Quantization Robustness to Input Degradations for Object Detection
Post-training quantization (PTQ) is crucial for deploying efficient object
detection models, like YOLO, on resource-constrained devices. However, the
impact of reduced precision on model robustness to real-world input
degradations such as noise, blur, and compression artifacts is a significant
concern. This paper presents a comprehensive empirical study evaluating the
robustness of YOLO models (nano to extra-large scales) across multiple
precision formats: FP32, FP16 (TensorRT), Dynamic UINT8 (ONNX), and Static INT8
(TensorRT). We introduce and evaluate a degradation-aware calibration strategy
for Static INT8 PTQ, where the TensorRT calibration process is exposed to a mix
of clean and synthetically degraded images. Models were benchmarked on the COCO
dataset under seven distinct degradation conditions (including various types
and levels of noise, blur, low contrast, and JPEG compression) and a
mixed-degradation scenario. Results indicate that while Static INT8 TensorRT
engines offer substantial speedups (~1.5-3.3x) with a moderate accuracy drop
(~3-7% mAP50-95) on clean data, the proposed degradation-aware calibration did
not yield consistent, broad improvements in robustness over standard clean-data
calibration across most models and degradations. A notable exception was
observed for larger model scales under specific noise conditions, suggesting
model capacity may influence the efficacy of this calibration approach. These
findings highlight the challenges in enhancing PTQ robustness and provide
insights for deploying quantized detectors in uncontrolled environments. All
code and evaluation tables are available at https://github.com/AllanK24/QRID.
68b339d0851c6e7b001ec763
Toghrul Karimov
68b339d0851c6e7b001ec764
Hassan Imani
68b339d0851c6e7b001ec765
Allan Kazakov
claimed_verified
2025-09-01T07:52:29.225Z
64fc60fe74574268a5723ec0
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eT2iHJx2WKQWRIW17egrE.jpeg
Allan Kazakov
user
AllanK24
Quantization Robustness to Input Degradations for Object Detection
Post-training quantization (PTQ) is crucial for deploying efficient object
detection models, like YOLO, on resource-constrained devices. However, the
impact of reduced precision on model robustness to real-world input
degradations such as noise, blur, and compression artifacts is a significant
concern. This paper presents a comprehensive empirical study evaluating the
robustness of YOLO models (nano to extra-large scales) across multiple
precision formats: FP32, FP16 (TensorRT), Dynamic UINT8 (ONNX), and Static INT8
(TensorRT). We introduce and evaluate a degradation-aware calibration strategy
for Static INT8 PTQ, where the TensorRT calibration process is exposed to a mix
of clean and synthetically degraded images. Models were benchmarked on the COCO
dataset under seven distinct degradation conditions (including various types
and levels of noise, blur, low contrast, and JPEG compression) and a
mixed-degradation scenario. Results indicate that while Static INT8 TensorRT
engines offer substantial speedups (~1.5-3.3x) with a moderate accuracy drop
(~3-7% mAP50-95) on clean data, the proposed degradation-aware calibration did
not yield consistent, broad improvements in robustness over standard clean-data
calibration across most models and degradations. A notable exception was
observed for larger model scales under specific noise conditions, suggesting
model capacity may influence the efficacy of this calibration approach. These
findings highlight the challenges in enhancing PTQ robustness and provide
insights for deploying quantized detectors in uncontrolled environments. All
code and evaluation tables are available at https://github.com/AllanK24/QRID.
Post-training quantization of YOLO models is evaluated for robustness to real-world degradations, with a focus on the effectiveness of a degradation-aware calibration strategy for Static INT8 quantization.
post-training quantization YOLO FP32 FP16 Dynamic UINT8 Static INT8 TensorRT calibration degradation-aware COCO dataset noise blur low contrast JPEG compression mAP50-95