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Smaller release checkpoint that slightly outperforms the YOLO26M release on both held-out test sets.
Fonte do modelo
Descrição da fonte
Smaller release checkpoint that slightly outperforms the YOLO26M release on both held-out test sets.
question1280yolo26nFontes
1 fonteVerificado 16 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechosThis 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 was trained for 50 epochs on the cleaned one-class question detection dataset.
| Model | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|
| YOLO26N Question Segmentation | 0.988 | 0.983 | 0.989 | 0.924 |
| Model | Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|---|
| YOLO26N Question Segmentation | 0.991 | 0.988 | 0.993 | 0.962 |
from ultralytics import YOLO
model = YOLO("hf://samil24/yolo26n-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: - yolo26n --- # 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 ```python 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](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): [Link](ht...
Source context: 7 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo samil24/yolo26n-question-segmentation