Driver Monitoring System - (DMS)
In-cabin driver monitoring imagery featuring bounding box annotations for drinking, yawning, calling, and texting behaviors. Diverse lighting and interior perspectives ensure robust model performance for real-time safety monitoring applications.
Sample Frames
20 annotated samples drawn from the train split. Toggle annotations to inspect bounding-box quality.
Class Distribution
4 annotation classes · 1,356 total images. Sorted by object count, descending.
Class Balance
Per-class counts, frequency, and average bounding-box area. Sort any column to surface the rarest or most prevalent classes for re-balancing.
| Class | Images↓ with class | Objects total | Per image average | Area % of frame |
|---|---|---|---|---|
| drinking | 478 | 478 | 1 | 15.57% |
| texting | 409 | 409 | 1 | 5.55% |
| calling | 250 | 250 | 1 | 10.75% |
| yawning | 219 | 219 | 1 | 9.97% |
Co-occurrence Matrix
How frequently pairs of classes appear in the same image. Diagonal cells show standalone image count for that class. Useful for spotting biased or correlated labels.
| drinking | yawning | calling | texting | |
|---|---|---|---|---|
| drinking | 478 | |||
| yawning | 219 | |||
| calling | 250 | |||
| texting | 409 |
Average Object Area
Each rectangle is one class, sized by the average area its bounding boxes occupy as a percentage of the frame. Surfaces tiny vs. dominant objects at a glance.
Spatial Distribution
Where annotations of each class tend to fall across the frame. Brighter regions indicate higher density — useful for detecting positional bias in your training data.
Model Performance
Validation metrics from a YOLOv8 detector trained on this dataset. Reference checkpoint: yolov8m.pt.
Validation curves over training
Dataset Metadata
| Annotation format | YOLO |
|---|---|
| Total images | 1,356 |
| Classes | 4 |
| Resolution | Mixed |
| Last updated | 2026-05 |
Interested in this dataset?
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