Vit L16 Mim

🚀 vit_l16_mim模型卡

這是一個使用掩碼圖像建模（MIM）預訓練的ViT - L16圖像編碼器。該模型未針對特定分類任務進行微調，旨在用作通用特徵提取器或用於下游任務（如目標檢測、分割或自定義分類）的主幹網絡。

🚀 快速開始

此模型可作為通用特徵提取器或下游任務的主幹網絡，以下是使用示例。

✨ 主要特性

• 基於掩碼圖像建模（MIM）進行預訓練，具有強大的特徵提取能力。
• 未針對特定分類任務微調，通用性強，可靈活應用於多種下游任務。

📚 詳細文檔

模型詳情

💻 使用示例

基礎用法

import torch
import birder
from PIL import Image

(net, model_info) = birder.load_pretrained_model("vit_l16_mim_400", inference=True)

# Get the image size the model was trained on
size = birder.get_size_from_signature(model_info.signature)

# Create an inference transform
transform = birder.classification_transform(size, model_info.rgb_stats)

image = Image.open("path/to/image.jpeg")
input_tensor = transform(image).unsqueeze(dim=0)
with torch.inference_mode():
    embedding = net.embedding(input_tensor)
    # embedding is a tensor with shape of (1, 1024)

📄 許可證

本項目採用Apache - 2.0許可證。

📚 引用

@misc{dosovitskiy2021imageworth16x16words,
      title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
      author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
      year={2021},
      eprint={2010.11929},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2010.11929},
}

@misc{he2021maskedautoencodersscalablevision,
      title={Masked Autoencoders Are Scalable Vision Learners},
      author={Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Dollár and Ross Girshick},
      year={2021},
      eprint={2111.06377},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2111.06377},
}