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hub / github.com/cure-lab/deep-active-learning / ShuffleNetG2

Function ShuffleNetG2

models/shufflenet.py:98–106  ·  view source on GitHub ↗
(channels=3,num_classes=10, dropout=False)

Source from the content-addressed store, hash-verified

96
97
98def ShuffleNetG2(channels=3,num_classes=10, dropout=False):
99 cfg = {
100 'out_planes': [200,400,800],
101 'num_blocks': [4,8,4],
102 'groups': 2
103 }
104 # print (cfg)
105 # print (channels, num_classes)
106 return ShuffleNet(channels=channels,num_classes=num_classes, dropout=dropout, cfg=cfg)
107
108def ShuffleNetG3(channels=3, num_classes=10):
109 cfg = {

Callers 1

testFunction · 0.85

Calls 1

ShuffleNetClass · 0.85

Tested by

no test coverage detected