Add task categories, paper link, and dataset structure details to README

README.md

@@ -1,16 +1,88 @@
 ---
 license: mit
+task_categories:
+- image-classification
+- image-segmentation
 tags:
-  - anomaly-detection
-  - cold-start
+- anomaly-detection
+- cold-start
 ---
 
 # ArcAD Cold-Start Data Splits
 
-Cold-start supervised data splits (JSON manifests) for **MVTec-AD, VisA,
-Real-IAD, and MANTA**, used by [ArcAD](https://github.com/LGC-AD/ArcAD)
-(arXiv:2607.02252, ECCV 2026).
+Cold-start supervised data splits (JSON manifests) for **MVTec-AD, VisA, Real-IAD, and MANTA**, used by [ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection](https://huggingface.co/papers/2607.02252) (ECCV 2026).
 
-All paths use each dataset's **original download structure** — download the
-official datasets and the paths resolve directly. See the
-[ArcAD repository](https://github.com/LGC-AD/ArcAD) for the JSON format and usage.
+- **Repository:** https://github.com/LGC-AD/ArcAD
+- **Paper:** https://huggingface.co/papers/2607.02252
+
+All paths use each dataset's **original download structure** — download the official datasets and the paths resolve directly. See the [ArcAD repository](https://github.com/LGC-AD/ArcAD) for usage.
+
+### Split JSON format
+
+Every `<category>.json` has the same schema:
+
+```json
+{
+  "meta":   { "dataset": "mvtec", "category": "bottle", "num_labeled": 69, "num_test": 223 },
+  "labeled":[ { "image": "bottle/train/good/000.png",            "mask": "",                                              "label": 0, "anomaly_class": "good" },
+              { "image": "bottle/test/broken_large/005.png",     "mask": "bottle/ground_truth/broken_large/005_mask.png", "label": 1, "anomaly_class": "broken_large" } ],
+  "test":   [ ... ]
+}
+```
+
+- All paths are **relative to the dataset root** (the `--data_path` argument) and use each dataset's **original download layout**.
+- `mask` is `""` for normal samples (no mask file).
+- `label`: `0` = normal, `1` = anomaly.
+- `anomaly_class`: `"good"` for normals; the defect sub-folder name (e.g. `broken_large`) for MVTec, `"anomaly"` for VisA / Real-IAD / MANTA.
+
+The total number of labeled samples matches the cold-start protocol (e.g. MVTec-AD: 1089 normals + 121 anomalies; Real-IAD: 10940 normals + 1216 anomalies).
+
+### Expected on-disk layout
+
+The JSON paths resolve against the **official download structure** of each dataset. Point `--data_path` at the root shown below:
+
+#### MVTec-AD
+It contains over 5000 high-resolution images divided into fifteen different object and texture categories.
+```
+<data_path>/bottle/
+    train/good/*.png
+    test/good/*.png
+    test/<defect_type>/*.png            # e.g. broken_large, broken_small, contamination, ...
+    ground_truth/<defect_type>/<name>_mask.png
+```
+
+#### VisA
+It contains 12 subsets corresponding to 12 different objects. There are 10,821 images with 9,621 normal and 1,200 anomalous samples.
+```
+<data_path>/candle/
+    Data/Images/Normal/*.JPG
+    Data/Images/Anomaly/*.JPG
+    Data/Masks/Anomaly/*.png
+```
+
+#### Real-IAD
+A large-scale challenging industrial AD dataset, containing 30 classes with totally 151,050 images.
+```
+<data_path>/realiad_1024/<category>/<image>      # image_path from realiad_jsons/sup/<cat>.json
+<data_path>/realiad_jsons/sup/<category>.json    # authoritative labeled/test split
+```
+
+#### MANTA
+It contains 38 categories and over 130K object-level images.
+```
+<data_path>/MANTA_TINY_256_cropped/<category>/<image>
+<data_path>/sup_cropped/<category>.json          # authoritative labeled/test split
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{han2026arcad,
+  title   = {ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection},
+  author  = {Han, Ningning and Fan, Lei and Guo, Jia and Cao, Yunkang and Su, Xiu and Cao, Feng and Di, Donglin and Su, Tonghua},
+  journal = {arXiv preprint arXiv:2607.02252},
+  year    = {2026}
+}
+```