https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html
 |
| Example use cases include detection, fine-grain classification, attributes and geo-localization. |
|
Model Checkpoint
|
Million MACs
|
Million Parameters
|
Top-1 Accuracy
|
Top-5 Accuracy
|
|
569
|
4.24
|
70.7
|
89.5
|
|
|
418
|
4.24
|
69.3
|
88.9
|
|
|
291
|
4.24
|
67.2
|
87.5
|
|
|
186
|
4.24
|
64.1
|
85.3
|
|
|
317
|
2.59
|
68.4
|
88.2
|
|
|
233
|
2.59
|
67.4
|
87.3
|
|
|
162
|
2.59
|
65.2
|
86.1
|
|
|
104
|
2.59
|
61.8
|
83.6
|
|
|
150
|
1.34
|
64.0
|
85.4
|
|
|
110
|
1.34
|
62.1
|
84.0
|
|
|
77
|
1.34
|
59.9
|
82.5
|
|
|
49
|
1.34
|
56.2
|
79.6
|
|
|
41
|
0.47
|
50.6
|
75.0
|
|
|
34
|
0.47
|
49.0
|
73.6
|
|
|
21
|
0.47
|
46.0
|
70.7
|
|
|
14
|
0.47
|
41.3
|
66.2
|
| Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. Top-1 and Top-5 accuracies are measured on the ILSVRC dataset. |
MobileNets: Open-Source Models for Efficient On-Device Vision
原文：http://www.cnblogs.com/antflow/p/7255109.html