Jun 10, 2015 · To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories.

LSUN Dataset Documentation and Demo Code. Contribute to fyu/lsun development by creating an account on GitHub.

The Large-scale Scene Understanding (LSUN) challenge aims to provide a different benchmark for large-scale scene classification and understanding. The LSUN classification dataset contains 10 scene categories, such as dining room, bedroom, chicken, outdoor church, and so on.

Dec 6, 2022 · lsun/bedroom. Config description: Images of category bedroom. Download size: 42.77 GiB. Dataset size: 44.48 GiB. Splits:

In this paper, we introduce an integrated framework using deep learning with human in the loop to annotate large-scale image data. We focus on interface design, procedures for checking for human annotation, as well as labeling propagation to amplify human effort automatically.

Jun 10, 2015 · To assess the effectiveness of this cascading procedure and enable further progress in visual recognition research, we construct a new image dataset, LSUN. It contains around one million labeled images for each of 10 scene categories and 20 object categories.

Jun 10, 2015 · To demonstrate the power of our annotation procedure and enable further progress of visual recognition, we construct a scene-centric database called "LSUN" containing millions of labeled images...

Jun 23, 2024 · 本文介绍LSUN数据集，一个类似ImageNet的大型图片数据集，利用深度学习进行自动标注，包含丰富的物体和场景标签。 文章提供数据集下载链接，详细说明mdb格式转换过程及常见问题解决方法。 类似 ImageNet 的大规模数据集，最早出自这篇论文： 这个数据集借助 深度学习 自动标注图片，相比ImageNet, 其标注更丰富，不仅有图片中主要物体的标注，还有场景标注。 其中场景标注是现在深度学习中常用的 benchmark 数据集. 常用场景包 …

The LSUN datasets can be conveniently downloaded via the script available here. We performed a custom split into training and validation images, and provide the corresponding filenames at https://ommer-lab.com/files/lsun.zip .

LSUN Dataset 是一个大规模图像数据集，其基于人类在循环中进行深度学习的构建，包含 10 个场景类别和 20 个对象类别，共计约 100 万张标记图像。 该数据集由加州大学伯克利分校等于 2015 年发布，主要发布人有 Fisher Yu 、 Peter Kontschieder 、 Shuran Song 、 Ming Jiang 、 Yinda Zhang 、 Catherine Qi Zhao 、 Thomas Funkhouser 和 Jianxiong Xiao，相关论文为《LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop》。