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MegDet: A Large Mini-Batch Object Detector

CVPR 2018 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors · 324 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Increase batch size from 16 to 32 without BNResNet-50 + FPN detector · COCO valmmAP: 36.2 -> 36.4Helped
Increase batch size from 16 to 32 without BNResNet-50 + FPN detector · COCO valtraining time (h): 33.2 -> 15.1Helped
Add warmup and Cross-GPU Batch NormalizationLarge-mini-batch ResNet-50 + FPN detector · COCO valmmAP: 36.2 -> 37.1Helped
Add warmup and Cross-GPU Batch NormalizationLarge-mini-batch ResNet-50 + FPN detector · COCO valconvergenceHelped
Increase BN statistics size from 2 to 16 images (ablation)Batch-16 ResNet-50 + FPN detector · COCO valmmAP: 31.0 -> 37.0Helped
Increase BN statistics size from 16 to 64 images (ablation)Batch-64 ResNet-50 + FPN detector · COCO valmmAP: 37.1 -> 35.3Hurt
Use the long training policy instead of the normal policy (ablation)Batch-32 detector with BN size 32 · COCO valmmAP: 37.3 -> 37.8Helped
Increase batch size from 16 to 256 with CGBNResNet-50 + FPN detector · COCO valtraining time (h): 33.2 -> 4.1Helped
Use batch size 64 without BN at the scaled learning rate (ablation)ResNet-50 + FPN detector · COCO valconvergenceHurt
Use batch size 128 with half learning rate and no BN (ablation)ResNet-50 + FPN detector · COCO valconvergenceHurt

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