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Add pretrained weights for FBNet-C

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Ross Wightman 2019-05-20 11:05:25 -07:00
parent a72e9b67ee
commit a4516fe0fb
2 changed files with 5 additions and 1 deletions
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1
README.md
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@ -118,6 +118,7 @@ I've leveraged the training scripts in this repository to train a few of the mod
| SE-ResNeXt-26 (32x4d) | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic | | SE-ResNeXt-26 (32x4d) | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
| SE-ResNet-34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear | | SE-ResNet-34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear |
| SE-ResNet-18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic | | SE-ResNet-18 | 71.742 (28.258) | 90.334 (9.666) | 11.8M | bicubic |
| FBNet-C | 74.830 (25.170 | 92.124 (7.876) | 5.6M | bilinear |
| Single-Path NASNet 1.00 | 74.084 (25.916) | 91.818 (8.182) | 4.42M | bilinear | | Single-Path NASNet 1.00 | 74.084 (25.916) | 91.818 (8.182) | 4.42M | bilinear |
### Ported Weights ### Ported Weights

5
models/genmobilenet.py
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@ -63,7 +63,7 @@ default_cfgs = {
'mobilenetv3_100': _cfg(url=''), 'mobilenetv3_100': _cfg(url=''),
'chamnetv1_100': _cfg(url=''), 'chamnetv1_100': _cfg(url=''),
'chamnetv2_100': _cfg(url=''), 'chamnetv2_100': _cfg(url=''),
'fbnetc_100': _cfg(url=''), 'fbnetc_100': _cfg(url='https://www.dropbox.com/s/0ku2tztuibrynld/fbnetc_100-f49a0c5f.pth?dl=1'),
'spnasnet_100': _cfg(url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'), 'spnasnet_100': _cfg(url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1'),
} }
@ -1227,6 +1227,9 @@ def mobilenetv3_100(num_classes, in_chans=3, pretrained=False, **kwargs):
def fbnetc_100(num_classes, in_chans=3, pretrained=False, **kwargs): def fbnetc_100(num_classes, in_chans=3, pretrained=False, **kwargs):
""" FBNet-C """ """ FBNet-C """
default_cfg = default_cfgs['fbnetc_100'] default_cfg = default_cfgs['fbnetc_100']
if pretrained:
# pretrained model trained with non-default BN epsilon
kwargs['bn_eps'] = _BN_EPS_TF_DEFAULT
model = _gen_fbnetc(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs) model = _gen_fbnetc(1.0, num_classes=num_classes, in_chans=in_chans, **kwargs)
model.default_cfg = default_cfg model.default_cfg = default_cfg
if pretrained: if pretrained: