Table 4.

Image classification results of MLP-like models on ImageNet-1K benchmark without extra data

Model	Date	Structure	Top 1 (%)	Params (M)	FLOPs (G)	Open source code
Small models
Sparse-MLP(MoE)-S88	2021.09	single stage	71.3	21	–	false
RepMLPNet-T22492	2021.12	pyramid	76.4	15.2	2.8	true
ResMLP-1260	2021.05	single stage	76.6	15	3.0	true
Hire-MLP-Ti86	2021.08	pyramid	78.9	17	2.1	falseb
gMLP-S67	2021.05	single stage	79.4	20	4.5	true
AS-MLP-T80	2021.07	pyramid	81.3	28	4.4	true
ViP-small/771	2021.06	two stage	81.5	25	6.9	true
CycleMLP-B283	2021.07	pyramid	81.6	27	3.9	true
MorphMLP-T77	2021.11	pyramid	81.6	23	3.9	false
Sparse MLP-T73	2021.09	pyramid	81.9	24.1	5.0	false
ActiveMLP-T85	2022.03	pyramid	82.0	27	4.0	false
S2-MLPv2-small/779	2021.08	two stage	82.0	25	6.9	false
MS-MLP-T87	2022.02	pyramid	82.1	28	4.9	true
WaveMLP-S76	2021.11	pyramid	82.6	30.0	4.5	falseb
DynaMixer-S75	2022.01	two stage	82.7∗	26	7.3	false
Medium models
FeedForward61	2021.05	single stage	74.9	62	11.4	true
Mixer-B/1615	2021.05	single stage	76.4	59	11.7	true
Sparse-MLP(MoE)-B88	2021.09	single stage	77.9	69	–	false
RaftMLP-1274	2021.08	single stage	78.0	58	12.0	false
ResMLP-3660	2021.05	single stage	79.7	45	8.9	true
Mixer-B/16 + CCS91	2021.06	single stage	79.8	57	11	false
RepMLPNet-B224 92	2021.12	pyramid	80.1	68.2	6.7	true
S2-MLP-deep 78	2021.06	single stage	80.7	51	9.7	false
ViP-medium/7 71	2021.06	two stage	82.7	55	16.3	true
CycleMLP-B4 83	2021.07	pyramid	83.0	52	10.1	true
AS-MLP-S 80	2021.07	pyramid	83.1	50	8.5	true
Hire-MLP-B 86	2021.08	pyramid	83.1	58	8.1	falseb
MorphMLP-B 77	2021.11	pyramid	83.2	58	10.2	false
Sparse MLP-B 73	2021.09	pyramid	83.4	65.9	14.0	false
MS-MLP-S 87	2022.02	pyramid	83.4	50	9.0	true
ActiveMLP-B 85	2022.03	pyramid	83.5	52	10.1	false
S2-MLPv2-medium/7 79	2021.08	two stage	83.6	55	16.3	false
WaveMLP-B 76	2021.11	pyramid	83.6	63.0	10.2	falseb
DynaMixer-M 75	2022.01	two stage	83.7∗	57	17.0	false
Large Models
Sparse-MLP(MoE)-L 88	2021.09	single stage	79.2	130	–	false
S2-MLP-wide 78	2021.06	single stage	80.0	71	14.0	false
gMLP-B 67	2021.05	single stage	81.6	73	15.8	true
RepMLPNet-L256a92	2021.12	pyramid	81.8	117.7	11.5	true
ViP-large/771	2021.06	two stage	83.2	88	24.3	true
CycleMLP-B583	2021.07	pyramid	83.2	76	12.3	true
AS-MLP-B80	2021.07	pyramid	83.3	88	15.2	true
Hire-MLP-L86	2021.08	pyramid	83.4	96	13.5	falseb
MorphMLP-L77	2021.11	pyramid	83.4	76	12.5	false
ActiveMLP-L85	2022.03	pyramid	83.6	76	12.3	false
MS-MLP-B87	2022.02	pyramid	83.8	88	16.1	true
DynaMixer-L75	2022.01	two stage	84.3∗	97	27.4	false

The training and testing size is $224\times 224$. “Date” means the initial release date on arXiv, where 2021.05 denotes May, 2021. “Open source code” refers to whether there is officially open source code.

^aThe training and testing size is $256\times 256$.

^bUnofficial code and weights are open sourced at https://github.com/sithu31296/image-classification.

^∗The best performance.