xcit_medium_24_p16_224.fb_dist_in1k模型卡片

模型详情

• 模型类型：图像分类/特征骨干
• 模型统计：
  • 参数数目（M）：84.4
  • GMACs：16.1
  • 激活（M）：31.7
  • 图像大小：224 x 224
  • 论文：
    • XCiT：交叉协方差图像变换器： https://arxiv.org/abs/2106.09681
    • 数据集：ImageNet-1k
    • 原始来源： https://github.com/facebookresearch/xcit

      模型用途

      图像分类

      from urllib.request import urlopen
      from PIL import Image
      import timm
      
      img = Image.open(urlopen(
          'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
      ))
      
      model = timm.create_model('xcit_medium_24_p16_224.fb_dist_in1k', pretrained=True)
      model = model.eval()
      
      # get model specific transforms (normalization, resize)
      data_config = timm.data.resolve_model_data_config(model)
      transforms = timm.data.create_transform(**data_config, is_training=False)
      
      output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1
      
      top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
      

      图像嵌入

      from urllib.request import urlopen
      from PIL import Image
      import timm
      
      img = Image.open(urlopen(
          'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
      ))
      
      model = timm.create_model(
          'xcit_medium_24_p16_224.fb_dist_in1k',
          pretrained=True,
          num_classes=0,  # remove classifier nn.Linear
      )
      model = model.eval()
      
      # get model specific transforms (normalization, resize)
      data_config = timm.data.resolve_model_data_config(model)
      transforms = timm.data.create_transform(**data_config, is_training=False)
      
      output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor
      
      # or equivalently (without needing to set num_classes=0)
      
      output = model.forward_features(transforms(img).unsqueeze(0))
      # output is unpooled, a (1, 197, 512) shaped tensor
      
      output = model.forward_head(output, pre_logits=True)
      # output is a (1, num_features) shaped tensor
      

      引用

      @article{el2021xcit,
        title={XCiT: Cross-Covariance Image Transformers},
        author={El-Nouby, Alaaeldin and Touvron, Hugo and Caron, Mathilde and Bojanowski, Piotr and Douze, Matthijs and Joulin, Armand and Laptev, Ivan and Neverova, Natalia and Synnaeve, Gabriel and Verbeek, Jakob and others},
        journal={arXiv preprint arXiv:2106.09681},
        year={2021}
      }