README.md

metadata

license: mit
library_name: ultralytics
pipeline_tag: object-detection
base_model: Ultralytics/YOLO11
tags:
  - yolo
  - smoking-detection
  - cigarette-detection
  - object-detection
  - ultralytics
model-index:
  - name: best_smoke_cigarette-detection
    results:
      - task:
          type: object-detection
        metrics:
          - name: mAP50
            type: mAP
            value: 0.7898
          - name: mAP50-95
            type: mAP
            value: 0.4617
          - name: precision
            type: precision
            value: 0.8199
          - name: recall
            type: recall
            value: 0.765

best_smoke_cigarette-detection

Fine-tuned from the official Ultralytics YOLO11m checkpoint (yolo11m.pt) using Ultralytics.

Classes

• cigarette

Dataset

• Source: richie-lab/smoking-tasfx (version 2)
• Images: 12046 train / 318 valid / 122 test
• Check the dataset page above for its license -- not necessarily the same as this repo's license field, which reflects the base model's license.

Training

Parameter	Value
Base checkpoint	yolo11m.pt
Epochs	100
Image size	640
Batch size	17
Optimizer	AdamW
Initial LR (lr0)	0.001
Patience (early stop)	20

Results (held-out validation split)

Metric	Value
mAP50	0.7898
mAP50-95	0.4617
Precision	0.8199
Recall	0.7650

Usage

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("image.jpg", conf=0.25)
for r in results:
    for box in r.boxes:
        print(model.names[int(box.cls[0])], float(box.conf[0]))