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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: 0.7650
---
# best_smoke_cigarette-detection
Fine-tuned from the official Ultralytics **YOLO11m** checkpoint (`yolo11m.pt`) using [Ultralytics](https://github.com/ultralytics/ultralytics).
## Classes
- `cigarette`
## Dataset
- Source: [richie-lab/smoking-tasfx](https://universe.roboflow.com/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
```python
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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