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πΈ ImageNet-ES Diverse
ImageNet-ES Diverse is a benchmark dataset of 192,000 real-world images captured with a physical camera in a controlled testbed (ES-Studio Diverse), under various sensor parameters and lighting conditions. It complements the original ImageNet-ES by introducing more diverse and realistic covariate shifts for robustness evaluation.
This dataset is introduced in the ICLR 2025 paper: Adaptive Camera Sensor for Vision Models.
π Read the paper (ICLR 2025)

ποΈ ImageNet-ES Diverse Strucuture
ImageNet-ES-Diverse
βββ es-train (8K)
β βββ tin_no_resize_sample_removed
β # 8K original validation samples of Tiny-ImageNet without references in ImageNet-ES
βββ es-diverse-test (193K)
βββ auto_exposure (30K) = 1K * 6 environments *5 shots
βββ param_control (162K) = 1K * 6 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 adaptive sensing and qualitative insights.
ποΈ ES-Studio Diverse
To address the limitations of existing robustness benchmarks, including ImageNet-ES, we customized ES-Studio into ES-Studio Diverse by replacing the reference display with printed images captured from a screen. While maintaining control over lighting and sensor parameters as in ES-Studio, this customized setup and broader lighting variations enable the collection of data that is complementary to ImageNet-ES.
π₯οΈ Download from terminal
To download the dataset directly from your terminal using wget:
wget https://huggingface.co/datasets/Edw2n/ImageNet-ES-Diverse/resolve/main/ImageNet-ES-Diverse.zip
π More Exploration
Visit our paper repository: π Lens GitHub Repository
π Citation
@inproceedings{
baek2025adaptive,
title={Adaptive Camera Sensor for Vision Models},
author={Eunsu Baek and Sung-hwan Han and Taesik Gong and Hyung-Sin Kim},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=He2FGdmsas}
}
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