Modular Quantization-Aware Training: Increasing Accuracy by Decreasing Precision in 6D Object Pose Estimation

• URL: http://arxiv.org/abs/2303.06753v2
• Date: Wed, 29 Nov 2023 01:17:18 GMT
• Title: Modular Quantization-Aware Training: Increasing Accuracy by Decreasing Precision in 6D Object Pose Estimation
• Authors: Saqib Javed, Chengkun Li, Andrew Price, Yinlin Hu, Mathieu Salzmann
• Abstract summary: Edge applications demand efficient 6D object pose estimation on resource-constrained embedded platforms. We introduce Modular Quantization-Aware Training (MQAT), an adaptive and mixed-precision quantization-aware training strategy. MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques.
• Score: 56.80039657816035
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D pose estimation networks are often too large for such deployments, necessitating compression while maintaining reliable performance. To address this challenge, we introduce Modular Quantization-Aware Training (MQAT), an adaptive and mixed-precision quantization-aware training strategy that exploits the modular structure of modern 6D pose estimation architectures. MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques. Our experiments showcase the generality of MQAT across datasets, architectures, and quantization algorithms. Remarkably, MQAT-trained quantized models achieve a significant accuracy boost (>7%) over the baseline full-precision network while reducing model size by a factor of 4x or more.

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