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DPN与DenseNet系列

概述

模型概述正在持续更新中。所有模型在预测时，图像的crop_size设置为224，resize_short_size设置为256。

精度、FLOPS和参数量

Models	Top1	Top5	Reference top1	Reference top5	FLOPS (G)	Parameters (M)
DenseNet121	0.757	0.926	0.750		5.690	7.980
DenseNet161	0.786	0.941	0.778		15.490	28.680
DenseNet169	0.768	0.933	0.764		6.740	14.150
DenseNet201	0.776	0.937	0.775		8.610	20.010
DenseNet264	0.780	0.939	0.779		11.540	33.370
DPN68	0.768	0.934	0.764	0.931	4.030	10.780
DPN92	0.799	0.948	0.793	0.946	12.540	36.290
DPN98	0.806	0.951	0.799	0.949	22.220	58.460
DPN107	0.809	0.953	0.802	0.951	35.060	82.970
DPN131	0.807	0.951	0.801	0.949	30.510	75.360

FP16预测速度

Models	batch_size=1 (ms)	batch_size=4 (ms)	batch_size=8 (ms)	batch_size=32 (ms)
DenseNet121	3.653	4.560	5.574	11.517
DenseNet161	7.826	8.936	10.970	22.554
DenseNet169	5.625	6.698	7.876	14.983
DenseNet201	7.243	8.537	10.111	18.928
DenseNet264	10.882	12.539	14.645	26.455
DPN68	10.310	11.060	14.299	29.618
DPN92	16.335	17.373	23.197	45.210
DPN98	18.975	23.073	28.902	66.280
DPN107	24.932	28.607	37.513	89.112
DPN131	25.425	29.874	37.355	88.583

FP32预测速度

Models	batch_size=1 (ms)	batch_size=4 (ms)	batch_size=8 (ms)	batch_size=32 (ms)
DenseNet121	3.732	6.614	8.517	21.755
DenseNet161	8.282	14.438	19.336	51.953
DenseNet169	5.705	10.074	12.432	28.756
DenseNet201	7.315	13.830	16.941	38.654
DenseNet264	10.986	21.460	25.724	56.501
DPN68	10.357	11.025	14.903	34.380
DPN92	16.067	21.315	26.176	62.126
DPN98	18.455	26.710	36.009	104.084
DPN107	24.164	37.691	51.307	148.041
DPN131	24.761	35.806	48.401	133.233