---
license: mit
task_categories:
  - image-classification
tags:
  - domain-generalization
  - computer-vision
  - benchmark
pretty_name: PACS
---

# PACS Dataset

## Overview

PACS is a benchmark dataset for **domain generalization** in image classification, 
introduced in "Deeper, Broader and Artier Domain Generalization" (Li et al., ICCV 2017).

It contains **9,991 images** across **4 domains** and **7 object categories**, 
with significantly larger domain shift than prior benchmarks like VLCS — 
averaging a 20.2% cross-domain performance drop versus 10.0% for VLCS.

## Domains

| Domain | Description |
|---|---|
| **P** — Photo | Real photographs |
| **A** — Art Painting | Artistic paintings |
| **C** — Cartoon | Cartoon-style illustrations |
| **S** — Sketch | Hand-drawn sketches |

## Classes

7 categories: **dog, elephant, giraffe, guitar, horse, house, person**

## Dataset Statistics

| Domain | Images |
|---|---|
| Photo | ~1,670 |
| Art Painting | ~2,048 |
| Cartoon | ~2,344 |
| Sketch | ~3,929 |
| **Total** | **9,991** |

## Usage

The standard evaluation protocol is **leave-one-domain-out**: train on 3 domains, 
test on the held-out domain. This yields 4 cross-domain tasks:
- Train on A, C, S → Test on P
- Train on P, C, S → Test on A
- Train on P, A, S → Test on C
- Train on P, A, C → Test on S

## Citation

```bibtex
@inproceedings{li2017deeper,
  title={Deeper, Broader and Artier Domain Generalization},
  author={Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy M},
  booktitle={ICCV},
  year={2017}
}
```

## Uploaded By

Mohammed Azeez Khan — used for domain generalization experiments at 
Carnegie Mellon University (EEG P300, motor imagery, fMRI neuroimaging).