---
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}
}
```