Safetyvision Yolov8

Honest note on the target. The Phase-2 goal was [email protected] ≥ 0.78 on the held-out test split. Validation cleared it (0.787); the held-out test came in at 0.766 (imgsz 896) — short of 0.78 by 0.014. We report the test number as the headline generalization figure rather than leading with the higher validation value. See Evaluation.

What's new in v2 (v1 → v2)

Test-vs-test improvement: +6.5 [email protected] / +4.6 [email protected]:0.95 at imgsz 896 (+5.3 [email protected] at 640). Two failure-mode classes improved dramatically — see Failure modes.

Model description

13-class PPE detection covering hard hats, safety vests, goggles, gloves, masks, their "missing/no" violation counterparts, fall detection, fall-harness absence, and a Person class.

• Base model: Ultralytics YOLOv8 (AGPL-3.0) — yolov8s.pt for v2, yolov8n.pt for v1
• Output: 17 channels × N anchors (8400 at 640, 16464 at 896) → NMS → boxes + class labels + confidence
• Use it for: flagging likely PPE violations in static images and short video clips for human review
• Do not use it for: automated disciplinary action, medical/clinical PPE, food safety, hazmat suits, or any standalone enforcement decision

Classes

Training data

v2 (current)

Five Roboflow Universe datasets merged into one corpus, deduplicated by MD5 hash and remapped to the 13 canonical classes:

• ppe-combined-9bprl-mmcaf (v1)
• hardhat-safetyvest (v1)
• fall-detection-ca3o8 (v4)
• safety_ppe (v1)
• construction-safety-gears-vcbdq (v1)

Stratified 85/10/5 split (~6.4 GB). Dataset selection deliberately favored side/back/occluded poses, low-light and high-glare scenes, and non-frontal workers to address v1's frontal bias.

v1 (original)

PPE-Combined v1 — 57,904 images (41,922 train / 10,834 val / 5,148 test).

Training procedure

v2 (current)

• Hardware: GCP L4 24GB (g2-standard-8, asia-southeast1-c)
• Framework: Ultralytics 8.4.51, PyTorch 2.12.0 + CUDA 13.0
• Epochs: 150 · Batch: 24 · Image size: 896 · LR schedule: cosine
• Augmentation: Albumentations (CoarseDropout, MotionBlur, RandomGamma, CLAHE — non-spatial) + native perspective, mosaic, mixup, HSV jitter
• multi_scale: False (see ADR-012 — multi_scale=True OOMs at batch=24 on a 24GB L4 at peak image size; the marginal benefit isn't worth halving the batch / ~95 hr wall time for a fixed-resolution deployment)
• Class balancing: none applied — augmentation alone hit target; the planned class-weighted loss was not needed (NO-Mask remained trainable at recall 0.789)
• Wall time: 61.25 hours, single uninterrupted run (no session cap, no resume), ~24 min/epoch, GPU memory 10–21 GB

v1 (original)

• Kaggle Notebooks, 2× Tesla T4 · Ultralytics 8.3.40 · 100 epochs · batch 32 · imgsz 640 · SGD
• ~15 hr across two Kaggle Save Versions (12-hr cap forced a resume at epoch 82)

Experiment tracking

• W&B run (v2, public): https://wandb.ai/agcr7jw-vellore-institute-of-technology/Ultralytics/runs/yolov8s-ppe-v2_20260519_065053
  • Logged under the Ultralytics project (the ultralytics W&B callback hardcodes the project name and ignores WANDB_PROJECT).
• MLflow (v2): local file store committed at mlruns/, experiment 621501274199551492, run 0af3bb3c50b84db3ac376d7e63e558d8.
• The v1 W&B run (9nctv2ai) has expired; v1 canonical metrics live in model/yolov8n-ppe-v1/results.csv in the repo.

Evaluation

Honest numbers, no cherry-picking. The held-out test split (4,026 images, never seen during training/validation) is the canonical generalization measure.

v2 headline (held-out test, 4,026 images, 12,080 instances)

The ~0.016 ONNX-vs-.pt gap at 640 is fp32 numerical drift through onnxslim/opset-20 (precision is unchanged, recall dips slightly at the detection threshold), not a broken export. The 640 ONNX ships on AWS Lambda (CPU budget); the 896 ONNX ships on Hugging Face Spaces (16GB RAM) for the full 0.766 ceiling.

v2 per-class test metrics (imgsz 896)

Confusion matrices and PR curves (640 and 896) are committed in docs/assets/eval/v2/.

v1 (for reference)

YOLOv8n test [email protected] = 0.701, [email protected]:0.95 = 0.441. Full v1 per-class metrics and curves in model/yolov8n-ppe-v1/.

Inference performance

• v2 GPU (L4), per image: ~7.5 ms inference @ 896, ~3.5 ms @ 640 (plus ~1 ms pre/post)
• v2 ONNX CPU (AWS Lambda, 3008 MB): ~500–800 ms warm inference @ 640. Cold start adds ~5–8 s container init plus a one-time ~10 s S3 fetch of the ONNX weights into /tmp (cached for subsequent warm invocations on the same container).
• v2 ONNX CPU (HF Spaces, 16 GB): sub-second warm detection @ 896; visible end-to-end latency on the public Space is dominated by the explainability + Gemini-multimodal report stages downstream of YOLO, not the forward pass itself.
• ONNX files: best_640.onnx 42.7 MB, best_896.onnx 42.8 MB (fp32, opset 20, onnxslim 0.1.94, no external-data sidecar)

Explainability

Per-violation results carry two attribution signals alongside the bounding boxes: a GradCAM heatmap and a SHAP pixel attribution. Both are surfaced in three places — the web UI tabs on the Upload result page, the API response (gradcam_b64 and shap_chart_b64), and embedded side-by-side in the downloadable PDF incident report alongside the annotated detection image, the OSHA citation, and the Gemini-generated incident narrative.

• GradCAM heatmap reliability — most informative on single-subject close-up scenes with one dominant detected object. In diffuse multi-person scenes, wide shots, or scenes where the violation target is small (<50 px), the heatmap can render flat or uninformative — a known limitation of class-activation-map techniques on dense scenes with small targets. The SPPF backbone layer (model.model[9]) is the only consistently usable GradCAM target identified during development on this model; targeting earlier or later layers produced noisier results in testing. Kept in the pipeline because the cases where it works clearly are exactly the ones that warrant visual confirmation; in diffuse scenes the SHAP attribution alongside it provides a useful complementary signal.
• SHAP attribution — shap.GradientExplainer against the YOLO classification head at 320×320 (host machine needs ≥11 GB RAM for the backward pass — relevant on WSL where the default 7.6 GB is insufficient). Rendered as a per-pixel attribution chart in the web UI and PDF report.

Intended use

Pre-screening tool to assist human workplace safety officers by surfacing likely PPE violations in images and short video clips for human review. Designed for construction sites, warehouses, manufacturing floors, and pre-shift safety walkthroughs.

Not a replacement for human judgment. Predictions must be reviewed by qualified safety personnel before any disciplinary, compliance, or insurance action.

Out of scope

• Medical/clinical settings (gowns, N95 fit testing, sterile gloves)
• Food processing (hairnets, beard guards, lab coats)
• Chemical/hazmat operations (full-face respirators, encapsulating suits)
• Drone or overhead camera angles (training data is ground/eye level)
• Crowded scenes with heavy mutual occlusion
• Real-time alerting where missing a single violation is unacceptable

Failure modes

Documented from training-data review and observed v2 test errors:

• NO-Safety Vest is the weakest class (test [email protected] 0.386, only 217 instances). High false-negative rate — do not rely on it as the sole vest-compliance signal.
• Mask / NO-Mask are weak (0.58 / 0.60). One source dataset (construction-safety-gears) mixes COVID-style face-mask close-ups into the industrial-mask class, adding domain noise. Mask precision in particular suffers (0.39).
• Low light / high glare — confidence drops; expect both false positives and false negatives.
• Partial occlusion — workers behind machinery/other workers may have PPE missed (improved vs v1 but not solved).
• Small workers (<50 px height) — distant figures often missed.
• Fast motion in video — motion blur causes missed frames;...

onnx · 42,7 MB · SHA-256 ea18ae903a56…0b35 · Hugging Face