YOLO26M Question Segmentation
Larger release checkpoint with stronger localization than the older public YOLO11m baseline.
- Task: object detection
- Classes:
question - Input size:
1280 - Base model:
yolo26m
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 is a cleaned fine-tune of a previously trained YOLO26M run, finalized without mosaic augmentation.
Compared with the previously published HF YOLO11M baseline, this release improves held-out mAP50-95 by +0.022 on the old test set and +0.057 on the combined test set.
Test results
Old held-out test set
| Model | Size | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|---|
| YOLO11M Question Segmentation v1 | 116 MB / 20.1M params | 0.962 | 0.980 | 0.979 | 0.898 |
| YOLO26M Question Segmentation v2 | 168 MB / 21.8M params | 0.990 | 0.982 | 0.988 | 0.920 |
| YOLO26S Question Segmentation v2 | 20 MB / 9.9M params | 0.988 | 0.983 | 0.990 | 0.923 |
| YOLO26N Question Segmentation v2 | 5.3 MB / 2.5M params | 0.988 | 0.983 | 0.989 | 0.924 |
Combined held-out test set
| Model | Size | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|---|
| YOLO11M Question Segmentation v1 | 116 MB / 20.1M params | 0.962 | 0.973 | 0.984 | 0.900 |
| YOLO26M Question Segmentation v2 | 168 MB / 21.8M params | 0.987 | 0.978 | 0.992 | 0.957 |
| YOLO26S Question Segmentation v2 | 20 MB / 9.9M params | 0.988 | 0.991 | 0.993 | 0.963 |
| YOLO26N Question Segmentation v2 | 5.3 MB / 2.5M params | 0.991 | 0.988 | 0.993 | 0.962 |
Benchmark graphs
Confidence guidance
From the confidence sweep used in this release:
- Best benchmark confidence for YOLO26S and YOLO26N is
conf=0.001(highest mAP50-95 on both held-out test sets). - For practical inference, start with
conf=0.25; useconf=0.20if you want fewer missed borderline boxes.
Qualitative examples
The newer YOLO26 checkpoints improve most clearly in two cases:
- Less false positives on pages with no questions.
- Better recovery of harder placements such as one-row questions and questions that occupy an entire column.
Example no-question page: GT + YOLO11M Question Segmentation v1 + YOLO26M Question Segmentation v2 + YOLO26S Question Segmentation v2 + YOLO26N Question Segmentation v2. The YOLO11M panel uses the earlier low-threshold prediction pass and shows two false positives; the YOLO26 checkpoints stay empty on the same page.
Usage
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
weights = hf_hub_download(repo_id="erayyapagci/yolo26m-question-segmentation-v2", filename="best.pt")
model = YOLO(weights)
results = model("page.png", imgsz=1280, conf=0.25)
Direct URL loading also works:
from ultralytics import YOLO
model = YOLO("https://huggingface.co/erayyapagci/yolo26m-question-segmentation-v2/resolve/main/best.pt")
results = model("page.png", imgsz=1280, conf=0.25)
Data note
Some of the training data comes from public Roboflow projects used in earlier versions of this question-segmentation pipeline:
- PDF Soru Cikarma (tanimazsinu): Link
- WholeQuestionDetection (Gazi University): Link
- ExamBuddy (ExamBuddy): Link
- Questions (Terry Li): Link
- Question Parsing from Document (Sefa): Link
- Question Dedector (Nur Etinkaya): Link
- Sorukes (Sorualgilama): Link
- Question Detection (Cognizen): Link
- Questions2 (Fiver): Link
- Question-New (Question): Link
License
This release is published under CC-BY-4.0.