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

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

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