Table 1: The table shows the performance of various vision backbones on the ImageNet1K[10] dataset for image recognition tasks.  $\star$  indicates additionally trained with the Token Labeling objective using MixToken[25] and a convolutional stem (conv-stem) [60] for patch encoding. This table provides results for input image size  $224\times 224$ . We have grouped the vision models into three categories based on their GFLOPs (Small, Base, and Large). The GFLOP ranges: Small (GFLOPs<6), Base (6≤GFLOPs<10), and Large (10≤GFLOPs<30).
Method	Params	GFLOPS	Top-1	Top-5	Method	Params	GFLOPS	Top-1	Top-5
Small	Large
ResNet-50 [21]	25.5M	4.1	78.3	94.3	ResNet-152 [21]	60.2M	11.6	81.3	95.5
BoTNet-S1-50 [52]	20.8M	4.3	80.4	95.0	ResNeXt101 [67]	83.5M	15.6	81.5	-
Cross-ViT-S [6]	26.7M	5.6	81.0	-	gMLP-B [37]	73.0M	15.8	81.6	-
Swin-T [39]	29.0M	4.5	81.2	95.5	DeiT-B [56]	86.6M	17.6	81.8	95.6
ConViT-S [14]	27.8M	5.4	81.3	95.7	SE-ResNet-152 [23]	66.8M	11.6	82.2	95.9
T2T-ViT-14 [72]	21.5M	4.8	81.5	95.7	Cross-ViT-B [6]	104.7M	21.2	82.2	-
RegionViT-Ti+ [5]	14.3M	2.7	81.5	-	ResNeSt-101 [75]	48.3M	10.2	82.3	-
SE-CoTNetD-50 [35]	23.1M	4.1	81.6	95.8	ConViT-B [14]	86.5M	16.8	82.4	95.9
Twins-SVT-S [9]	24.1M	2.9	81.7	95.6	PoolFormer [71]	73.0M	11.8	82.5	-
CoaT-Lite-S [68]	20.0M	4.0	81.9	95.5	T2T-ViT-24 [72]	64.1M	15.0	82.6	95.9
PVTV2-B2 [62]	25.4M	4.0	82.0	96.0	TNT-B [19]	65.6M	14.1	82.9	96.3
LITv2-S [42]	28.0M	3.7	82.0	-	CycleMLP-B4 [7]	52.0M	10.1	83.0	-
MVViT2-T [33]	24.0M	4.7	82.3	-	DeepViT-L [78]	58.9M	12.8	83.1	-
Wave-ViT-S [70]	19.8M	4.3	82.7	96.2	RegionViT-B [5]	72.7M	13.0	83.2	96.1
CSwin-T [12]	23.0M	4.3	82.7	-	CycleMLP-B5 [7]	76.0M	12.3	83.2	-
DaViT-Ti [11]	28.3M	4.5	82.8	-	ViP-Large/7 [22]	88.0M	24.4	83.2	-
SVT-H-S	21.7M	3.9	83.1	96.3	CaiT-S36 [57]	68.4M	13.9	83.3	-
iFormer-S [51]	20.0M	4.8	83.4	96.6	AS-MLP-B [36]	88.0M	15.2	83.3	-
CMT-S [17]	25.1M	4.0	83.5	-	BoTNet-S1-128 [52]	75.1M	19.3	83.5	96.5
MaxViT-T [58]	31.0M	5.6	83.6	-	Swin-B [39]	88.0M	15.4	83.5	96.5
Wave-ViT-S* [70]	22.7M	4.7	83.9	96.6	Wave-MLP-B [53]	63.0M	10.2	83.6	-
SVT-H-S* (Ours)	22.0M	3.9	84.2	96.9	LITv2-B [42]	87.0M	13.2	83.6	-
Base	PVTv2-B4 [62]	62.6M	10.1	83.6	96.7
ResNet-101 [21]	44.6M	7.9	80.0	95.0	ViL-Base [76]	55.7M	13.4	83.7	-
BoTNet-S1-59 [52]	33.5M	7.3	81.7	95.8	Twins-SVT-L [9]	99.3M	15.1	83.7	96.5
T2T-ViT-19 [72]	39.2M	8.5	81.9	95.7	Hire-MLP-L [18]	96.0M	13.4	83.8	-
CvT-21 [64]	32.0M	7.1	82.5	-	RegionViT-B+ [5]	73.8M	13.6	83.8	-
GFNet-H-B [47]	54.0M	8.6	82.9	96.2	Focal-Base [69]	89.8M	16.0	83.8	96.5
Swin-S [39]	50.0M	8.7	83.2	96.2	PVTV2-B5 [62]	82.0M	11.8	83.8	96.6
Twins-SVT-B [9]	56.1M	8.6	83.2	96.3	CoTNetD-152 [35]	55.8M	17.0	84.0	97.0
CoTNetD-101 [35]	40.9M	8.5	83.2	96.5	DAT-B [65]	88.0M	15.8	84.0	-
PVTV2-B3 [62]	45.2M	6.9	83.2	96.5	LV-ViT-M* [25]	55.8M	16.0	84.1	96.7
LITv2-M [42]	49.0M	7.5	83.3	-	CSwin-B [12]	78.0M	15.0	84.2	-
RegionViT-M+ [5]	42.0M	7.9	83.4	-	HorNet-BGF[46]	88.0M	15.5	84.3	-
MVViT2-S [33]	35.0M	7.0	83.6	-	DynaMixer-L [63]	97.0M	27.4	84.3	-
CSwin-S [12]	35.0M	6.9	83.6	-	MVViT2-B [33]	52.0M	10.2	84.4	-
DaViT-S [11]	49.7M	8.8	84.2	-	DaViT-B [11]	87.9M	15.5	84.6	-
VOLO-D1* [73]	26.6M	6.8	84.2	-	CMT-L [17]	74.7M	19.5	84.8	-
CMT-B [17]	45.7M	9.3	84.5	-	MaxViT-B [58]	120.0M	23.4	85.0	-
MaxViT-S [58]	69.0M	11.7	84.5	-	VOLO-D2* [73]	58.7M	14.1	85.2	-
iFormer-B [51]	48.0M	9.4	84.6	97.0	VOLO-D3* [73]	86.3M	20.6	85.4	-
Wave-ViT-B* [70]	33.5M	7.2	84.8	97.1	Wave-ViT-L* [70]	57.5M	14.8	85.5	97.3
SVT-H-B* (Ours)	32.8M	6.3	85.2	97.3	SVT-H-L* (Ours)	54.0M	12.7	85.7	97.5