Fine-tuned YOLO models for detecting factory workers in industrial environments. These models were trained on real-world factory footage to reliably localize workers under challenging conditions such as occlusion, machinery clutter, and varied lighting.

Models

File	Base model	Size	Task
`yolo11x_best.pt`	YOLO11x	114 MB	Worker detection
`yolo26x_best.pt`	YOLO26x	118 MB	Worker detection

Classes: worker (person in a factory/industrial setting)

Intended Use

Worker presence detection on the factory floor
Occupancy and zone-monitoring analytics
Input stage for downstream safety systems (e.g., restricted-area alerts)

Out of scope: These models are not certified safety devices. Do not use them as the sole mechanism for life-critical decisions. Face recognition or identification of individuals is not supported and not intended.

Usage

python

from ultralytics import YOLO

# Load either model
model = YOLO("yolo11x_best.pt")   # or "yolo26x_best.pt"

# Inference on an image, video, or stream
results = model.predict("factory_frame.jpg", conf=0.4)

for r in results:
    for box in r.boxes:
        print(box.cls, box.conf, box.xyxy)

Download directly from the Hub:

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

weights = hf_hub_download(
    repo_id="etemkocaaslan/balkontech-models",
    filename="yolo11x_best.pt",
)
model = YOLO(weights)

Training

Base models: Ultralytics YOLO11x and YOLO26x pretrained weights
Data: Proprietary dataset of factory-floor imagery annotated for workers
Fine-tuning: Standard Ultralytics training pipeline

Evaluation

Model	mAP50	mAP50-95	Precision	Recall
yolo11x_best	0.9512	0.5209	0.9973	0.9200
yolo26x_best	0.9457	0.5291	0.9683	0.9200

Limitations

Trained on factory environments; performance may degrade in outdoor or non-industrial scenes.
Heavy occlusion, unusual poses, or extreme camera angles may reduce recall.
Not evaluated for fairness across demographics; detections are class-level only (no identity).

Ethical Considerations

These models detect people in workplaces. Deployments should comply with local privacy and labor regulations (e.g., KVKK/GDPR), inform affected workers, and avoid use for individual surveillance or performance tracking.

License

Released under AGPL-3.0, consistent with the Ultralytics license of the base models. For commercial licensing of Ultralytics-derived models, see Ultralytics Licensing.

Citation

bibtex

@misc{balkontech-worker-detection,
  author = {Kocaaslan, Etem},
  title = {BalkonTech Models: Fine-tuned YOLO for Factory Worker Detection},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/etemkocaaslan/balkontech-models}}
}