akdve/ot-forklift-person-yolo11s-smoke
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`
- 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.ptfrom `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
{
"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
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-safety at Space: https://huggingface.co/spaces/akdve/ml-intern-ot-yolo-smoke
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