Model Zoo for Face Recognition

测试集

模型名称	LFW精度(ZQCNN)	耗时(ZQCNN)	备注
MobileFaceNet-v0	99.13%-99.23%	单线程9-10ms, 四线程6-7ms，3.6GHz，MKL	从model-y1.zip转的格式
MobileFaceNet-v1	99.17%-99.37%	单线程9-10ms, 四线程6-7ms，3.6GHz，MKL	我自己用insightface训练了一把
MobileFaceNet-GNAP	99.38%-99.43%	单线程9-10ms, 四线程6-7ms，3.6GHz，MKL	感谢YaqiLYU训练此模型
ArcFace-r34	99.65%-99.70%	单线程131ms，四线程53ms，3.6GHz，MKL	这个模型是ms1m-refine-v1训练的
ArcFace-r34-v2	99.68%-99.78%	单线程131ms，四线程53ms，3.6GHz，MKL	这个模型是ms1m-refine-v2训练的
ArcFace-r50	99.75%-99.78%	单线程175ms，四线程70ms, 3.6GHz, MKL	这个模型是ms1m-refine-v1训练的
ArcFace-r100	99.80%-99.82%	单线程327ms，四线程125ms, 3.6GHz, MKL	这个模型是ms1m-refine-v2训练的

模型名称	LFW精度(ZQCNN)	LFW精度(OpenCV3.4.2)	LFW精度(minicaffe)	耗时 (ZQCNN)	备注
MobileFaceNet-res2-6-10-2-dim128	99.67%-99.55%(matlab crop), 99.72-99.60%(C++ crop)	99.63%-99.65%(matlab crop), 99.68-99.70%(C++ crop)	99.62%-99.65%(matlab crop), 99.68-99.60%(C++ crop)	时间与dim256接近	网络结构与dim256一样，只不过输出维数不同
MobileFaceNet-res2-6-10-2-dim256	99.60%-99.60%(matlab crop), 99.62-99.62%(C++ crop)	99.73%-99.68%(matlab crop), 99.78-99.68%(C++ crop)	99.55%-99.63%(matlab crop), 99.60-99.62%(C++ crop)	单线程约21-22ms，四线程约11-12ms, 3.6GHz,MKL	网络结构在下载链接里,用faces_emore训练的
MobileFaceNet-res2-6-10-2-dim512	99.52%-99.60%(matlab crop), 99.63-99.72%(C++ crop)	99.70%-99.67%(matlab crop), 99.77-99.77%(C++ crop)	99.55%-99.62%(matlab crop), 99.62-99.68%(C++ crop)	时间与dim256接近	网络结构与dim256一样，只不过输出维数不同。感谢moli训练此模型

模型名称	LFW精度(ZQCNN)	LFW精度(OpenCV3.4.2)	LFW精度(minicaffe)	耗时 (ZQCNN)	备注
MobileFaceNet-res4-8-16-4-dim128	99.72%-99.72%(matlab crop), 99.72-99.68%(C++ crop)	99.82%-99.83%(matlab crop), 99.80-99.78%(C++ crop)	99.72%-99.72%(matlab crop), 99.72-99.68%(C++ crop)	时间与dim256接近	网络结构与dim256一样，只不过输出维数不同
MobileFaceNet-res4-8-16-4-dim256	99.78%-99.78%(matlab crop), 99.75-99.75%(C++ crop)	99.82%-99.82%(matlab crop), 99.80-99.82%(C++ crop)	99.78%-99.78%(matlab crop), 99.73-99.73%(C++ crop)	单线程约32-33ms，四线程约16-19ms, 3.6GHz,MKL	网络结构在下载链接里,用faces_emore训练的
MobileFaceNet-res4-8-16-4-dim512	99.80%-99.73%(matlab crop), 99.85-99.83%(C++ crop)	99.83%-99.82%(matlab crop), 99.87-99.83%(C++ crop)	99.80%-99.73%(matlab crop), 99.85-99.82%(C++ crop)	时间与dim256接近	网络结构与dim256一样，只不过输出维数不同。感谢moli训练此模型

模型\测试集webface1000X50	thresh@ FAR=1e-7	TAR@ FAR=1e-7	thresh@ FAR=1e-6	TAR@ FAR=1e-6	thresh@ FAR=1e-5	TAR@ FAR=1e-5
MobileFaceNet-res2-6-10-2-dim128	0.78785	9.274%	0.66616	40.459%	0.45855	92.716%
MobileFaceNet-res2-6-10-2-dim256	0.77708	7.839%	0.63872	40.934%	0.43182	92.605%
MobileFaceNet-res2-6-10-2-dim512	0.76699	8.197%	0.63452	38.774%	0.41572	93.000%
MobileFaceNet-res4-8-16-4-dim128	0.79268	9.626%	0.65770	48.252%	0.45431	95.576%
MobileFaceNet-res4-8-16-4-dim256	0.76858	9.220%	0.62852	46.195%	0.40010	96.929%
MobileFaceNet-res4-8-16-4-dim512	0.76287	9.296%	0.62555	44.775%	0.39047	97.347%

模型\测试集webface5000X20	thresh@ FAR=1e-7	TAR@ FAR=1e-7	thresh@ FAR=1e-6	TAR@ FAR=1e-6	thresh@ FAR=1e-5	TAR@ FAR=1e-5
MobileFaceNet-res2-6-10-2-dim128	0.70933	29.558%	0.51732	85.160%	0.45108	94.313%
MobileFaceNet-res2-6-10-2-dim256	0.68897	28.376%	0.48820	85.278%	0.42386	94.244%
MobileFaceNet-res2-6-10-2-dim512	0.68126	27.708%	0.47260	85.840%	0.40727	94.632%
MobileFaceNet-res4-8-16-4-dim128	0.71238	32.153%	0.51391	89.525%	0.44667	96.583%
MobileFaceNet-res4-8-16-4-dim256	0.68490	30.639%	0.46092	91.900%	0.39198	97.696%
MobileFaceNet-res4-8-16-4-dim512	0.67303	32.404%	0.45216	92.453%	0.38344	98.003%

模型\测试集TAO ids:6606,ims:87210	thresh@ FAR=1e-7	TAR@ FAR=1e-7	thresh@ FAR=1e-6	TAR@ FAR=1e-6	thresh@ FAR=1e-5	TAR@ FAR=1e-5
MobileFaceNet-res2-6-10-2-dim128	0.92204	01.282%	0.88107	06.837%	0.78302	41.740%
MobileFaceNet-res2-6-10-2-dim256	0.91361	01.275%	0.86750	07.081%	0.76099	42.188%
MobileFaceNet-res2-6-10-2-dim512	0.90657	01.448%	0.86061	07.299%	0.75488	41.956%
MobileFaceNet-res4-8-16-4-dim128	0.92098	01.347%	0.88233	06.795%	0.78711	41.856%
MobileFaceNet-res4-8-16-4-dim256	0.90862	01.376%	0.86397	07.083%	0.75975	42.430%
MobileFaceNet-res4-8-16-4-dim512	0.90710	01.353%	0.86190	06.948%	0.75518	42.241%

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