SEResNeXt与Res2Net系列

概述

所有模型在预测时，图像的crop_size设置为224，resize_short_size设置为256。

更多的模型概述正在持续更新中。

Models	Top1	Top5	Reference top1	Reference top5	FLOPS (G)	Parameters (M)
Res2Net50_26w_4s	0.793	0.946	0.780	0.936	8.520	25.700
Res2Net50_vd_26w_4s	0.798	0.949			8.370	25.060
Res2Net50_14w_8s	0.795	0.947	0.781	0.939	9.010	25.720
Res2Net101_vd_26w_4s	0.806	0.952			16.670	45.220
Res2Net200_vd_26w_4s	0.812	0.957			31.490	76.210
ResNeXt50_32x4d	0.778	0.938	0.778		8.020	23.640
ResNeXt50_vd_32x4d	0.796	0.946			8.500	23.660
ResNeXt50_64x4d	0.784	0.941			15.060	42.360
ResNeXt50_vd_64x4d	0.801	0.949			15.540	42.380
ResNeXt101_32x4d	0.787	0.942	0.788		15.010	41.540
ResNeXt101_vd_32x4d	0.803	0.951			15.490	41.560
ResNeXt101_64x4d	0.784	0.945	0.796		29.050	78.120
ResNeXt101_vd_64x4d	0.808	0.952			29.530	78.140
ResNeXt152_32x4d	0.790	0.943			22.010	56.280
ResNeXt152_vd_32x4d	0.807	0.952			22.490	56.300
ResNeXt152_64x4d	0.795	0.947			43.030	107.570
ResNeXt152_vd_64x4d	0.811	0.953			43.520	107.590
SE_ResNet18_vd	0.733	0.914			4.140	11.800
SE_ResNet34_vd	0.765	0.932			7.840	21.980
SE_ResNet50_vd	0.795	0.948			8.670	28.090
SE_ResNeXt50_32x4d	0.784	0.940	0.789	0.945	8.020	26.160
SE_ResNeXt50_vd_32x4d	0.802	0.949			10.760	26.280
SE_ResNeXt101_32x4d	0.791	0.942	0.793	0.950	15.020	46.280
SENet154_vd	0.814	0.955			45.830	114.290

Models	batch_size=1 (ms)	batch_size=4 (ms)	batch_size=8 (ms)	batch_size=32 (ms)
Res2Net50_26w_4s	2.625	3.338	4.670	11.939
Res2Net50_vd_26w_4s	2.642	3.480	4.862	13.089
Res2Net50_14w_8s	3.393	4.237	5.473	13.979
Res2Net101_vd_26w_4s	5.128	6.190	7.995	20.534
Res2Net200_vd_26w_4s	9.594	11.131	14.278	36.258
ResNeXt50_32x4d	6.795	7.102	8.444	18.938
ResNeXt50_vd_32x4d	7.455	7.231	8.891	19.849
ResNeXt50_64x4d	20.279	12.343	13.633	32.772
ResNeXt50_vd_64x4d	16.325	21.773	25.007	55.329
ResNeXt101_32x4d	14.847	15.092	15.847	42.681
ResNeXt101_vd_32x4d	15.227	15.139	16.603	39.371
ResNeXt101_64x4d	28.221	29.455	29.873	59.415
ResNeXt101_vd_64x4d	31.051	28.160	28.915	60.525
ResNeXt152_32x4d	22.961	23.167	24.173	51.621
ResNeXt152_vd_32x4d	23.259	23.469	23.886	52.085
ResNeXt152_64x4d	41.930	42.441	45.985	79.405
ResNeXt152_vd_64x4d	42.778	43.281	45.017	79.728
SE_ResNet18_vd	1.256	1.463	1.917	4.316
SE_ResNet34_vd	2.314	2.691	3.432	7.411
SE_ResNet50_vd	2.884	4.051	5.421	15.013
SE_ResNeXt50_32x4d	7.973	10.613	12.788	29.091
SE_ResNeXt50_vd_32x4d	8.340	12.245	15.253	30.399
SE_ResNeXt101_32x4d	17.324	21.004	28.541	52.888
SENet154_vd	47.234	48.018	52.967	109.787

Models	batch_size=1 (ms)	batch_size=4 (ms)	batch_size=8 (ms)	batch_size=32 (ms)
Res2Net50_26w_4s	3.711	5.855	8.450	26.084
Res2Net50_vd_26w_4s	3.651	5.986	8.747	26.772
Res2Net50_14w_8s	4.549	6.863	9.492	27.049
Res2Net101_vd_26w_4s	6.658	10.870	15.364	47.054
Res2Net200_vd_26w_4s	12.017	19.871	28.330	88.645
ResNeXt50_32x4d	6.747	8.862	11.961	32.782
ResNeXt50_vd_32x4d	6.746	9.037	12.279	33.496
ResNeXt50_64x4d	11.577	14.570	20.425	57.979
ResNeXt50_vd_64x4d	19.219	21.454	30.943	90.950
ResNeXt101_32x4d	14.652	18.082	24.148	70.200
ResNeXt101_vd_32x4d	14.927	18.454	23.894	67.334
ResNeXt101_64x4d	28.726	30.999	43.169	116.282
ResNeXt101_vd_64x4d	28.350	31.186	41.315	113.655
ResNeXt152_32x4d	23.578	27.323	35.588	99.121
ResNeXt152_vd_32x4d	23.548	26.879	35.091	104.832
ResNeXt152_64x4d	43.214	43.339	60.990	159.381
ResNeXt152_vd_64x4d	43.998	44.510	61.094	160.601
SE_ResNet18_vd	1.353	1.867	3.021	9.331
SE_ResNet34_vd	2.421	3.201	5.294	16.849
SE_ResNet50_vd	3.403	6.023	8.721	26.978
SE_ResNeXt50_32x4d	8.339	12.689	15.471	41.562
SE_ResNeXt50_vd_32x4d	7.849	13.530	16.810	44.020
SE_ResNeXt101_32x4d	16.853	24.409	32.666	81.806
SENet154_vd	46.002	53.666	70.589	180.334