Datasetpublic

edw2n/ImageNet-ES

📸 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. 📄 Read the paper (CVPR 2024) 🗂️ ImageNet-ES Strucuture ImageNet-ES ├── es-train │ └── tin_no_resize_sample_removed │ # 8K original validation samples of Tiny-ImageNet… See the full description on the dataset page: https://huggingface.co/datasets/edw2n/ImageNet-ES.

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Dataset Card

📸 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. 📄 Read the paper (CVPR 2024) <img align="center" src="https://raw.githubusercontent.com/Edw2n/ImageNet-ES/main/supples/ImageNet-ES.jpg" width="800">

🗂️ ImageNet-ES Strucuture

ImageNet-ES
├── es-train
│   └── tin_no_resize_sample_removed 
│   # 8K original validation samples of Tiny-ImageNet without references
├── es-val
│   ├── auto_exposure # 10K = 1K reference samples * 2 environments * 5 shots
│   ├── param_control # 128K = 1K reference samples * 2 environments * 64 shots
│   └── sampled_tin_no_resize # reference samples (1K)
├── es-test
    ├── auto_exposure # 10K = 1K reference samples * 2 environments * 5 shots
    ├── param_control # 54K = 1K reference samples * 2 environments * 27 shots
    └── 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.

🎛️ 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. <img align="center" src="https://raw.githubusercontent.com/Edw2n/ImageNet-ES/main/supples/Testbed.png" width="800"> <img align="center" src="https://raw.githubusercontent.com/Edw2n/ImageNet-ES/main/supples/Testbed_actual.jpg" width="800">

🖥️ Download from terminal

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

bash

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

🔍 More Exploration

Visit our paper repository: 🔗 ImageNet-ES GitHub Repository

📜 Citation

bibtex

@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}
}