PUG_ImageNet

## PUG: ImageNet
The PUG: ImageNet dataset contains 88,328 pre-rendered images based on Unreal Engine using 724 assets representing 151 ImageNet classes with 64 environments, 7 sizes, 9 textures, 18 different camera orientations, 18 different character orientations and 7 light intensities. In contrast to PUG: Animals, PUG: ImageNet was created by varying only a single factor at a time (which explains the lower number of images than PUG: Animals despite using more factors). The main purpose of this dataset is to provide a novel, useful benchmark, paralleling ImageNet, but for fine-grained evaluation of the robustness of image classifiers, along several factors of variation.

## LICENSE
The datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.

## Citing PUG
If you use one of the PUG datasets, please cite:
```
@misc{bordes2023pug,
title={PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning},
author={Florian Bordes and Shashank Shekhar and Mark Ibrahim and Diane Bouchacourt and Pascal Vincent and Ari S. Morcos},
year={2023},
eprint={2308.03977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```

## To learn more about the PUG datasets:
Please visit the [website](https://pug.metademolab.com/) and the [github](https://github.com/facebookresearch/PUG)

## PUG: ImageNet
PUG: ImageNet数据集包含88328张预渲染图像，基于虚幻引擎（Unreal Engine）制作，采用724个资产素材，涵盖151个ImageNet（ImageNet）类别，包含64种环境、7种尺寸、9种纹理、18种不同相机朝向、18种不同角色朝向以及7种光照强度。与PUG: Animals数据集不同，PUG: ImageNet仅通过每次变更单一变量进行构建（这也解释了尽管其使用的变量更多，但图像总数却少于PUG: Animals的原因）。本数据集的核心目的是提供一个新颖且实用的基准测试集，与ImageNet对标，但可用于从多个变化变量维度对图像分类器的鲁棒性进行细粒度评估。

## 许可证
本数据集采用CC-BY-NC协议进行分发，附加条款规定不得将其用于训练生成式AI（Generative AI）模型。

## 引用PUG数据集
若您使用本PUG系列数据集之一，请引用如下文献：

@misc{bordes2023pug,
title={PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning},
author={Florian Bordes and Shashank Shekhar and Mark Ibrahim and Diane Bouchacourt and Pascal Vincent and Ari S. Morcos},
year={2023},
eprint={2308.03977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

## 了解更多PUG数据集信息
请访问[官方网站](https://pug.metademolab.com/)与[GitHub仓库](https://github.com/facebookresearch/PUG)