@ -2,10 +2,11 @@
An implementation of EfficienNet that covers variety of related models with efficient architectures : An implementation of EfficienNet that covers variety of related models with efficient architectures :
* EfficientNet ( B0 - B8 + Tensorflow pretrained AutoAug / RandAug / AdvProp weight ports ) * EfficientNet ( B0 - B8 , L2 + Tensorflow pretrained AutoAug / RandAug / AdvProp / NoisyStudent weight ports )
- EfficientNet : Rethinking Model Scaling for CNNs - https : / / arxiv . org / abs / 1905.11946
- EfficientNet : Rethinking Model Scaling for CNNs - https : / / arxiv . org / abs / 1905.11946
- CondConv : Conditionally Parameterized Convolutions for Efficient Inference - https : / / arxiv . org / abs / 1904.04971
- CondConv : Conditionally Parameterized Convolutions for Efficient Inference - https : / / arxiv . org / abs / 1904.04971
- Adversarial Examples Improve Image Recognition - https : / / arxiv . org / abs / 1911.09665
- Adversarial Examples Improve Image Recognition - https : / / arxiv . org / abs / 1911.09665
- Self - training with Noisy Student improves ImageNet classification - https : / / arxiv . org / abs / 1911.04252
* MixNet ( Small , Medium , and Large ) * MixNet ( Small , Medium , and Large )
- MixConv : Mixed Depthwise Convolutional Kernels - https : / / arxiv . org / abs / 1907.09595
- MixConv : Mixed Depthwise Convolutional Kernels - https : / / arxiv . org / abs / 1907.09595
@ -91,6 +92,8 @@ default_cfgs = {
url = ' ' , input_size = ( 3 , 600 , 600 ) , pool_size = ( 19 , 19 ) , crop_pct = 0.949 ) ,
url = ' ' , input_size = ( 3 , 600 , 600 ) , pool_size = ( 19 , 19 ) , crop_pct = 0.949 ) ,
' efficientnet_b8 ' : _cfg (
' efficientnet_b8 ' : _cfg (
url = ' ' , input_size = ( 3 , 672 , 672 ) , pool_size = ( 21 , 21 ) , crop_pct = 0.954 ) ,
url = ' ' , input_size = ( 3 , 672 , 672 ) , pool_size = ( 21 , 21 ) , crop_pct = 0.954 ) ,
' efficientnet_l2 ' : _cfg (
url = ' ' , input_size = ( 3 , 800 , 800 ) , pool_size = ( 25 , 25 ) , crop_pct = 0.961 ) ,
' efficientnet_es ' : _cfg (
' efficientnet_es ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth ' ) ,
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth ' ) ,
' efficientnet_em ' : _cfg (
' efficientnet_em ' : _cfg (
@ -162,6 +165,36 @@ default_cfgs = {
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth ' ,
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth ' ,
mean = IMAGENET_INCEPTION_MEAN , std = IMAGENET_INCEPTION_STD ,
mean = IMAGENET_INCEPTION_MEAN , std = IMAGENET_INCEPTION_STD ,
input_size = ( 3 , 672 , 672 ) , pool_size = ( 21 , 21 ) , crop_pct = 0.954 ) ,
input_size = ( 3 , 672 , 672 ) , pool_size = ( 21 , 21 ) , crop_pct = 0.954 ) ,
' tf_efficientnet_b0_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth ' ,
input_size = ( 3 , 224 , 224 ) ) ,
' tf_efficientnet_b1_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth ' ,
input_size = ( 3 , 240 , 240 ) , pool_size = ( 8 , 8 ) , crop_pct = 0.882 ) ,
' tf_efficientnet_b2_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth ' ,
input_size = ( 3 , 260 , 260 ) , pool_size = ( 9 , 9 ) , crop_pct = 0.890 ) ,
' tf_efficientnet_b3_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth ' ,
input_size = ( 3 , 300 , 300 ) , pool_size = ( 10 , 10 ) , crop_pct = 0.904 ) ,
' tf_efficientnet_b4_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth ' ,
input_size = ( 3 , 380 , 380 ) , pool_size = ( 12 , 12 ) , crop_pct = 0.922 ) ,
' tf_efficientnet_b5_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth ' ,
input_size = ( 3 , 456 , 456 ) , pool_size = ( 15 , 15 ) , crop_pct = 0.934 ) ,
' tf_efficientnet_b6_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth ' ,
input_size = ( 3 , 528 , 528 ) , pool_size = ( 17 , 17 ) , crop_pct = 0.942 ) ,
' tf_efficientnet_b7_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth ' ,
input_size = ( 3 , 600 , 600 ) , pool_size = ( 19 , 19 ) , crop_pct = 0.949 ) ,
' tf_efficientnet_l2_ns_475 ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth ' ,
input_size = ( 3 , 475 , 475 ) , pool_size = ( 15 , 15 ) , crop_pct = 0.936 ) ,
' tf_efficientnet_l2_ns ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth ' ,
input_size = ( 3 , 800 , 800 ) , pool_size = ( 25 , 25 ) , crop_pct = 0.961 ) ,
' tf_efficientnet_es ' : _cfg (
' tf_efficientnet_es ' : _cfg (
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth ' ,
url = ' https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth ' ,
mean = ( 0.5 , 0.5 , 0.5 ) , std = ( 0.5 , 0.5 , 0.5 ) ,
mean = ( 0.5 , 0.5 , 0.5 ) , std = ( 0.5 , 0.5 , 0.5 ) ,
@ -208,7 +241,7 @@ class EfficientNet(nn.Module):
""" (Generic) EfficientNet
""" (Generic) EfficientNet
A flexible and performant PyTorch implementation of efficient network architectures , including :
A flexible and performant PyTorch implementation of efficient network architectures , including :
* EfficientNet B0 - B8
* EfficientNet B0 - B8 , L2
* EfficientNet - EdgeTPU
* EfficientNet - EdgeTPU
* EfficientNet - CondConv
* EfficientNet - CondConv
* MixNet S , M , L , XL
* MixNet S , M , L , XL
@ -586,6 +619,7 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
' efficientnet-b6 ' : ( 1.8 , 2.6 , 528 , 0.5 ) ,
' efficientnet-b6 ' : ( 1.8 , 2.6 , 528 , 0.5 ) ,
' efficientnet-b7 ' : ( 2.0 , 3.1 , 600 , 0.5 ) ,
' efficientnet-b7 ' : ( 2.0 , 3.1 , 600 , 0.5 ) ,
' efficientnet-b8 ' : ( 2.2 , 3.6 , 672 , 0.5 ) ,
' efficientnet-b8 ' : ( 2.2 , 3.6 , 672 , 0.5 ) ,
' efficientnet-l2 ' : ( 4.3 , 5.3 , 800 , 0.5 ) ,
Args :
Args :
channel_multiplier : multiplier to number of channels per layer
channel_multiplier : multiplier to number of channels per layer
@ -928,6 +962,24 @@ def efficientnet_b7(pretrained=False, **kwargs):
return model
return model
@register_model
def efficientnet_b8 ( pretrained = False , * * kwargs ) :
""" EfficientNet-B8 """
# NOTE for train, drop_rate should be 0.5, drop_connect_rate should be 0.2
model = _gen_efficientnet (
' efficientnet_b8 ' , channel_multiplier = 2.2 , depth_multiplier = 3.6 , pretrained = pretrained , * * kwargs )
return model
@register_model
def efficientnet_l2 ( pretrained = False , * * kwargs ) :
""" EfficientNet-L2. """
# NOTE for train, drop_rate should be 0.5, drop_connect_rate should be 0.2
model = _gen_efficientnet (
' efficientnet_l2 ' , channel_multiplier = 4.3 , depth_multiplier = 5.3 , pretrained = pretrained , * * kwargs )
return model
@register_model @register_model
def efficientnet_es ( pretrained = False , * * kwargs ) : def efficientnet_es ( pretrained = False , * * kwargs ) :
""" EfficientNet-Edge Small. """
""" EfficientNet-Edge Small. """
@ -1166,6 +1218,109 @@ def tf_efficientnet_b8_ap(pretrained=False, **kwargs):
return model
return model
@register_model
def tf_efficientnet_b0_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B0 NoisyStudent. Tensorflow compatible variant """
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b0_ns ' , channel_multiplier = 1.0 , depth_multiplier = 1.0 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b1_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B1 NoisyStudent. Tensorflow compatible variant """
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b1_ns ' , channel_multiplier = 1.0 , depth_multiplier = 1.1 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b2_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B2 NoisyStudent. Tensorflow compatible variant """
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b2_ns ' , channel_multiplier = 1.1 , depth_multiplier = 1.2 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b3_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B3 NoisyStudent. Tensorflow compatible variant """
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b3_ns ' , channel_multiplier = 1.2 , depth_multiplier = 1.4 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b4_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B4 NoisyStudent. Tensorflow compatible variant """
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b4_ns ' , channel_multiplier = 1.4 , depth_multiplier = 1.8 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b5_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B5 NoisyStudent. Tensorflow compatible variant """
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b5_ns ' , channel_multiplier = 1.6 , depth_multiplier = 2.2 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b6_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B6 NoisyStudent. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b6_ns ' , channel_multiplier = 1.8 , depth_multiplier = 2.6 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_b7_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-B7 NoisyStudent. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_b7_ns ' , channel_multiplier = 2.0 , depth_multiplier = 3.1 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_l2_ns_475 ( pretrained = False , * * kwargs ) :
""" EfficientNet-L2 NoisyStudent @ 475x475. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_l2_ns_475 ' , channel_multiplier = 4.3 , depth_multiplier = 5.3 , pretrained = pretrained , * * kwargs )
return model
@register_model
def tf_efficientnet_l2_ns ( pretrained = False , * * kwargs ) :
""" EfficientNet-L2 NoisyStudent. Tensorflow compatible variant """
# NOTE for train, drop_rate should be 0.5
kwargs [ ' bn_eps ' ] = BN_EPS_TF_DEFAULT
kwargs [ ' pad_type ' ] = ' same '
model = _gen_efficientnet (
' tf_efficientnet_l2_ns ' , channel_multiplier = 4.3 , depth_multiplier = 5.3 , pretrained = pretrained , * * kwargs )
return model
@register_model @register_model
def tf_efficientnet_es ( pretrained = False , * * kwargs ) : def tf_efficientnet_es ( pretrained = False , * * kwargs ) :
Ross Wightman
5 years ago