---
library_name: ultralytics
tags:
- yolo
- detection
- document-analysis
- pdf
- question-segmentation
datasets:
- custom
metrics:
- mAP@50: 0.716
- precision: 0.971
pipeline_tag: object-detection
license: mit
---

# YOLOv11m - Question Segmentation for PDFs

A fine-tuned **YOLOv11m** model designed to detect and segment questions in Turkish educational documents (PDFs). 

- **Task**: Object Detection
- **Classes**: `question` (Single class)
- **Resolution**: `1280` x `1280`

## Model Details
- **Base Model**: `yolo11m` (Ultralytics)
- **Parameters**: ~20M
- **Training Epochs**: 44
- **Compute**: Trained for **~4 hours** on an **NVIDIA RTX 4090 Mobile** GPU.
- **Precision**: 0.971 (Very Low False Positives)
- **mAP@50**: 0.716

## Intended Use & Limitations
This model is optimized for extracting question blocks from dense test papers, worksheets, and exam booklets. 

### ✅ Best For
- **Two-Column Tests**: Standard exam layouts where questions are split into columns.
- **Dense Worksheets**: Pages packed with questions.
- **LGS Style**: It *potentially* works for single-column LGS-style next-generation questions detailed with graphics, though performance is robustest on two-column layouts.

### ⚠️ Limitations
- **Header Merging**: In some cases, the model might accidentally merge the test header/title with the first question.
- **Answer Format**: The model is heavily biased towards typical "choice-based" questions (A, B, C, D, E). It **may fail** to detect questions that lack these choice markers or follow an open-ended format.
- **Weird Layouts**: Extremely irregular layouts or overlapping text boxes might confuse the boundary checks.
- **Confidence**: It is recommended to use a confidence threshold of **0.3 - 0.4** depending on the specific test.

## Usage & Best Practices

```python
from ultralytics import YOLO
from ultralytics.utils.downloads import safe_download

# Load the model
model_url = "https://huggingface.co/erayyapagci/yolo11m-question-segmentation/resolve/main/yolov11m-question-seg.pt"
model = YOLO(safe_download(model_url))

# Run Inference
# Recommended conf: 0.3 - 0.4
results = model("page_image.jpg", imgsz=1280, conf=0.35)

# Show results
results[0].show()
```

## Example Output
Here is a side-by-side comparison on a real ÖSYM test sample:
![Example Output](ösym_comparison.png)

### Filtering False Positives (Heuristics)
To further eliminate false positives (e.g., random paragraphs detected as questions), it is highly recommended to use an **OCR** library (like Tesseract, EasyOCR, or PaddleOCR) on the cropped question image.
- **Check for Question Numbers**: verify if the text starts with a number pattern like `1.`, `2)`, `Soru 3:`.
- **Check for Choices**: verify if the text contains multiple choice markers like `A)`, `B)`, `C)`, `D)`, `E)`.
- If a detected box contains neither, it is likely a false positive (header, instruction text) and can be discarded.

## Training Data & Citations
Trained on a dataset of **14,693 images** (after strict filtering) sourced from 10 public Roboflow datasets. The data was split using **Document-Aware Splitting** to ensure no data leakage between training and validation sets.

We gratefully acknowledge the keys datasets used in this training:

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](https://universe.roboflow.com/sorualgilama/sorukes-4akor)
8.  **Question Detection** (Cognizen): [Link](https://universe.roboflow.com/cognizen/question-detection-aizzu)
9.  **Questions2** (Fiver): [Link](https://universe.roboflow.com/fiver-1lcfr/questions2)
10. **Question-New** (Question): [Link](https://universe.roboflow.com/question/question-new)