🚀 vit_small_patch14_224.dinobloom模型

本项目提供了一个用于特征提取和图像分类的模型，可用于生成通用的细胞嵌入，在血液学领域具有重要价值。

🚀 快速开始

图像嵌入

from urllib.request import urlopen
from PIL import Image
import timm

# get example histology image
img = Image.open(
  urlopen(
    "https://raw.githubusercontent.com/zxaoyou/segmentation_WBC/master/Dataset%201/001.bmp"
  )
)

# load model from the hub
model = timm.create_model(
  model_name="hf-hub:1aurent/vit_small_patch14_224.dinobloom",
  pretrained=True,
).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)

data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data)  # output is a (batch_size, num_features) shaped tensor

✨ 主要特性

• 作为特征主干网络，可用于特征提取和图像分类任务。
• 模型参数规模为22M，属于小型模型。
• 支持输入尺寸为224 x 224 x 3的图像，采用14 x 14 x 3的图像块大小。

📦 模型信息

📄 许可证

本模型使用 Apache License 2.0 许可证。

📚 引用

@misc{koch2024dinobloom,
  title         = {DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology}, 
  author        = {Valentin Koch and Sophia J. Wagner and Salome Kazeminia and Ece Sancar and Matthias Hehr and Julia Schnabel and Tingying Peng and Carsten Marr},
  year          = {2024},
  eprint        = {2404.05022},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}