convnext_xlargecvt_t=convnext_tiny()cvt_s=convnext_small()cvt_b=convnext_base()cvt_l=convnext_large()cvt_x=convnext_xlarge()W021121:12:49.976547686device_context.cc:447]PleaseNOTE:device:0,GPUComputeCapability:7.0,DriverAPIVersion:10.1,RuntimeAPIVersion:10.1W021121:12:49.982038686device_cont...
"convnext_base_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", "convnext_large_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", "convnext_xlarge_22k": "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", ...
Cancel Create saved search Sign in Sign up Reseting focus {{ message }} This repository has been archived by the owner on Oct 31, 2023. It is now read-only. facebookresearch / ConvNeXt Public archive Notifications You must be signed in to change notification settings Fork ...
"""original code from facebook research:https://github.com/facebookresearch/ConvNeXt"""import torchimport torch.nn as nnimport torch.nn.functional as Fdef drop_path(x, drop_prob: float = 0., training: bool = False):"""Drop paths (Stochastic Depth) per sample (when applied in main pa...
def convnext_xlarge(pretrained=False, in_22k=False, **kwargs): model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs) if pretrained: assert in_22k, "only ImageNet-22K pre-trained ConvNeXt-XL is available; please set in_22k=True" ...
In [3] # 配置模型 from ConvNeXt import convnext_tiny, convnext_small, convnext_base, convnext_large, convnext_xlarge cvt_t = convnext_tiny() cvt_s = convnext_small() cvt_b = convnext_base() cvt_l = convnext_large() cvt_x = convnext_xlarge() W0211 21:12:49.976547 686 ...
作者认为更大的感受野是 ViT 性能更好的可能原因之一,作者尝试增大卷积的kernel,使模型获得更大的感受野。Swin Transformers 的 Window 大小至少是7×7的,明显大于 3×3 的 ResNe(X)t 卷积核大小。 首先是把计算复杂度比较高的 depthwise conv layer 往前移动,将 block 由下图3(b)变为3(c)。使得复杂的模块...
# https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth model = ConvNeXt(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], num_classes=num_classes) return model def convnext_xlarge(num_classes: int): # https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k...
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k 89.946 99.01 305.08 448 eva_giant_patch14_560.m30m_ft_in22k_in1k 89.792 98.992 1014.45 560 eva02_large_patch14_448.mim_in22k_ft_in1k 89.626 98.954 305.08 448 eva02_large_patch14_448.mim_m38m_ft_in1k 89.57 98.918 305.08 448 eva_gia...
def convnext_xlarge(pretrained=False, in_22k=False, **kwargs): model = ConvNeXt(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs) if pretrained: assert in_22k, "only ImageNet-22K pre-trained ConvNeXt-XL is available; please set in_22k=True" ...