semantic segmentation and object detection), datasets and model variants show the superiority of PTQ4SAM. For example, when quantizing SAM-L to 6-bit, we achieve lossless accuracy for instance segmentation, about 0.5% drop with theoretical 3.9× acceleration. The code is available at urlhttps://github.com/chengtao-lv/PTQ4SAM.