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Add convnext_nano weights, 80.8 @ 224, 81.5 @ 288

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This commit is contained in:
Ross Wightman 2022-07-26 16:37:36 -07:00
parent 4042a94f8f
commit 6f103a442b
1 changed files with 19 additions and 11 deletions
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30
timm/models/convnext.py
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@ -42,11 +42,15 @@ default_cfgs = dict(
convnext_base=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth"),
convnext_large=_cfg(url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth"),
# timm specific variants
convnext_nano=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_d1h-7eb4bdea.pth',
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
convnext_nano_hnf=_cfg(url=''),
convnext_nano_ols=_cfg(url=''),
convnext_tiny_hnf=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth',
crop_pct=0.95),
crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0),
convnext_tiny_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth'),
@ -410,8 +414,18 @@ def _create_convnext(variant, pretrained=False, **kwargs):
return model
@register_model
def convnext_nano(pretrained=False, **kwargs):
# timm nano variant with standard stem and head
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), conv_mlp=True, **kwargs)
model = _create_convnext('convnext_nano', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_nano_hnf(pretrained=False, **kwargs):
# experimental nano variant with normalization before pooling in head (head norm first)
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True, **kwargs)
model = _create_convnext('convnext_nano_hnf', pretrained=pretrained, **model_args)
@ -420,23 +434,17 @@ def convnext_nano_hnf(pretrained=False, **kwargs):
@register_model
def convnext_nano_ols(pretrained=False, **kwargs):
# experimental nano variant with overlapping conv stem
model_args = dict(
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), head_norm_first=True, conv_mlp=True,
conv_bias=False, stem_type='overlap', stem_kernel_size=9, **kwargs)
depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), conv_mlp=True,
stem_type='overlap', stem_kernel_size=9, **kwargs)
model = _create_convnext('convnext_nano_ols', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_hnf(pretrained=False, **kwargs):
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)
return model
@register_model
def convnext_tiny_hnfd(pretrained=False, **kwargs):
# experimental tiny variant with norm before pooling in head (head norm first)
model_args = dict(
depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), head_norm_first=True, conv_mlp=True, **kwargs)
model = _create_convnext('convnext_tiny_hnf', pretrained=pretrained, **model_args)