Table 6.

Object detection and instance segmentation results of different backbones on the COCO val2017 dataset

Backbone	Mask R-CNN 1 $\times$38
	${\text{AP}}^b$	${\text{AP}}_{50}^b$	${\text{AP}}_{75}^b$	${\text{AP}}^m$	${\text{AP}}_{50}^m$	${\text{AP}}_{75}^m$	Params	FLOPs
CNN based
ResNet10197	40.4	61.1	44.2	36.4	57.7	38.8	63.2M	336G
ResNeXt101126	42.8	63.8	47.3	38.4	60.6	41.3	101.9M	493G
VAN-large105	47.1	67.9	51.9	42.2	65.4	45.5	64.4M	–
Transformer based
PVT-large112	42.9	65.0	46.6	39.5	61.9	42.5	81M	364G
Swin-B49	46.9	–	–	42.3	–	–	107M	496G
CSWin-B115	48.7∗	70.4∗	53.9∗	43.9∗	67.8∗	47.3	97M	526G
MLP based
CycleMLP-B583	44.1	65.5	48.4	40.1	62.8	43.0	95.3M	421G
WaveMLP-B76	45.7	67.5	50.1	27.8	49.2	59.7∗	75.1M	353G
HireMLP-L86	45.9	67.2	50.4	41.7	64.7	45.3	115.2M	443G
MS-MLP-B87	46.4	67.2	50.7	42.4	63.6	46.4	107.5M	557G
ActiveMLP-L85	47.4	69.9	52.0	43.2	67.3	46.5	96.0M	–

Employing the Mask R-CNN,³⁸ where “1x” means that a single-scale training schedule is used.

^∗The best performance.