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@ -88,6 +88,9 @@ default_cfgs = {
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url='https://storage.googleapis.com/vit_models/augreg/'
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url='https://storage.googleapis.com/vit_models/augreg/'
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'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
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'B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
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input_size=(3, 384, 384), crop_pct=1.0),
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input_size=(3, 384, 384), crop_pct=1.0),
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'vit_base_patch8_224': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/'
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'B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz'),
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'vit_large_patch32_224': _cfg(
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'vit_large_patch32_224': _cfg(
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url='', # no official model weights for this combo, only for in21k
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url='', # no official model weights for this combo, only for in21k
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),
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),
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@ -118,6 +121,9 @@ default_cfgs = {
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'vit_base_patch16_224_in21k': _cfg(
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'vit_base_patch16_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
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url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
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num_classes=21843),
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num_classes=21843),
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'vit_base_patch8_224_in21k': _cfg(
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url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz',
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num_classes=21843),
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'vit_large_patch32_224_in21k': _cfg(
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'vit_large_patch32_224_in21k': _cfg(
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth',
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num_classes=21843),
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num_classes=21843),
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@ -640,6 +646,16 @@ def vit_base_patch16_384(pretrained=False, **kwargs):
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return model
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return model
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@register_model
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def vit_base_patch8_224(pretrained=False, **kwargs):
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""" ViT-Base (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
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ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
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"""
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model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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@register_model
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def vit_large_patch32_224(pretrained=False, **kwargs):
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def vit_large_patch32_224(pretrained=False, **kwargs):
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""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
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""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights.
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@ -756,6 +772,18 @@ def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
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return model
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return model
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@register_model
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def vit_base_patch8_224_in21k(pretrained=False, **kwargs):
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""" ViT-Base model (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929).
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ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
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NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
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"""
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model_kwargs = dict(
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patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs)
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model = _create_vision_transformer('vit_base_patch8_224_in21k', pretrained=pretrained, **model_kwargs)
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return model
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@register_model
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@register_model
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def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
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def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
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""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
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""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
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