Table 5.

Image classification results of representative CNN, ViT, and MLP-like models on ImageNet-1K benchmark

Model	Pre-trained dataset	Top 1 (%)	Params (M)	FLOPs (G)
CNN based
VGG-16100	–	71.5	134	15.5
Xception16	–	79.0	22.9	–
Inception-ResNet-V2101	–	80.1	–	–
ResNet-5097,102	–	80.4	25.6	4.1
ResNet-15297,102	–	82.0	60.2	11.5
RegNetY-8GF102,103	–	82.2	39	8.0
RegNetY-16GF103	–	82.9	84	15.9
ConvNeXt-B104	–	83.8	89.0	15.4
VAN-Huge105	–	84.2	60.3	12.2
EfficientNetV2-M106	–	85.1	54	24.0
EfficientNetV2-L106	–	85.7	120	53.0
PolyLoss(EfficientNetV2-L)107	–	87.21	–	–
EfficientNetV2-XL106	ImageNet-21k	87.3	208	94.0
RepLKNet-XL108	ImageNet-21k	87.82	335	128.7
Meta pseudo labels (EfficientNet-L2)109	JFT-300M	90.23	480	–
Transformer based
ViT-B/1647	–	77.9	86	55.5
DeiT-B/16110	–	81.8	86	17.6
T2T-ViT-24111	–	82.3	64.1	13.8
PVT-large112	–	82.3	61	9.8
Swin-B49	–	83.5	88	15.4
Nest-B113	–	83.8	68	17.9
PyramidTNT-B114	–	84.1	157	16.0
CSWin-B115	–	84.2	78	15.0
CaiT-M-48-448116	–	86.5	356	330
PeCo(ViT-H)99	–	88.31	635	–
ViT-L/1647	ImageNet-21k	85.3	307	–
SwinV1-L49	ImageNet-21k	87.3	197	103.9
SwinV2-G117	ImageNet-21k	90.22	3000	–
V-MoE90	JFT-300M	90.4	14,700	–
ViT-G/1447	JFT-300M	90.53	1843	–
CNN + Transformer
Twins-SVT-B118	–	83.2	56	8.6
Shuffle-B119	–	84.0	88	15.6
CMT-B120	–	84.5	45.7	9.3
CoAtNet-3121	–	84.5	168	34.7
VOLO-D3122	–	85.4	86	20.6
VOLO-D5122	–	87.11	296	69.0
CoAtNet-4121	ImageNet-21k	88.12	275	360.9
CoAtNet-7121	JFT-300M	90.93	2440	–
MLP based
DynaMixer-L75	–	84.31	97	27.4
ResMLP-B24/860	ImageNet-21k	84.42	129.1	100.2
Mixer-H/1415	JFT-300M	86.33	431	–

Pre-trained dataset column provides extra data information. PloyLoss, PeCo, and meta pseudo labels are different training strategies, where the used model is in the bracket

¹The best performance on ImageNet-1k without pre-trained dataset.

²The best performance on ImageNet-1k with ImageNet-21k pre-training.

³The best performance on ImageNet-1k with JFT-300M pre-training.