Vit Reg4 B16 Mim

🚀 vit_reg4_b16_mim模型卡片

這是一個使用掩碼圖像建模（MIM）預訓練的ViT reg4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，或作為下游任務（如目標檢測、分割或自定義分類）的骨幹網絡。

🚀 快速開始

模型詳情

模型使用

💻 使用示例

基礎用法

import torch
import birder
from PIL import Image

(net, model_info) = birder.load_pretrained_model("vit_reg4_b16_mim_300", 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, 768)

高級用法

import torch
import birder
from PIL import Image

# Must first download the model files
(net, cfg) = birder.load_model_with_cfg("models/vit_reg4_b16_mim.json", "models/vit_reg4_b16_mim_300.pt")
net.eval()

# Get the image size the model was trained on
size = birder.get_size_from_signature(cfg["signature"])

# Create an inference transform
transform = birder.classification_transform(size, cfg["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, embedding_size)

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{darcet2024visiontransformersneedregisters,
      title={Vision Transformers Need Registers}, 
      author={Timothée Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
      year={2024},
      eprint={2309.16588},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2309.16588}, 
}

@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}, 
}

📄 許可證

本項目採用Apache 2.0許可證。