Note

You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

Model Zoo

ImageNet

ImageNet has multiple versions, but the most commonly used one is ILSVRC 2012. The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.

Model	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Config	Download
VGG-11	132.86	7.63	68.75	88.87	config	model | log
VGG-13	133.05	11.34	70.02	89.46	config	model | log
VGG-16	138.36	15.5	71.62	90.49	config	model | log
VGG-19	143.67	19.67	72.41	90.80	config	model | log
VGG-11-BN	132.87	7.64	70.75	90.12	config	model | log
VGG-13-BN	133.05	11.36	72.15	90.71	config	model | log
VGG-16-BN	138.37	15.53	73.72	91.68	config	model | log
VGG-19-BN	143.68	19.7	74.70	92.24	config	model | log
RepVGG-A0*	9.11（train) | 8.31 (deploy)	1.52 (train) | 1.36 (deploy)	72.41	90.50	config (train) | config (deploy)	model
RepVGG-A1*	14.09 (train) | 12.79 (deploy)	2.64 (train) | 2.37 (deploy)	74.47	91.85	config (train) | config (deploy)	model
RepVGG-A2*	28.21 (train) | 25.5 (deploy)	5.7 (train) | 5.12 (deploy)	76.48	93.01	config (train) | config (deploy)	model
RepVGG-B0*	15.82 (train) | 14.34 (deploy)	3.42 (train) | 3.06 (deploy)	75.14	92.42	config (train) | config (deploy)	model
RepVGG-B1*	57.42 (train) | 51.83 (deploy)	13.16 (train) | 11.82 (deploy)	78.37	94.11	config (train) | config (deploy)	model
RepVGG-B1g2*	45.78 (train) | 41.36 (deploy)	9.82 (train) | 8.82 (deploy)	77.79	93.88	config (train) | config (deploy)	model
RepVGG-B1g4*	39.97 (train) | 36.13 (deploy)	8.15 (train) | 7.32 (deploy)	77.58	93.84	config (train) | config (deploy)	model
RepVGG-B2*	89.02 (train) | 80.32 (deploy)	20.46 (train) | 18.39 (deploy)	78.78	94.42	config (train) | config (deploy)	model
RepVGG-B2g4*	61.76 (train) | 55.78 (deploy)	12.63 (train) | 11.34 (deploy)	79.38	94.68	config (train) | config (deploy)	model
RepVGG-B3*	123.09 (train) | 110.96 (deploy)	29.17 (train) | 26.22 (deploy)	80.52	95.26	config (train) | config (deploy)	model
RepVGG-B3g4*	83.83 (train) | 75.63 (deploy)	17.9 (train) | 16.08 (deploy)	80.22	95.10	config (train) | config (deploy)	model
RepVGG-D2se*	133.33 (train) | 120.39 (deploy)	36.56 (train) | 32.85 (deploy)	81.81	95.94	config (train) | config (deploy)	model
ResNet-18	11.69	1.82	70.07	89.44	config	model | log
ResNet-34	21.8	3.68	73.85	91.53	config	model | log
ResNet-50 (rsb-a1)	25.56	4.12	80.12	94.78	config	model | log
ResNet-101	44.55	7.85	78.18	94.03	config	model | log
ResNet-152	60.19	11.58	78.63	94.16	config	model | log
Res2Net-50-14w-8s*	25.06	4.22	78.14	93.85	config	model
Res2Net-50-26w-8s*	48.40	8.39	79.20	94.36	config	model
Res2Net-101-26w-4s*	45.21	8.12	79.19	94.44	config	model
ResNeSt-50*	27.48	5.41	81.13	95.59	config	model
ResNeSt-101*	48.28	10.27	82.32	96.24	config	model
ResNeSt-200*	70.2	17.53	82.41	96.22	config	model
ResNeSt-269*	110.93	22.58	82.70	96.28	config	model
ResNetV1D-50	25.58	4.36	77.54	93.57	config	model | log
ResNetV1D-101	44.57	8.09	78.93	94.48	config	model | log
ResNetV1D-152	60.21	11.82	79.41	94.7	config	model | log
ResNeXt-32x4d-50	25.03	4.27	77.90	93.66	config	model | log
ResNeXt-32x4d-101	44.18	8.03	78.71	94.12	config	model | log
ResNeXt-32x8d-101	88.79	16.5	79.23	94.58	config	model | log
ResNeXt-32x4d-152	59.95	11.8	78.93	94.41	config	model | log
SE-ResNet-50	28.09	4.13	77.74	93.84	config	model | log
SE-ResNet-101	49.33	7.86	78.26	94.07	config	model | log
RegNetX-400MF	5.16	0.41	72.56	90.78	config	model | log
RegNetX-800MF	7.26	0.81	74.76	92.32	config	model | log
RegNetX-1.6GF	9.19	1.63	76.84	93.31	config	model | log
RegNetX-3.2GF	15.3	3.21	78.09	94.08	config	model | log
RegNetX-4.0GF	22.12	4.0	78.60	94.17	config	model | log
RegNetX-6.4GF	26.21	6.51	79.38	94.65	config	model | log
RegNetX-8.0GF	39.57	8.03	79.12	94.51	config	model | log
RegNetX-12GF	46.11	12.15	79.67	95.03	config	model | log
ShuffleNetV1 1.0x (group=3)	1.87	0.146	68.13	87.81	config	model | log
ShuffleNetV2 1.0x	2.28	0.149	69.55	88.92	config	model | log
MobileNet V2	3.5	0.319	71.86	90.42	config	model | log
ViT-B/16*	86.86	33.03	85.43	97.77	config	model
ViT-B/32*	88.3	8.56	84.01	97.08	config	model
ViT-L/16*	304.72	116.68	85.63	97.63	config	model
Swin-Transformer tiny	28.29	4.36	81.18	95.61	config	model | log
Swin-Transformer small	49.61	8.52	83.02	96.29	config	model | log
Swin-Transformer base	87.77	15.14	83.36	96.44	config	model | log
Transformer in Transformer small*	23.76	3.36	81.52	95.73	config	model
T2T-ViT_t-14	21.47	4.34	81.83	95.84	config	model | log
T2T-ViT_t-19	39.08	7.80	82.63	96.18	config	model | log
T2T-ViT_t-24	64.00	12.69	82.71	96.09	config	model | log
Mixer-B/16*	59.88	12.61	76.68	92.25	config	model
Mixer-L/16*	208.2	44.57	72.34	88.02	config	model
DeiT-tiny	5.72	1.08	74.50	92.24	config	model | log
DeiT-tiny distilled*	5.72	1.08	74.51	91.90	config	model
DeiT-small	22.05	4.24	80.69	95.06	config	model | log
DeiT-small distilled*	22.05	4.24	81.17	95.40	config	model
DeiT-base	86.57	16.86	81.76	95.81	config	model | log
DeiT-base distilled*	86.57	16.86	83.33	96.49	config	model
DeiT-base 384px*	86.86	49.37	83.04	96.31	config	model
DeiT-base distilled 384px*	86.86	49.37	85.55	97.35	config	model
Conformer-tiny-p16*	23.52	4.90	81.31	95.60	config	model
Conformer-small-p32*	38.85	7.09	81.96	96.02	config	model
Conformer-small-p16*	37.67	10.31	83.32	96.46	config	model
Conformer-base-p16*	83.29	22.89	83.82	96.59	config	model
PCPVT-small*	24.11	3.67	81.14	95.69	config	model
PCPVT-base*	43.83	6.45	82.66	96.26	config	model
PCPVT-large*	60.99	9.51	83.09	96.59	config	model
SVT-small*	24.06	2.82	81.77	95.57	config	model
SVT-base*	56.07	8.35	83.13	96.29	config	model
SVT-large*	99.27	14.82	83.60	96.50	config	model
EfficientNet-B0*	5.29	0.02	76.74	93.17	config	model
EfficientNet-B0 (AA)*	5.29	0.02	77.26	93.41	config	model
EfficientNet-B0 (AA + AdvProp)*	5.29	0.02	77.53	93.61	config	model
EfficientNet-B1*	7.79	0.03	78.68	94.28	config	model
EfficientNet-B1 (AA)*	7.79	0.03	79.20	94.42	config	model
EfficientNet-B1 (AA + AdvProp)*	7.79	0.03	79.52	94.43	config	model
EfficientNet-B2*	9.11	0.03	79.64	94.80	config	model
EfficientNet-B2 (AA)*	9.11	0.03	80.21	94.96	config	model
EfficientNet-B2 (AA + AdvProp)*	9.11	0.03	80.45	95.07	config	model
EfficientNet-B3*	12.23	0.06	81.01	95.34	config	model
EfficientNet-B3 (AA)*	12.23	0.06	81.58	95.67	config	model
EfficientNet-B3 (AA + AdvProp)*	12.23	0.06	81.81	95.69	config	model
EfficientNet-B4*	19.34	0.12	82.57	96.09	config	model
EfficientNet-B4 (AA)*	19.34	0.12	82.95	96.26	config	model
EfficientNet-B4 (AA + AdvProp)*	19.34	0.12	83.25	96.44	config	model
EfficientNet-B5*	30.39	0.24	83.18	96.47	config	model
EfficientNet-B5 (AA)*	30.39	0.24	83.82	96.76	config	model
EfficientNet-B5 (AA + AdvProp)*	30.39	0.24	84.21	96.98	config	model
EfficientNet-B6 (AA)*	43.04	0.41	84.05	96.82	config	model
EfficientNet-B6 (AA + AdvProp)*	43.04	0.41	84.74	97.14	config	model
EfficientNet-B7 (AA)*	66.35	0.72	84.38	96.88	config	model
EfficientNet-B7 (AA + AdvProp)*	66.35	0.72	85.14	97.23	config	model
EfficientNet-B8 (AA + AdvProp)*	87.41	1.09	85.38	97.28	config	model
ConvNeXt-T*	28.59	4.46	82.05	95.86	config	model
ConvNeXt-S*	50.22	8.69	83.13	96.44	config	model
ConvNeXt-B*	88.59	15.36	83.85	96.74	config	model
ConvNeXt-B*	88.59	15.36	85.81	97.86	config	model
ConvNeXt-L*	197.77	34.37	84.30	96.89	config	model
ConvNeXt-L*	197.77	34.37	86.61	98.04	config	model
ConvNeXt-XL*	350.20	60.93	86.97	98.20	config	model
HRNet-W18*	21.30	4.33	76.75	93.44	config	model
HRNet-W30*	37.71	8.17	78.19	94.22	config	model
HRNet-W32*	41.23	8.99	78.44	94.19	config	model
HRNet-W40*	57.55	12.77	78.94	94.47	config	model
HRNet-W44*	67.06	14.96	78.88	94.37	config	model
HRNet-W48*	77.47	17.36	79.32	94.52	config	model
HRNet-W64*	128.06	29.00	79.46	94.65	config	model
HRNet-W18 (ssld)*	21.30	4.33	81.06	95.70	config	model
HRNet-W48 (ssld)*	77.47	17.36	83.63	96.79	config	model
WRN-50*	68.88	11.44	81.45	95.53	config	model
WRN-101*	126.89	22.81	78.84	94.28	config	model
CSPDarkNet50*	27.64	5.04	80.05	95.07	config	model
CSPResNet50*	21.62	3.48	79.55	94.68	config	model
CSPResNeXt50*	20.57	3.11	79.96	94.96	config	model
DenseNet121*	7.98	2.88	74.96	92.21	config	model
DenseNet169*	14.15	3.42	76.08	93.11	config	model
DenseNet201*	20.01	4.37	77.32	93.64	config	model
DenseNet161*	28.68	7.82	77.61	93.83	config	model
VAN-T*	4.11	0.88	75.41	93.02	config	model
VAN-S*	13.86	2.52	81.01	95.63	config	model
VAN-B*	26.58	5.03	82.80	96.21	config	model
VAN-L*	44.77	8.99	83.86	96.73	config	model
MViTv2-tiny*	24.17	4.70	82.33	96.15	config	model
MViTv2-small*	34.87	7.00	83.63	96.51	config	model
MViTv2-base*	51.47	10.20	84.34	96.86	config	model
MViTv2-large*	217.99	42.10	85.25	97.14	config	model
EfficientFormer-l1*	12.19	1.30	80.46	94.99	config	model
EfficientFormer-l3*	31.41	3.93	82.45	96.18	config	model
EfficientFormer-l7*	82.23	10.16	83.40	96.60	config	model

Models with * are converted from other repos, others are trained by ourselves.

CIFAR10

Model	Params(M)	Flops(G)	Top-1 (%)	Config	Download
ResNet-18-b16x8	11.17	0.56	94.82		config
ResNet-34-b16x8	21.28	1.16	95.34		config
ResNet-50-b16x8	23.52	1.31	95.55		config
ResNet-101-b16x8	42.51	2.52	95.58		config
ResNet-152-b16x8	58.16	3.74	95.76		config