Create README.md

README.md

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+---
+license: agpl-3.0
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+  - yolo
+  - yolo11
+  - yolo26
+  - object-detection
+  - worker-detection
+  - person-detection
+  - industrial
+  - safety
+  - computer-vision
+base_model:
+  - Ultralytics/YOLO11
+---
+
+# BalkonTech Models — Factory Worker Detection
+
+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
+
+<!-- TODO: add training details (epochs, image size, augmentations, dataset size) -->
+
+## Evaluation
+
+<!-- TODO: fill in validation metrics -->
+
+| 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](https://www.ultralytics.com/legal/agpl-3-0-software-license) of the base models. For commercial licensing of Ultralytics-derived models, see [Ultralytics Licensing](https://www.ultralytics.com/license).
+
+## 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}}
+}
+```