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Add i18n variant of so400m model w/ weights. Add two in1k fine-tunes of original so400m 384x384 but at 378x378 (better matches patch14)

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Ross Wightman 2024-10-08 23:40:24 -07:00
parent 41a79e0fcb
commit 01b62264af
1 changed files with 77 additions and 0 deletions
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timm/models/vision_transformer.py
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@ -1841,6 +1841,16 @@ default_cfgs = {
hf_hub_id='timm/ViT-SO400M-14-SigLIP',
hf_hub_filename='open_clip_pytorch_model.bin',
num_classes=0),
'vit_so400m_patch16_siglip_256.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-16-SigLIP-i18n-256',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 256, 256),
num_classes=0),
'vit_so400m_patch14_siglip_378.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-14-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 378, 378),
num_classes=0),
'vit_so400m_patch14_siglip_384.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-14-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
@ -1890,6 +1900,16 @@ default_cfgs = {
hf_hub_filename='paligemma-3b-pt-224.npz',
custom_load='hf',
num_classes=0),
'vit_so400m_patch16_siglip_gap_256.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-16-SigLIP-i18n-256',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 256, 256),
num_classes=0),
'vit_so400m_patch14_siglip_gap_378.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-14-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
input_size=(3, 378, 378), crop_pct=1.0,
num_classes=0),
'vit_so400m_patch14_siglip_gap_384.webli': _cfg(
hf_hub_id='timm/ViT-SO400M-14-SigLIP-384',
hf_hub_filename='open_clip_pytorch_model.bin',
@ -1914,6 +1934,17 @@ default_cfgs = {
input_size=(3, 896, 896), crop_pct=1.0,
num_classes=0),
'vit_so400m_patch14_siglip_378.webli_ft_in1k': _cfg(
#hf_hub_id='timm/',
#file='vit_so400m_p14_378_map-8.pth',
input_size=(3, 378, 378), crop_pct=1.0, crop_mode='squash',
),
'vit_so400m_patch14_siglip_gap_378.webli_ft_in1k': _cfg(
# hf_hub_id='timm/',
#file='vit_so400m_p14_378_gap-8.pth',
input_size=(3, 378, 378), crop_pct=1.0, crop_mode='squash',
),
'vit_xsmall_patch16_clip_224.tinyclip_yfcc15m': _cfg(
hf_hub_id='timm/',
hf_hub_filename='open_clip_pytorch_model.bin',
@ -2935,6 +2966,28 @@ def vit_so400m_patch14_siglip_224(pretrained: bool = False, **kwargs) -> VisionT
return model
@register_model
def vit_so400m_patch16_siglip_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
# this is a corrected variant of the 384 with a res properly divisible by patch size (no padding/truncation)
model_args = dict(
patch_size=16, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_so400m_patch16_siglip_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so400m_patch14_siglip_378(pretrained: bool = False, **kwargs) -> VisionTransformer:
# this is a corrected variant of the 384 with a res properly divisible by patch size (no padding/truncation)
model_args = dict(
patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362, class_token=False, global_pool='map',
)
model = _create_vision_transformer(
'vit_so400m_patch14_siglip_378', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so400m_patch14_siglip_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
model_args = dict(
@ -3023,6 +3076,30 @@ def vit_so400m_patch14_siglip_gap_224(pretrained: bool = False, **kwargs) -> Vis
return model
@register_model
def vit_so400m_patch16_siglip_gap_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
model_args = dict(
patch_size=16, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362,
class_token=False, global_pool='avg', fc_norm=False,
)
model = _create_vision_transformer(
'vit_so400m_patch16_siglip_gap_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so400m_patch14_siglip_gap_378(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""
model_args = dict(
patch_size=14, embed_dim=1152, depth=27, num_heads=16, mlp_ratio=3.7362,
class_token=False, global_pool='avg', fc_norm=False,
)
model = _create_vision_transformer(
'vit_so400m_patch14_siglip_gap_378', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_so400m_patch14_siglip_gap_384(pretrained: bool = False, **kwargs) -> VisionTransformer:
""" A SigLIP variant of ViT with global average pooling (GAP) instead of attention pooling (MAP)."""