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其他模型

概述

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DarkNet53在预测时，图像的crop_size设置为256，resize_short_size设置为256；其余模型在预测时，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)
AlexNet	0.567	0.792	0.5720		1.370	61.090
SqueezeNet1_0	0.596	0.817	0.575		1.550	1.240
SqueezeNet1_1	0.601	0.819			0.690	1.230
VGG11	0.693	0.891			15.090	132.850
VGG13	0.700	0.894			22.480	133.030
VGG16	0.720	0.907	0.715	0.901	30.810	138.340
VGG19	0.726	0.909			39.130	143.650
DarkNet53	0.780	0.941	0.772	0.938	18.580	41.600
ResNet50_ACNet	0.767	0.932			10.730	33.110
ResNet50_ACNet _deploy	0.767	0.932			8.190	25.550

FP16预测速度

Models	batch_size=1 (ms)	batch_size=4 (ms)	batch_size=8 (ms)	batch_size=32 (ms)
AlexNet	0.684	0.740	0.810	1.481
SqueezeNet1_0	0.545	0.841	1.146	3.501
SqueezeNet1_1	0.473	0.575	0.805	1.862
VGG11	1.096	1.655	2.396	6.728
VGG13	1.216	2.059	3.056	9.468
VGG16	1.518	2.594	4.019	12.145
VGG19	1.817	3.124	4.886	14.958
DarkNet53	2.150	2.627	3.422	10.092
ResNet50_ACNet _deploy	2.748	3.178	3.823	8.369

FP32预测速度

Models	batch_size=1 (ms)	batch_size=4 (ms)	batch_size=8 (ms)	batch_size=32 (ms)
AlexNet	0.682	0.875	1.196	3.196
SqueezeNet1_0	0.530	1.072	1.652	5.338
SqueezeNet1_1	0.439	0.787	1.164	2.973
VGG11	1.575	3.638	6.427	23.227
VGG13	1.859	4.832	8.832	32.946
VGG16	2.316	6.420	11.936	44.719
VGG19	2.775	8.013	14.925	57.272
DarkNet53	2.648	5.727	9.616	33.664
ResNet50_ACNet _deploy	4.544	6.873	9.627	28.283