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πΈ ImageNet-ES
Unlike conventional robustness benchmarks that rely on digital perturbations, we directly capture 202k images by using a real camera in a controllable testbed. The dataset presents a wide range of covariate shifts caused by variations in light and camera sensor factors.
π Read the paper (CVPR 2024)

ποΈ ImageNet-ES Strucuture
ImageNet-ES
βββ es-train
β βββ tin_no_resize_sample_removed
β # 8K original validation samples of Tiny-ImageNet without references
βββ es-val
β βββ auto_exposure # 10K = 1K reference samples * 2 environments * 5 shots
β βββ param_control # 128K = 1K reference samples * 2 environments * 64 shots
β βββ sampled_tin_no_resize # reference samples (1K)
βββ es-test
βββ auto_exposure # 10K = 1K reference samples * 2 environments * 5 shots
βββ param_control # 54K = 1K reference samples * 2 environments * 27 shots
βββ sampled_tin_no_resize2 # reference samples (1K)
The main paper and the appendix detail the dataset specifications and present analyses on covariate shifts, robustness evaluations, and qualitative insights.
ποΈ ES-Studio
To compensate the missing perturbations in current robustness benchmarks, we construct a new testbed, ES-Studio (Environment and camera Sensor perturbation Studio). It can control physical light and camera sensor parameters during data collection.

π₯οΈ Download from terminal
To download the dataset directly from your terminal using wget:
wget https://huggingface.co/datasets/Edw2n/ImageNet-ES/resolve/main/ImageNet-ES.zip
π More Exploration
Visit our paper repository: π ImageNet-ES GitHub Repository
π Citation
@InProceedings{Baek_2024_CVPR,
author = {Baek, Eunsu and Park, Keondo and Kim, Jiyoon and Kim, Hyung-Sin},
title = {Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {22294--22303}
}
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