| --- |
| 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). |
|
|
