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edw2n/ImageNet-ES-Diverse

📸 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.📄 Read the paper (ICLR 2025)… See the full description on the dataset page: https://huggingface.co/datasets/edw2n/ImageNet-ES-Diverse.

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Dataset Card

📸 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. 📄 Read the paper (ICLR 2025) <img align="center" src="https://raw.githubusercontent.com/Edw2n/Lens/main/ImageNet-ES-Diverse-example.png" width="800">

🗂️ ImageNet-ES Diverse Strucuture

ImageNet-ES-Diverse
├── es-train (8K)
│   └── tin_no_resize_sample_removed 
│   # 8K original validation samples of Tiny-ImageNet without references in ImageNet-ES 
├── es-diverse-test (193K)
    ├── auto_exposure (30K)  = 1K * 6 environments *5 shots
    ├── param_control  (162K) = 1K * 6 environments * 27 shots
    └── 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.

🎛️ 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.

🖥️ Download from terminal

To download the dataset directly from your terminal using `wget`:

bash

wget https://huggingface.co/datasets/Edw2n/ImageNet-ES-Diverse/resolve/main/ImageNet-ES-Diverse.zip

🔍 More Exploration

Visit our paper repository: 🔗 Lens GitHub Repository

📜 Citation

bibtex

@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}
}