
Architecture	YOLOv11s-cls (Ultralytics)
Input	224×224 RGB
Output	logits over 2 classes: `no_seatbelt`, `seat_belt`
Parameters	5.4 M
GFLOPs	12.0
Weights	`best.pt` (PyTorch, 11 MB) / `best.onnx` (21 MB, opset 19)
Val top1	100.0 % at epoch 8 (early-stop after 18)
Train epochs	18 (early-stopped out of 40)

Training data

Source: lavdeep1234/driver-seat-belt-dectection (Kaggle). Windshield-view still frames; labels collapsed to binary (no seatbelt / seat_belt).

Split	`no_seatbelt`	`seat_belt`	Total
train	46	690	736
val (15 % holdout)	8	121	129
test	33	366	399

The dataset is heavily imbalanced (seat-belt class ~15× more frequent). 100 % val accuracy should be interpreted against the small negative class size. On out-of-distribution traffic footage, expect lower accuracy; combine with driver-ROI detection and a second-tier verifier.

Usage

Ultralytics

python

from ultralytics import YOLO
model = YOLO("best.pt")
r = model("driver_crop.jpg")
print(r[0].probs.top1, r[0].names[r[0].probs.top1])

ONNX Runtime

python

import cv2, numpy as np, onnxruntime as ort
sess = ort.InferenceSession("best.onnx", providers=["CUDAExecutionProvider"])
img = cv2.cvtColor(cv2.imread("driver_crop.jpg"), cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (224, 224)).astype(np.float32) / 255.0
x = np.ascontiguousarray(img.transpose(2, 0, 1)[None])
logits = sess.run(None, {"images": x})[0][0]
print(["no_seatbelt", "seat_belt"][int(logits.argmax())], float(logits.max()))

Intended use

Real-time seatbelt violation flagging on road-traffic video after car detection + tracking (e.g. via DeepStream TrafficCamNet + NvDCF tracker).
Run on the top ~50 % crop of a detected car bbox, where the windshield / driver sits.

Out-of-scope / limitations

Nighttime / tinted-glass / heavy glare scenes under-represented in training.
Dataset is English/European angle; fine-tune on local data for RU / KZ plates.
Binary only — does not distinguish passenger vs driver belt.

License

AGPL-3.0 (inherits Ultralytics YOLOv11 weight license).

RISEF/yolov11s-seatbelt

YOLOv11s-cls · seatbelt binary classifier

Summary