Hiera Abswin Base Mim

🚀 hiera_abswin_base_mim模型卡片

这是一个采用绝对窗口位置嵌入策略的Hiera图像编码器，通过掩码图像建模（MIM）进行预训练。该模型未针对特定分类任务进行微调，旨在作为通用特征提取器或用于下游任务（如目标检测、分割或自定义分类）的骨干网络。

🚀 快速开始

此模型可作为通用特征提取器或下游任务的骨干网络。你可以按照以下使用示例进行操作。

✨ 主要特性

• 采用绝对窗口位置嵌入策略的图像编码器。
• 通过掩码图像建模（MIM）进行预训练。
• 未针对特定分类任务进行微调，适用于通用特征提取和下游任务。

📚 详细文档

模型详情

💻 使用示例

基础用法

图像嵌入

import birder
from birder.inference.classification import infer_image

(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim", 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 = "path/to/image.jpeg"  # or a PIL image
(out, embedding) = infer_image(net, image, transform, return_embedding=True)
# embedding is a NumPy array with shape of (1, 768)

检测特征图

from PIL import Image
import birder

(net, model_info) = birder.load_pretrained_model("hiera_abswin_base_mim", 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")
features = net.detection_features(transform(image).unsqueeze(0))
# features is a dict (stage name -> torch.Tensor)
print([(k, v.size()) for k, v in features.items()])
# Output example:
# [('stage1', torch.Size([1, 96, 56, 56])),
#  ('stage2', torch.Size([1, 192, 28, 28])),
#  ('stage3', torch.Size([1, 384, 14, 14])),
#  ('stage4', torch.Size([1, 768, 7, 7]))]

📄 许可证

本模型采用Apache 2.0许可证。

📖 引用

@misc{ryali2023hierahierarchicalvisiontransformer,
      title={Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles},
      author={Chaitanya Ryali and Yuan-Ting Hu and Daniel Bolya and Chen Wei and Haoqi Fan and Po-Yao Huang and Vaibhav Aggarwal and Arkabandhu Chowdhury and Omid Poursaeed and Judy Hoffman and Jitendra Malik and Yanghao Li and Christoph Feichtenhofer},
      year={2023},
      eprint={2306.00989},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2306.00989},
}

@misc{bolya2023windowattentionbuggedinterpolate,
      title={Window Attention is Bugged: How not to Interpolate Position Embeddings},
      author={Daniel Bolya and Chaitanya Ryali and Judy Hoffman and Christoph Feichtenhofer},
      year={2023},
      eprint={2311.05613},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2311.05613},
}