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README.md

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+---
+language:
+- en
+license: mit
+task_categories:
+- anomaly-detection
+- image-segmentation
+tags:
+- industrial-anomaly-detection
+- anomaly-detection
+- mvtec
+- visa
+- real-iad
+- patchcore
+- reproducibility
+size_categories:
+- 10M<n<100M
+---
+
+# seg-gap-iad-data
+
+Pre-computed anomaly energy maps for reproducing all experiments in:
+
+> **Hu Xiaojun.** "The Segmentation Gap in Industrial Anomaly Detection: A Halo-Plateau Diagnosis." *Pattern Recognition Letters* (under review).
+
+**Code:** https://github.com/knifecaojia/seg-gap-iad
+
+## Contents
+
+This dataset stores the **intermediate outputs** of running three anomaly-detection
+method families on five IAD benchmarks (225 categories total, ~310k images).
+It is **not** the raw images — those must be obtained from the original dataset
+publishers (see the code repo's `DATA.md`).
+
+| Component | Size | Files |
+|---|---|---|
+| PatchCore energy maps (.npy, 224×224 float32) | ~58 GB | 305,803 |
+| Dinomaly maps (MVTec AD + VisA) | ~0.8 GB | — |
+| EfficientAD maps (MVTec AD, anomalib + direct) | ~0.7 GB | — |
+| Per-method `_meta.json` + `_eval_summary_*.json` | small | — |
+| `dinomaly_mvtec_model.pth` (trained weights) | 0.25 GB | 1 |
+
+**Total:** 61.79 GB / 306,136 files.
+
+## Why pre-computed?
+
+Re-running PatchCore + Dinomaly + EfficientAD on all 225 categories takes ~1 day
+of GPU time. These energy maps let you skip that step and go straight to
+evaluation / halo analysis / figure generation in the code repo.
+
+## Download & extract
+
+```bash
+# Download (set HF_ENDPOINT=https://hf-mirror.com if you are in mainland China)
+hf download knifeandcj/seg-gap-iad-data --repo-type dataset --local-dir seg-gap-iad-data
+
+# Extract every part into the same target dir; they reconstruct outputs/ with
+# no filename collisions. Each part-NNNNN.zip is a standalone standard zip.
+cd seg-gap-iad-data
+mkdir ../outputs
+for p in part-*.zip; do unzip -q "$p" -d ../outputs; done
+```
+
+The resulting `outputs/` tree matches the layout the code repo's `src/paths.py`
+expects by default (or set `SGAP_OUTPUTS` to point at it).
+
+## Integrity
+
+`manifest.json` maps every source file to its `part`, `size`, and `md5`.
+Verify after extraction:
+
+```python
+import json, hashlib, os
+m = json.load(open("manifest.json"))
+# compare md5 of each extracted file against manifest
+```
+
+`README.json` has packing metadata (part count, totals, timestamp).
+
+## License
+
+MIT. The energy maps are derived from the following datasets, each under its own
+license — obtain and respect those terms for any redistribution of raw images:
+- MVTec AD / MVTec AD 2 (CC BY-NC-SA / research use)
+- VisA (Apache-2.0)
+- Real-IAD / Real-IAD Variety (research use, see real-iad.org)
+
+## Citation
+
+```bibtex
+@article{hu2026segmentation,
+  title={The Segmentation Gap in Industrial Anomaly Detection: A Halo-Plateau Diagnosis},
+  author={Hu, Xiaojun},
+  journal={Pattern Recognition Letters},
+  year={2026},
+  note={Under review}
+}
+```