--- license: cc-by-nc-4.0 language: - en size_categories: - 1K5 workers) / Layer 2 (per-person, ≤5 workers) | | Annotators | 5 active tier-1 annotators + 2 experts + 1 safety officer | | Ground truth provenance | Highest-priority annotation per clip (expert > tier-1 > safety_officer) | | License | CC-BY-NC 4.0 (non-commercial, attribution) | ## Dataset structure ``` SteelBench/ ├── README.md # this file ├── LICENSE # CC-BY-NC 4.0 full text ├── ethics.md # surveillance consent + face anonymization rationale ├── croissant.json # Croissant Core + RAI metadata ├── data/ │ ├── frames/ # 1,345 dirs × 8 .jpg = 10,760 jpg (~11 GB, anonymized) │ ├── annotations/ # 1,345 canonical GT JSONs │ ├── annotation_source.json # per-clip provenance map │ └── safety_review/ # 186 safety officer reviews (parallel layer) ├── manifests/ │ ├── gt_clips.json # canonical 1,345 clip_id list │ ├── batch_manifest.csv # per-clip metadata (site, work_area, BRISQUE, etc.) │ ├── camera_zones.csv # zone tag per camera_id │ └── safety_rules.yaml # rule definitions ├── eval_data/ │ ├── prompt_sensitivity_clips.json # 150-clip ablation subset │ └── ablation_150_clips.json └── sample/ # 50-clip stratified preview (594 MB) for reviewers ├── clips/ # 50 .mp4 (full original clips for sanity-check) ├── frames/ # 400 anonymized .jpg ├── annotations/ # 47 canonical GT JSONs (3 of 50 lacked annotations │ # in the canonical set; documented for transparency) └── sample_manifest.csv ``` The `sample/` directory satisfies the NeurIPS Datasets & Benchmarks track requirement that >4 GB datasets ship a small sample for reviewer inspection. **Why no full mp4 clips in the main release?** The 8 representative jpgs per clip were anonymized via face blurring; the full mp4s were not (re-encoding 360+ frames per clip with face detection was out-of-scope for this release and the camera-distance argument that justifies the low face-detection rate on jpgs becomes less reliable across continuous video where movement reveals more). The 50-clip `sample/` subdir does include mp4s for reviewer inspection — this is a small, scoped exposure consistent with the double-blind review process. For full mp4 access for legitimate research, contact the authors after acceptance. ## How to load ```python from huggingface_hub import snapshot_download import json # Full dataset local_dir = snapshot_download(repo_id="steelbench/SteelBench", repo_type="dataset") # Just the manifest + annotations (no media) local_dir = snapshot_download( repo_id="steelbench/SteelBench", repo_type="dataset", allow_patterns=["manifests/*", "data/annotations/*", "README.md", "LICENSE"], ) clip_ids = json.load(open(f"{local_dir}/manifests/gt_clips.json")) ann = json.load(open(f"{local_dir}/data/annotations/{clip_ids[0]}.json")) ``` ## Datasheet for Datasets ### Motivation **For what purpose was the dataset created?** SteelBench was created to fill a gap in VLM evaluation: existing video-and-action benchmarks (Kinetics, ActivityNet, Charades) are curated, well-lit, and unambiguous; existing industrial datasets (IndustryEQA, MonitorVLM, iSafetyBench) are simulated, synthetic, or single-task. We needed a real-deployment benchmark with multiple evaluation dimensions (perception, safety reasoning, calibration) to test whether modern VLMs are deployment-ready in industrial monitoring. **Who created the dataset?** The dataset was created by the SteelBench authors as part of an academic research project. (Author identities withheld during double-blind review.) **Funding / interests:** No commercial relationship to the steel plant. Footage shared under research-only data-use agreement. ### Composition **What does each instance represent?** A 15-second clip from a fixed-position CCTV camera in a steel plant operational area. Each clip is annotated with: scene-level action labels, per-person action codes (when ≤5 workers), PPE assessment per worker, safety rule citations (when violations are observed), spatial context tags, visibility conditions, and an annotator-confidence score. **How many instances are there?** 1,345 clips total. Per-site distribution ranges from 1 (TAR Plant) to 211 (CRM 1&2). **Does the dataset contain all possible instances or is it a sample?** Sample. Source video totals ~149 hours from 117 unique videos; SteelBench is a curated subset stratified for action-class balance and visibility diversity. Curation pipeline is open-sourced in the companion code repository (`extract_clips.py`, `filter_clips.py`, `curate_batch.py`). **What data does each instance consist of?** - A 15-second .mp4 clip (1080p, H.264) - 8 evenly-spaced JPEG frames (anonymized — see `ethics.md`) - A canonical GT annotation JSON with the structured fields documented in `annotation_tool/schema_validator.py` **Is there a label or target?** Yes — per-clip structured annotation with multiple targets: action codes, PPE compliance, safety violations, scene type, worker count, visibility conditions. **Are there labeled subsets / splits?** - `manifests/gt_clips.json` — full 1,345-clip benchmark - `eval_data/prompt_sensitivity_clips.json` — 150-clip stratified ablation subset (used in Section 7 prompt-sensitivity ablation in the paper) - `eval_data/ablation_150_clips.json` — 150-clip stratified subset for frame-density ablation - `sample/` — 50-clip preview for review **Are there missing modalities or relationships between instances?** Different clips may share a source video and camera_id; this is documented in `manifests/batch_manifest.csv`. ### Collection process **How was the data acquired?** CCTV footage from an operating integrated steel plant, streamed continuously from 64 fixed cameras across 16 work areas. Source videos cover December 2025–April 2026. **What sampling/processing was applied?** - Person detection (YOLOv8-n) on 0.5 fps sampled frames - Detection-interval merging with 5 s gap tolerance and 2 s padding - 15 s fixed-window slicing - Quality filtering (BRISQUE, person-detection ratio, bounding-box area) - Stratified curation by action class and visibility condition **Who was involved in data collection?** Plant safety personnel installed and maintain the camera infrastructure. The research team applied processing and curation. The annotation pipeline involved 5 trained tier-1 annotators, 2 domain experts (industrial safety), and 1 safety officer. ### Preprocessing / cleaning / labeling **Was the data preprocessed/cleaned/labeled?** Yes. See the curation pipeline above and the annotation tooling in `annotation_tool/` (`schema_validator.py`, `safety_rules.py`, `app.py`). **Was the "raw" data saved in addition to the preprocessed/cleaned/labeled data?** Raw source videos are retained by the data provider but not released; this dataset ships the curated 15 s clips only. **Annotation methodology — model-assisted with audit:** The annotation tool pre-fills the structured form using a single VLM (Qwen3-VL-30B-A3B) so annotators verify rather than write from scratch. The paper's audit protocol (Section 4) empirically bounds the influence of this pre-fill via override rate, direction analysis, and dual-track calibration (anchored vs blind). The full audit code and intermediate audit data are in the companion code repository. ### Uses **For what purposes can the dataset be used?** - Evaluation of VLMs on industrial action recognition, PPE detection, and safety-violation reasoning - Benchmarking calibration and degradation robustness - Research on annotation provenance and audit protocols **What restrictions apply?** - Non-commercial use only (CC-BY-NC 4.0) - No re-identification attempts - No use for surveillance product training without separate agreement - See `ethics.md` for full ethics statement **Are there tasks for which the dataset should NOT be used?** - Worker identification or biometric profiling (faces are blurred) - Predictive surveillance of protected categories - Production deployment without further validation (the paper shows no evaluated VLM is deployment-ready: best DRS = 0.40) ### Distribution **Will the dataset be distributed?** Yes, on Hugging Face under CC-BY-NC 4.0. **Is there an associated paper?** Yes, NeurIPS 2026 D&B submission. (Cite once accepted; pre-acceptance citation withheld during review.) ### Maintenance **Who is supporting/hosting/maintaining the dataset?** The author team. Issues and questions should be filed on the companion code repository. **Are there errata?** Will be tracked as GitHub issues in the code repo post-acceptance. **Will old versions be supported/hosted?** Yes — Hugging Face dataset versions are tagged. ## Known limitations - **Scene-level vs per-person**: Clips with >5 workers receive only scene-level (Layer 1) annotations; per-person assessment (Layer 2) applies to ≤5-worker scenes. - **Anonymization is best-effort, not exhaustive** — see `ethics.md` for full method, and version history. ## Anonymization (v1.1) This is dataset version **1.1.0** (re-anonymized 2026-05-15). Two passes are applied: 1. **Face blur.** - JPGs (10,760 frames): **MediaPipe BlazeFace long-range** (`solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.3)`), 99×99 Gaussian + 20% pad. Detection rate 4.81%; low rate is structural at 7–10 m CCTV distance. - Sample MP4s (50 clips, 13,714 frames): **OpenCV YuNet** (`face_detection_yunet_2023mar.onnx`, threshold 0.5), 99×99 Gaussian + 20% pad, applied per frame. Audio stripped on re-encode. 2. **On-pixel text blur (new in v1.1).** EasyOCR (English + Hindi/Devanagari, threshold 0.20), 51×51 Gaussian + 8 px pad. Blurs text matching brand identifiers and ALL detected text in the top/bottom 80 px overlay bands. - JPGs: 12,573 sensitive + 10,229 overlay regions blurred across 10,616 of 10,760 frames. - Sample MP4s (every 5th frame): 3,347 sensitive + 2,363 overlay regions blurred. See `data/anonymization_report.json` for the full machine-readable stats and `ethics.md` for the responsible-use statement. ## Citation ``` @inproceedings{steelbench2026, title = {SteelBench: Evaluating Vision-Language Models in Real-World Industrial Environments}, author = {Anonymous Authors}, year = {2026}, } ``` ## License This dataset is released under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](LICENSE). Code in the companion repository is released under Apache-2.0.