轻量级神经网络算法-总结对比

4. 轻量级神经网络算法目录

4. 轻量级神经网络算法
   4.1 各轻量级神经网络算法总结对比
   4.2 SqueezeNet
   4.3 DenseNet
   4.4 Xception
   4.5 MobileNet v1
   4.6 IGCV
   4.7 NASNet
   4.8 CondenseNet
   4.9 PNASNet
   4.10 SENet
   4.11 ShuffleNet v1
   4.12 MobileNet v2
   4.13 AmoebaNet
   4.14 IGCV2
   4.15 IGCV3
   4.16 ShuffleNet v2
   4.17 MnasNet
   4.18 MobileNet v3

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4.1 各轻量级神经网络算法对比

轻量级神经网络准确率、Params、MAdds、推理时间等对比，对比数据集:ImageNet 2012 classification dataset。

Date	Model	Detail	Top-1 Acc. (%)	Top-5 Acc. (%)	Params(M)	MAdds(M)	Infer-time(ms)
2016.2	SqueezeNet		67.5	88.2	3.2	708
2016.8	DenseNet	DenseNet(0.5)	41.4			42	25
		DenseNet(1.0)	44.8			142	63
		DenseNet(1.5)	60.1			295	103
		DenseNet(2.0)	65.4			519	164
2016.1	Xception	Xception(0.5)	55.1			40	19
		Xception(1.0)	65.9			145	51
		Xception(1.5)	70.6			305	95
		Xception(2.0)	72.4			525	149
2017.4	MobileNet v1	MobileNet v1(0.25)	50.6		0.5	41	27
		MobileNet v1(0.5)	63.7		1.3	149	60
		MobileNet v1(0.75)	68.4		2.6	325	94
		MobileNet v1(1.0)	70.6	89.5	4.2	569	154
2017.7.10	IGCV
2017.7.21	NASNet	NASNet-A	74	91.3	5.3	564	183
2017.11	CondenseNet	CondenseNet(G=C=4)	71	90	2.9	274
		CondenseNet(G=C=8)	73.8	91.7	4.8	529
2017.12	PNASNet	PNASNet	74.2	91.9	5.1	588
2017.9	SENet
2017.12	ShuffleNet v1	ShuffleNet(0.5)	56.8			38	18
		ShuffleNet v1(1.0)-g=3	67.4			140	46
		ShuffleNet v1(1.5)-g=3	71.5	-	3.4	292	97
		ShuffleNet v1(x2)-g=3	73.7	-	5.4	524	156
2018.1	MobileNet v2	MobileNet v2(0.35)	60.8		1.6	59.2	16.6/19.6/13.9(Pixel*)
		MobileNet v2(1.0)	72	91	3.4	300	75(Pixel 1 Phone)
		MobileNet v2(1.4)	74.7	92.5	6.9	585	143(Pixel 1 Phone)
2018.2	AmoebaNet	AmoebaNet-A	74.5	92	5.1	555	190
2018.4	IGCV2	IGCV2-0.25	54.9		0.5	46	32
		IGCV2-0.5	65.5		1.3	156	65
		IGCV2-1.0	70.7		4.1	564	204
2018.6	IGCV3	IGCV3-0.7	68.45		2.8	210	85
		IGCV3-1.0	72.2		3.5	318	159
		IGCV3-1.4	74.55		7.2	610	222
2018.7	ShuffleNet v2	ShuffleNet v2(0.5)	60.3		1.4	41	18
		ShuffleNet v2(1.0)	69.4		2.3	146	41
		ShuffleNet v2(1.5)	72.6		3.5	299	85
		ShuffleNet v2(x2)	74.9		7.4	597	149
		ShuffleNet v2(x2)-SE	75.4			597	179
2019.3	MnasNet	MnasNet-Small	64.9		1.9	65.1	20.3/24.2/17.2
		MnasNet-A1	75.2	92.5	3.9	312	78(Pixel 1 Phone)
		MnasNet-A2	75.6	92.7	4.8	340	84(Pixel 1 Phone)
		MnasNet-A3	76.7	93.3	5.2	403	103(Pixel 1 Phone)
2019.5.6	MobileNet v3	MobileNet v3-Large(1.0)	75.2		5.4	219	51/61/44(Pixel*)
		MobileNet v3-Large(0.75)	73.3		4	155	39/46/40(Pixel*)
		MobileNet v3-Small(1.0)	67.4		2.5	56	15.8/19.4/14.4(Pixel*)
		MobileNet v3-Small(0.75)	65.4		2	44	12.8/15.6/11.7(Pixel*)

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总结

以上就是关于轻量级神经网络算法的对比结果，点击Model列的算法可以详细了解各个算法。