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Add updated EfficientNet-V2S weights, 83.8 @ 384x384 test. Add PyTorch trained EfficientNet-B4 weights, 83.4 @ 384x384 test. Tweak non TF EfficientNet B1-B4 train/test res scaling.

pull/571/head
Ross Wightman 2021-04-19 10:42:56 -07:00
parent e8a64fb881
commit c1cf9712fc
1 changed files with 11 additions and 20 deletions
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31
timm/models/efficientnet.py
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@ -85,21 +85,16 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth'),
'efficientnet_b1': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth',
input_size=(3, 240, 240), pool_size=(8, 8)),
test_input_size=(3, 256, 256), crop_pct=1.0),
'efficientnet_b2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
input_size=(3, 260, 260), pool_size=(9, 9)),
'efficientnet_b2a': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth',
input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0),
input_size=(3, 256, 256), pool_size=(8, 8), test_input_size=(3, 288, 288), crop_pct=1.0),
'efficientnet_b3': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904),
'efficientnet_b3a': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth',
input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0),
input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0),
'efficientnet_b4': _cfg(
url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b4_ra2_320-7eb33cd5.pth',
input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), crop_pct=1.0),
'efficientnet_b5': _cfg(
url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934),
'efficientnet_b6': _cfg(
@ -155,8 +150,8 @@ default_cfgs = {
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_v2s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2-b265c1ba.pth',
input_size=(3, 224, 224), test_input_size=(3, 320, 320), pool_size=(7, 7), crop_pct=1.0), # FIXME WIP
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth',
input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), # FIXME WIP
'tf_efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
@ -1077,10 +1072,8 @@ def efficientnet_b2(pretrained=False, **kwargs):
@register_model
def efficientnet_b2a(pretrained=False, **kwargs):
""" EfficientNet-B2 @ 288x288 w/ 1.0 test crop"""
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b2a', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
# WARN this model def is deprecated, different train/test res + test crop handled by default_cfg now
return efficientnet_b2(pretrained=pretrained, **kwargs)
@register_model
@ -1095,10 +1088,8 @@ def efficientnet_b3(pretrained=False, **kwargs):
@register_model
def efficientnet_b3a(pretrained=False, **kwargs):
""" EfficientNet-B3 @ 320x320 w/ 1.0 test crop-pct """
# NOTE for train, drop_rate should be 0.3, drop_path_rate should be 0.2
model = _gen_efficientnet(
'efficientnet_b3a', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
# WARN this model def is deprecated, different train/test res + test crop handled by default_cfg now
return efficientnet_b3(pretrained=pretrained, **kwargs)
@register_model