Create README.md

README.md

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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).