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Larger release checkpoint with stronger localization than the older public YOLO11m baseline.
Model source
Source description
Larger release checkpoint with stronger localization than the older public YOLO11m baseline.
question1280yolo26mSources
1 sourceVerified Sep 16
Model artifacts
1 artifactSource excerpts
2 excerptsThis 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.
This release checkpoint is a cleaned fine-tune of a previously trained YOLO26M run, finalized without mosaic augmentation.
| Model | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|
| YOLO26M Question Segmentation | 0.990 | 0.982 | 0.988 | 0.920 |
| Model | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|
| YOLO26M Question Segmentation | 0.987 | 0.978 | 0.992 | 0.957 |
from ultralytics import YOLO
model = YOLO("hf://samil24/yolo26m-question-segmentation")
results = model("page.png", imgsz=1280, conf=0.25)
best.pt: release checkpointmetrics_summary.json: test-set metrics for this releaseconfidence_sweep_summary.json: confidence sweep outputs used during evaluationcomparison_examples/: side-by-side qualitative examplesSome of the training data comes from public Roboflow projects used in earlier versions of this question-segmentation pipeline:
This release is published under CC-BY-4.0.
--- library_name: ultralytics pipeline_tag: object-detection license: cc-by-4.0 tags: - yolo - object-detection - document-analysis - pdf - question-segmentation - turkish datasets: - custom base_model: - yolo26m --- # 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. ## Test results ### Old held-out test set | Model | Precision | Recall | mAP50 | mAP50-95 | | --- | --- | --- | --- | --- | | YOLO26M Question Segmentation | 0.990 | 0.982 | 0.988 | 0.920 | ### Combined held-out test set | Model | Precision | Recall | mAP50 | mAP50-95 | | --- | --- | --- | --- | --- | | YOLO26M Question Segmentation | 0.987 | 0.978 | 0.992 | 0.957 | ## Usage ```python from ultralytics import YOLO model = YOLO("hf://samil24/yolo26m-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](https://universe.roboflow.com/tanimazsinu/pdf-soru-cikarma-wmwfu) 2. **WholeQuestionDetection** (Gazi University): [Link](https://universe.roboflow.com/gazi-university-gi7lx/wholequestiondetection) 3. **ExamBuddy** (ExamBuddy): [Link](https://universe.roboflow.com/exambuddy/exambuddy) 4. **Questions** (Terry Li): [Link](https://universe.roboflow.com/terry-li/questions-klidd) 5. **Question Parsing from Document** (Sefa): [Link](https://universe.roboflow.com/sefa-8tkmm/question-parsing-from-document) 6. **Question Dedector** (Nur Etinkaya): [Link](https://universe.roboflow.com/nur-etinkaya/question-dedector) 7. **Sorukes** (Sorualgilama...
Source context: 11 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo samil24/yolo26m-question-segmentation