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synced 2025-06-03 15:01:08 +08:00
add other tinyclip
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@ -1739,6 +1739,19 @@ default_cfgs = {
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'vit_8m_patch16_tinyclip_224.yfcc15m': _cfg(
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url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M.pt',
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license='mit',
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mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
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'vit_39m_patch16_tinyclip_224.yfcc15m': _cfg(
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url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-39M-16-Text-19M-YFCC15M.pt',
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license='mit',
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mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
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'vit_40m_patch32_tinyclip_224.laion400m': _cfg(
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url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-40M-32-Text-19M-LAION400M.pt',
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license='mit',
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mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
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'vit_61m_patch32_tinyclip_224.laion400m': _cfg(
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url='https://github.com/wkcn/TinyCLIP-model-zoo/releases/download/checkpoints/TinyCLIP-ViT-61M-32-Text-29M-LAION400M.pt',
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license='mit',
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mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512),
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'vit_medium_patch16_reg4_256': _cfg(
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@ -2635,6 +2648,32 @@ def vit_8m_patch16_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTra
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return model
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@register_model
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def vit_39m_patch16_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
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model_args = dict(embed_dim=512, depth=12, num_heads=8, pre_norm=True, norm_layer=nn.LayerNorm)
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model = _create_vision_transformer(
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'vit_39m_patch16_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
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return model
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@register_model
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def vit_40m_patch32_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
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model_args = dict(
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patch_size=32, embed_dim=512, depth=12, num_heads=8, pre_norm=True, norm_layer=nn.LayerNorm)
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model = _create_vision_transformer(
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'vit_40m_patch32_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
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return model
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@register_model
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def vit_61m_patch32_tinyclip_224(pretrained: bool = False, **kwargs) -> VisionTransformer:
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model_args = dict(
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patch_size=32, embed_dim=640, depth=12, num_heads=10, pre_norm=True, norm_layer=nn.LayerNorm)
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model = _create_vision_transformer(
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'vit_61m_patch32_tinyclip_224', pretrained=pretrained, **dict(model_args, **kwargs))
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return model
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@register_model
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def vit_medium_patch16_reg4_256(pretrained: bool = False, **kwargs) -> VisionTransformer:
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model_args = dict(
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