Digital Inspector
Automatic detection of signatures, stamps, and QR codes in business documents (Russian/Kazakh).
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 6,04–83,6 MB
- Versão
- vBest
- Atualizado
- 21 de out. de 2026
- Fonte
- Hugging Face
Descrição da fonte
YOLOv8 Document Inspector
Automatic detection of signatures, stamps, and QR codes in business documents (Russian/Kazakh).
Results (Run 3 — Conservative Augmentation)
| Model | mAP@50 | mAP@50-95 | AP Signature | AP Stamp | ms/img | Params |
|---|---|---|---|---|---|---|
| y8n_1024 ⭐ | 0.881 | 0.669 | 0.355 | 0.982 | 237 | 3.2M |
| y8m_1024 | 0.872 | 0.649 | 0.378 | 0.920 | 308 | 25.9M |
| y8l_1024 | 0.858 | 0.642 | 0.302 | 0.981 | 316 | 43.7M |
| y8s_1024 | 0.831 | 0.630 | 0.354 | 0.905 | 249 | 11.2M |
| y8s_640 | 0.823 | 0.615 | 0.333 | 0.897 | 225 | 11.2M |
| y8s_768 | 0.836 | 0.596 | 0.317 | 0.875 | 233 | 11.2M |
Speed vs Accuracy
Pareto
Performance Comparison
Bar Chart
Training Curves
Curves
Precision vs Recall
Scatter
Detection Examples
Detections
Key Findings
| Run | Augmentation | mAP@50-95 |
|---|---|---|
| Run 1 | None | 0.650 |
| Run 2 | Mosaic + copy-paste | 0.252 ❌ |
| Run 3 | Conservative | 0.669 ✅ |
y8n beats y8l: 3.2M params model outperforms 43.7M params model in both speed (237ms vs 316ms) and accuracy (0.669 vs 0.642).
Usage
from ultralytics import YOLO
model = YOLO("models/y8n_1024/best.pt")
results = model.predict("document.jpg", imgsz=1024, conf=0.25)
results[0].show()
Training Config (Run 3)
model.train(
imgsz=1024, epochs=150, patience=30,
degrees=3.0, translate=0.05, scale=0.2,
hsv_v=0.2, hsv_s=0.1,
mosaic=0.0, # OFF — harmful for documents
fliplr=0.0, # OFF — text would mirror
flipud=0.0,
)
Dataset
Provided during a hackathon competition. Contains annotated Russian/Kazakh business documents. Dataset is not publicly available due to privacy constraints.
Source code: github.com/AlihanSDev/digital-inspector
Fontes
1 fonte
Fontes
1 fonteVerificado 21 de out.
Artefatos de modelo
6 artefatos
Artefatos de modelo
6 artefatosTrechos de fonte
2 trechos
Trechos de fonte
2 trechos--- license: apache-2.0 tags: - object-detection - document-analysis - yolov8 - stamps - signatures language: - ru - kk metrics: - map pipeline_tag: object-detection --- # YOLOv8 Document Inspector Automatic detection of **signatures**, **stamps**, and **QR codes** in business documents (Russian/Kazakh). ## Results (Run 3 — Conservative Augmentation) | Model | mAP@50 | mAP@50-95 | AP Signature | AP Stamp | ms/img | Params | |-------|--------|-----------|--------------|----------|--------|--------| | **y8n_1024** ⭐ | **0.881** | **0.669** | 0.355 | 0.982 | **237** | 3.2M | | y8m_1024 | 0.872 | 0.649 | 0.378 | 0.920 | 308 | 25.9M | | y8l_1024 | 0.858 | 0.642 | 0.302 | 0.981 | 316 | 43.7M | | y8s_1024 | 0.831 | 0.630 | 0.354 | 0.905 | 249 | 11.2M | | y8s_640 | 0.823 | 0.615 | 0.333 | 0.897 | 225 | 11.2M | | y8s_768 | 0.836 | 0.596 | 0.317 | 0.875 | 233 | 11.2M | ## Speed vs Accuracy  ## Performance Comparison  ## Training Curves  ## Precision vs Recall  ## Detection Examples  ## Key Findings | Run | Augmentation | mAP@50-95 | |-----|-------------|-----------| | Run 1 | None | 0.650 | | Run 2 | Mosaic + copy-paste | 0.252 ❌ | | **Run 3** | **Conservative** | **0.669** ✅ | **y8n beats y8l:** 3.2M params model outperforms 43.7M params model in both speed (237ms vs 316ms) and accuracy (0.669 vs 0.642). ## Usage ```python from ultralytics import YOLO model = YOLO("models/y8n_1024/best.pt") results = model.predict("document.jpg", imgsz=1024, conf=0.25) results[0].show() ``` ## Training Config (Run 3) ```python model.train( imgsz=1024, epochs=150, patience=30, degrees=3.0, translate=0.05, scale=0.2, hsv_v=0.2, hsv_s=0.1, mosaic=0.0, # OFF — harmful for documents fliplr=0.0, # OFF — text would mirror flipud=0.0, ) ``` ## Dataset Provided during a hackathon competition. Contains annotated Russian/Kazakh business documents. Dataset is not publicly available due to privacy constraints. Source code: [github.com/AlihanSDev/digital-inspector](https://github.com/AlihanSDev/digital-inspector)
Source context: 0 downloads · 0 likes · Pipeline object-detection · Repo AlihanSDev/digital-inspector
