ViT:视觉Transformer...
blog.csdn.net › weixin_37737254 › articleJun 06, 2021 · pip install vit-pytorch vit-pytorch用法如下: import torch from vit_pytorch import ViT # 创建ViT模型实例 v = ViT( image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 16, mlp_dim = 2048, dropout = 0.1, emb_dropout = 0.1 ) # 随机化一个图像输入 img = torch.randn(1, 3, 256, 256) # 获取输出 ...
Transformer在计算机视觉中的应用 - 简书
www.jianshu.com › p › bf95f5515626Jun 26, 2021 · github: Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch. VIT(VISION TRANSFORMER)是Google在2020年提出的一种使用Transformer进行图像分类的backbone。 VIT在模型结构上保持了和原版Transformer相近。
GitHub - jeonsworld/ViT-pytorch: Pytorch reimplementation of ...
github.com › jeonsworld › ViT-pytorchNov 29, 2020 · Vision Transformer. Pytorch reimplementation of Google's repository for the ViT model that was released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil ...
ViT:视觉Transformer backbone网络ViT论文与代码详解-技术圈
jishuin.proginn.com › p › 763bfbd5c103Jun 08, 2021 · pip install vit-pytorch vit-pytorch用法如下: import torch from vit_pytorch import ViT # 创建ViT模型实例 v = ViT(image_size = 256, patch_size = 32, num_classes = 1000, dim = 1024, depth = 6, heads = 16, mlp_dim = 2048, dropout = 0.1, emb_dropout = 0.1) # 随机化一个图像输入 img = torch.randn(1, 3, 256, 256) # 获取输出 ...
vit-pytorch - PyPI
https://pypi.org/project/vit-pytorch25.12.2021 · Files for vit-pytorch, version 0.26.2; Filename, size File type Python version Upload date Hashes; Filename, size vit_pytorch-0.26.2-py3-none-any.whl (50.5 kB) File type Wheel Python version py3 Upload date Jan 3, 2022 Hashes View
GitHub - gupta-abhay/pytorch-vit: An Image is Worth 16x16 ...
github.com › gupta-abhay › pytorch-vitOct 01, 2021 · @article {dosovitskiy2020image, title = {An image is worth 16x16 words: Transformers for image recognition at scale}, author = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others}, journal = {arXiv preprint arXiv:2010.11929 ...