PaddleClas/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md

EfficientNet与ResNeXt101_wsl系列

概述

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该系列模型的FLOPS、参数量以及FP32预测耗时如下图所示。

精度、FLOPS和参数量

Models	Top1	Top5	Reference top1	Reference top5	FLOPS (G)	Parameters (M)
ResNeXt101_ 32x8d_wsl	0.826	0.967	0.822	0.964	29.140	78.440
ResNeXt101_ 32x16d_wsl	0.842	0.973	0.842	0.972	57.550	152.660
ResNeXt101_ 32x32d_wsl	0.850	0.976	0.851	0.975	115.170	303.110
ResNeXt101_ 32x48d_wsl	0.854	0.977	0.854	0.976	173.580	456.200
Fix_ResNeXt101_ 32x48d_wsl	0.863	0.980	0.864	0.980	354.230	456.200
EfficientNetB0	0.774	0.933	0.773	0.935	0.720	5.100
EfficientNetB1	0.792	0.944	0.792	0.945	1.270	7.520
EfficientNetB2	0.799	0.947	0.803	0.950	1.850	8.810
EfficientNetB3	0.812	0.954	0.817	0.956	3.430	11.840
EfficientNetB4	0.829	0.962	0.830	0.963	8.290	18.760
EfficientNetB5	0.836	0.967	0.837	0.967	19.510	29.610
EfficientNetB6	0.840	0.969	0.842	0.968	36.270	42.000
EfficientNetB7	0.843	0.969	0.844	0.971	72.350	64.920
EfficientNetB0_ small	0.758	0.926			0.720	4.650

FP32预测速度

Models	Crop Size	Resize Short Size	Batch Size=1 (ms)
ResNeXt101_ 32x8d_wsl	224	256	19.127
ResNeXt101_ 32x16d_wsl	224	256	23.629
ResNeXt101_ 32x32d_wsl	224	256	40.214
ResNeXt101_ 32x48d_wsl	224	256	59.714
Fix_ResNeXt101_ 32x48d_wsl	320	320	82.431
EfficientNetB0	224	256	2.449
EfficientNetB1	240	272	3.547
EfficientNetB2	260	292	3.908
EfficientNetB3	300	332	5.145
EfficientNetB4	380	412	7.609
EfficientNetB5	456	488	12.078
EfficientNetB6	528	560	18.381
EfficientNetB7	600	632	27.817
EfficientNetB0_ small	224	256	1.692