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YAML Metadata Warning:The task_categories "anomaly-detection" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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
# 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:
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
@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}
}
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