Datasets:
File size: 1,741 Bytes
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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).
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