| | --- |
| | license: cc-by-4.0 |
| | task_categories: |
| | - image-classification |
| | language: |
| | - en |
| | tags: |
| | - benchmark |
| | - image-classification |
| | - out-of-distribution |
| | - robustness |
| | - sensor-control |
| | - light-control |
| | - real-photo |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| | # 📸 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)](https://openaccess.thecvf.com/content/CVPR2024/html/Baek_Unexplored_Faces_of_Robustness_and_Out-of-Distribution_Covariate_Shifts_in_Environment_CVPR_2024_paper.html) |
| | <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** (**E**nvironment and camera **S**ensor 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](https://github.com/Edw2n/ImageNet-ES) |
| |
|
| | --- |
| | ### 📜 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} |
| | } |
| | ``` |
