Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

First set of MobileNetV4 weights trained in timm #2202

Merged
merged 2 commits into from
Jun 12, 2024
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
48 changes: 24 additions & 24 deletions timm/models/mobilenetv3.py
Original file line number Diff line number Diff line change
Expand Up @@ -996,28 +996,28 @@ def _cfg(url: str = '', **kwargs):
),
"lcnet_150.untrained": _cfg(),

'mobilenetv4_conv_small': _cfg(
# hf_hub_id='timm/',
interpolation='bicubic'),
'mobilenetv4_conv_medium.r224': _cfg(
# hf_hub_id='timm/',
crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_conv_medium.r256': _cfg(
# hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_conv_large.r256': _cfg(
'mobilenetv4_conv_small.e1200_r224_in1k': _cfg(
hf_hub_id='timm/',
test_input_size=(3, 256, 256), test_crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_conv_medium.e500_r224_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0, interpolation='bicubic'),
'mobilenetv4_conv_medium.e500_r256_in1k': _cfg(
hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
'mobilenetv4_conv_large.e500_r256_in1k': _cfg(
# hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
file='mobilenetv4_conv_large-r256.pth',
input_size=(3, 256, 256), pool_size=(8, 8),
crop_pct=0.95, test_input_size=(3, 320, 320), test_crop_pct=1.0, interpolation='bicubic'),
'mobilenetv4_conv_large.r384': _cfg(
# hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=0.95, interpolation='bicubic'),

'mobilenetv4_hybrid_small': _cfg(
# hf_hub_id='timm/',
interpolation='bicubic'),
'mobilenetv4_hybrid_medium.r224': _cfg(
# hf_hub_id='timm/',
crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_hybrid_medium.e500_r224_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0, interpolation='bicubic'),
'mobilenetv4_hybrid_medium.r256': _cfg(
# hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
Expand All @@ -1029,16 +1029,16 @@ def _cfg(url: str = '', **kwargs):
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=0.95, interpolation='bicubic'),

# experimental
'mobilenetv4_conv_aa_medium.r256': _cfg(
# hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_conv_blur_medium.r256': _cfg(
'mobilenetv4_conv_aa_medium.untrained': _cfg(
# hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_hybrid_medium_075': _cfg(
'mobilenetv4_conv_blur_medium.e500_r224_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_input_size=(3, 256, 256), test_crop_pct=1.0, interpolation='bicubic'),
'mobilenetv4_hybrid_medium_075.untrained': _cfg(
# hf_hub_id='timm/',
crop_pct=0.95, interpolation='bicubic'),
'mobilenetv4_hybrid_large_075.r256': _cfg(
'mobilenetv4_hybrid_large_075.untrained': _cfg(
# hf_hub_id='timm/',
input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95, interpolation='bicubic'),
})
Expand Down Expand Up @@ -1256,7 +1256,7 @@ def mobilenetv4_hybrid_medium_075(pretrained: bool = False, **kwargs) -> MobileN
@register_model
def mobilenetv4_hybrid_large_075(pretrained: bool = False, **kwargs) -> MobileNetV3:
""" MobileNet V4 Hybrid"""
model = _gen_mobilenet_v4('mobilenetv4_hybrid_large', 0.75, pretrained=pretrained, **kwargs)
model = _gen_mobilenet_v4('mobilenetv4_hybrid_large_075', 0.75, pretrained=pretrained, **kwargs)
return model


Expand Down
Loading