samil24/yolo26n-question-segmentation

YOLO26N Question Segmentation

Smaller release checkpoint that slightly outperforms the YOLO26M release on both held-out test sets.

• Task: object detection
• Classes: question
• Input size: 1280
• Base model: yolo26n

Intended use

This model is designed to detect question regions in exam booklets, worksheets, and PDF page renders. It works best on dense educational pages with clear question boundaries.

Release notes

This release checkpoint was trained for 50 epochs on the cleaned one-class question detection dataset.

Test results

Old held-out test set

Model	Precision	Recall	mAP50	mAP50-95
YOLO26N Question Segmentation	0.988	0.983	0.989	0.924

Combined held-out test set

Model	Precision	Recall	mAP50	mAP50-95
YOLO26N Question Segmentation	0.991	0.988	0.993	0.962

Usage

from ultralytics import YOLO

model = YOLO("hf://samil24/yolo26n-question-segmentation")
results = model("page.png", imgsz=1280, conf=0.25)

Included files

• best.pt: release checkpoint
• metrics_summary.json: test-set metrics for this release
• confidence_sweep_summary.json: confidence sweep outputs used during evaluation
• comparison_examples/: side-by-side qualitative examples

Data note

Some of the training data comes from public Roboflow projects used in earlier versions of this question-segmentation pipeline:

1. PDF Soru Cikarma (tanimazsinu): Link
2. WholeQuestionDetection (Gazi University): Link
3. ExamBuddy (ExamBuddy): Link
4. Questions (Terry Li): Link
5. Question Parsing from Document (Sefa): Link
6. Question Dedector (Nur Etinkaya): Link
7. Sorukes (Sorualgilama): Link
8. Question Detection (Cognizen): Link
9. Questions2 (Fiver): Link
10. Question-New (Question): Link

License

This release is published under CC-BY-4.0.