Ot Forklift Person Yolo11s Smoke

--- library_name: ultralytics license: cc-by-4.0 tags: - yolo - yolo11 - object-detection - operational-technology - industrial-safety - forklift - person-detection - ml-intern datasets: - keremberke/forklift-object-detection metrics: - mAP50 - mAP50-95 --- # OT Forklift-Person YOLO11 Detector This model is a YOLO11 object detector trained for a real Operational Technology (OT) vision use case: detecting **forklifts** and **people** in industrial/warehouse imagery to support pedestrian/forklift collision-risk monitoring. ## Application Industrial sites, warehouses, and plants use cameras for safety monitoring. A detector for `forklift` and `person` is a core perception component for zone intrusion alerts, near-miss analytics, and driver/pedestrian warning systems. ## Training data - Dataset: [`keremberke/forklift-object-detection`](https://huggingface.co/datasets/keremberke/forklift-object-detection) - License: CC BY 4.0 - Classes: `['forklift', 'person']` - Audit: `{"train": {"images": 2, "boxes": 4, "classes": {"forklift": 2, "person": 2}, "boxes_per_image_min": 2, "boxes_per_image_max": 2, "boxes_per_image_mean": 2.0, "bad_boxes_skipped": 0}, "val": {"images": 2, "boxes": 5, "classes": {"forklift": 4, "person": 1}, "boxes_per_image_min": 2, "boxes_per_image_max": 3, "boxes_per_image_mean": 2.5, "bad_boxes_skipped": 0}, "test": {"images": 2, "boxes": 2, "classes": {"forklift": 2}, "boxes_per_image_min": 1, "boxes_per_image_max": 1, "boxes_per_image_mean": 1.0, "bad_boxes_skipped": 0}}` The original COCO annotations were converted to Ultralytics YOLO format. Bounding boxes were clipped to image bounds when necessary; invalid zero-area boxes were skipped. ## Training recipe - Base model: `yolo11n.pt` from [`Ultralytics/YOLO11`](https://huggingface.co/Ultralytics/YOLO11) - Epochs: `1` - Image size: `640` - Batch size: `2` - Optimizer: `SGD` - LR0: `0.01` - Momentum: `0.937` - Weight decay: `0.0005` - Patience: `30` ## Validation metrics ```json { "metrics/precision(B)": 0.007017543859649123, "metrics/recall(B)": 1.0, "metrics/mAP50(B)": 0.2238235294117648, "metrics/mAP50-95(B)": 0.08390324665431029, "fitness": 0.08390324665431029 } ``` ## Usage ```python from ultralytics import YOLO model = YOLO("best.pt") results = model("factory_or_warehouse_image.jpg") ``` ## Monitoring Training was logged with Trackio project `ot-yolo-forklift-safe...

Source context: 21 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo akdve/ot-forklift-person-yolo11s-smoke