Dataset之LSUN：LSUN数据集的简介、安装、使用方法之详细攻略
LSUN数据集的简介
1、Paper
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser and Jianxiong Xiao
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
arXiv:1506.03365 [cs.CV], 10 Jun 2015
2、简介
LSun场景分类的10个场景类别。LSUN 是一个场景理解图像数据集，主要包含了卧室、固房、客厅、教室等场景图像。
20对象类别：链接列表。每个类别的图像以LMDB格式存储，然后数据库被压缩。下载和解压缩ZIP文件后，请参考LSun实用代码来可视化和导出图像。还提供了每个zip文件的MD5和，以便您可以验证下载。
While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimize millions of parameters in deep network models. Lagging behind the growth in model capacity, the available datasets are quickly becoming outdated in terms of size and density. To circumvent this bottleneck, we propose to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop. Starting from a large set of candidate images for each category, we iteratively sample a subset, ask people to label them, classify the others with a trained model, split the set into positives, negatives, and unlabeled based on the classification confidence, and then iterate with the unlabeled set. 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. We experiment with training popular convolutional networks and find that they achieve substantial performance gains when trained on this dataset.
虽然在视觉识别算法的性能上已经取得了显著的进步，但是最先进的模型往往特别需要数据。为了在深层网络模型中优化数百万个参数，需要大量标注的训练数据集，这些数据集的生产既昂贵又繁琐。滞后于模型容量的增长，可用的数据集在尺寸和密度方面很快变得过时。为了绕过这个瓶颈，我们建议通过部分自动化的标签方案，利用循环中的人的深层学习，来增强人的努力。从每个类别的一大组候选图像开始，我们迭代地采样一个子集，要求人们标记它们，用训练好的模型对其他类别进行分类，根据分类置信度将集合划分为正、负和未标记，然后用未标记的集合进行迭代。为了评估这种级联过程的有效性，并使视觉识别研究取得进一步进展，我们构建了一个新的图像数据集，LSUN。
它包含10个场景类别和20个对象类别中的每一个的大约一百万个标记图像。我们对当前流行的卷积网络进行了实验，发现当在这个数据集上进行训练时，它们获得了显著的性能增益。
