result_det.md

Here lists selected experiment result. The performance is potentially being better if more effort is paid on tuning. See experience.md to communicate training skills.

Detection

For training and inference instructions, refer detectron2.md. As the project is keeping upgrading, the pretrained model provided on Google Drive might show better performance compared with the one in table. For more details, please refer to our paper.

Dataset	Task Method	Quantization method	Model	A/W	Reported	AP	Flags
COCO	Retina-Net	-	Torch-18	32/32	-	31.5	1x
COCO	Retina-Net	-	Torch-18	32/32	-	32.8	1x, FPN-BN,Head-GN
COCO	Retina-Net	-	Torch-18	32/32	-	33.0	1x, FPN-BN,Head-BN
COCO	Retina-Net	-	Torch-34	32/32	-	35.2	1x
COCO	Retina-Net	-	Torch-50	32/32	-	36.6	1x
COCO	Retina-Net	-	Torch-50	32/32	-	37.8	1x, FPN-BN,Head-BN
COCO	Retina-Net	-	MSRA-R50	32/32	-	36.4	1x
COCO	Retina-Net	-	Torch-18	4/4	-	34.0	1x,Full-BN, Quantize-All
COCO	Retina-Net	-	Torch-18	3/3	-	32.8	1x,Full-BN, Quantize-All
COCO	Retina-Net	-	Torch-18	2/2	-	29.6	1x,Full-BN, Quantize-All
COCO	Retina-Net	-	Torch-34	4/4	-	37.0	1x,Full-BN, Quantize-All
COCO	Retina-Net	-	Torch-34	3/3	-	35.9	1x,Full-BN, Quantize-All
COCO	Retina-Net	-	Torch-34	2/2	-	33.1	1x,Full-BN, Quantize-All
COCO	FCOS	-	MSRA-R50	32/32	-	38.6	1x
COCO	FCOS	-	Torch-50	32/32	-	38.4	1x
COCO	FCOS	-	Torch-50	32/32	-	38.5	1x,FPN-BN
COCO	FCOS	-	Torch-50	32/32	-	38.9	1x,FPN-BN,Head-BN
COCO	FCOS	-	Torch-34	32/32	-	37.3	1x
COCO	FCOS	-	Torch-18	32/32	-	32.2	1x
COCO	FCOS	-	Torch-18	32/32	-	33.4	1x,FPN-BN
COCO	FCOS	-	Torch-18	32/32	-	33.9	1x,FPN-BN, FP16
COCO	FCOS	-	Torch-18	32/32	-	33.9	1x,FPN-BN,Head-BN
COCO	FCOS	-	Torch-18	32/32	-	34.3	1x,FPN-SyncBN,Head-SyncBN
COCO	FCOS	-	Torch-18	4/4	-	35.2	1x,FPN-BN, Quantize-All, double-init
COCO	FCOS	-	Torch-18	3/3	-	34.1	1x,FPN-BN, Quantize-All, double-init
COCO	FCOS	-	Torch-18	2/2	-	33.4	1x,FPN-BN, Quantize-Backbone, double-init
COCO	FCOS	-	Torch-18	2/2	-	32.0	1x,FPN-BN, Quantize-All, singe-pass-init
COCO	FCOS	-	Torch-18	2/2	-	30.3	1x,FPN-BN, Quantize-All, double-init
COCO	FCOS	LQ-Net	Torch-18	ter/ter	-	32.6	1x,FPN-BN, Quantize-Backbone, double-init
COCO	FCOS	LQ-Net	Torch-18	ter/ter	-	26.2	1x,FPN-BN, Quantize-All, double-init

Flags:

FPN-BN indicates adding BN and RELU in the FPN; FP16 implies the case is trained in FP16 (half float) mode; Head-BN represents the prospoal header employes non shared BatchNorm. Full-BN indicates combining FPN-BN and Head-BN. Torch-18/34/50 means the backbone is the Pytorch ResNet-18/34/50.