Instructions to use Beehzod/best_smoke_cigarette-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Beehzod/best_smoke_cigarette-detection with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("Beehzod/best_smoke_cigarette-detection") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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
licensefield, 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]))
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Model tree for Beehzod/best_smoke_cigarette-detection
Base model
Ultralytics/YOLO11Evaluation results
- mAP50self-reported0.790
- mAP50-95self-reported0.462
- precisionself-reported0.820
- recallself-reported0.765