Benchmark

Speed (images/sec)

model	batch size 10	batch size 20	batch size 30	batch size 40
CaffeNet	755.64	1054.47	1019.24	1077.63
SqueezeNet	458.27	579.37	534.68	549.55
NIN	443.88	516.21	612.83	656.14
ResNet-18	257.40	308.30	331.57	339.09
ResNet-34	149.88	182.69	201.75	207.49
Inception-BN	147.60	183.82	193.74	203.86
ResNet-50	88.55	102.44	109.98	111.04
Inception-v3	67.11	75.90	80.67	82.34
VGG16	56.38	58.01	59.80	59.35
ResNet-101	53.42	63.28	68.14	68.35
VGG19	45.02	46.88	48.62	48.28
ResNet-152	37.88	44.89	48.48	48.62
ResNet-200	22.58	25.61	27.17	27.32
ResNeXt-50	53.30	64.40	71.07	72.96
ResNeXt-101	31.76	39.56	42.90	43.99
ResNeXt-101-64x4d	18.22	23.08	out of memory	out of memory

model	batch size 10	batch size 20	batch size 30	batch size 40	Reference accuracy (imagenet1k Top-5)
CaffeNet	430	496	631	716	78.3%
SqueezeNet	608	937	1331	1672	78.8%
NIN	650	902	1062	1222	81.3%
ResNet-18	814	1163	1497	1853	88.7%
ResNet-34	1127	1619	2094	2598	91.0%
Inception-BN	1007	1569	2212	2772	90.8%
ResNet-50	1875	3080	4265	5483	92.6%
Inception-v3	2075	3509	4944	6383	93.3%
VGG16	1738	2960	4751	5977	89.8%
ResNet-101	2791	4576	6341	8158	93.3%
VGG19	1920	3242	5133	6458	89.8%
ResNet-152	3790	6296	8777	11330	93.1%
ResNet-200	2051	2769	3471	4201	unknown
ResNeXt-50	2248	3863	5468	7089	93.3%
ResNeXt-101	3350	5749	8126	10539	94.1%
ResNeXt-101-64x4d	5140	8679	out of memory	out of memory	94.3%