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AQD: Towards Accurate Quantized Object Detection

CVPR 2021 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors · 29 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use 4-bit AQD instead of the full-precision detectorRetinaNet, ResNet-18 · COCO minivalAP: 32.3 -> 34.1Helped
use 4-bit AQD instead of the full-precision detectorRetinaNet, ResNet-34 · COCO minivalAP: 36.3 -> 37.1Helped
use 4-bit AQD instead of the full-precision detectorRetinaNet, ResNet-50 · COCO minivalAP: 37.8 -> 38.1Helped
use 3-bit AQD instead of the full-precision detectorRetinaNet, ResNet-50 · COCO minivalAP: 37.8 -> 36.9Hurt
use 2-bit AQD instead of the full-precision detectorRetinaNet, ResNet-50 · COCO minivalAP: 37.8 -> 34.8Hurt
use 2-bit AQD instead of Group-NetFCOS, ResNet-50 · COCO minivalAP: 32.7 -> 35.4Helped
replace shared BatchNorm with multi-level BatchNorm (ablation)2-bit FCOS, ResNet-18 · COCO minivalAP: 26.4 -> 31.8Helped
replace shared GroupNorm with multi-level BatchNorm (ablation)2-bit FCOS, ResNet-18 · COCO minivalAP: 29.4 -> 31.8Helped
replace shared BatchNorm with multi-level BatchNorm (ablation)2-bit FCOS, ResNet-50 · COCO minivalAP: 30.3 -> 35.4Helped
replace shared GroupNorm with multi-level BatchNorm (ablation)2-bit FCOS, ResNet-50 · COCO minivalAP: 33.4 -> 35.4Helped
quantize the feature pyramid in addition to the backbone (ablation)FCOS, ResNet-18 (AQD*) · COCO minivalAP: 33.8 -> 33.2Hurt
quantize the detection heads in addition to backbone and feature pyramid (ablation)FCOS, ResNet-18 (AQD*) · COCO minivalAP: 33.2 -> 32.2Hurt

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