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davanstrien/
archival-index-card-detector-v4

Name: archival-index-card-detector-v4
Availability: InStock

ultralytics

object-detection

yolo

Archival Index Card Detector v4

A YOLO26n model trained to detect index cards in digitized archival document scans. This is a development version for validation before promoting to the NationalLibraryOfScotland/archival-index-card-detector repo.

Changes from v3

Expanded training data: 1015 annotations (up from 905), including 110 human-reviewed images from the late-range Al-Allan scans (images 1127+)
Better late-range recall: v3 had ~24% recall on images 1127+ due to a scan configuration shift (different background appearance). v4 specifically targets this gap.
Background diversity: Added 20 new negative examples (card backs, blank pages) from the late range

Performance

Metric	v3	v4	Delta
mAP@50	99.3%	99.5%	+0.2%
mAP@50-95	87.5%	99.2%	+11.7%
Precision	99.9%	100%	+0.1%
Recall	98.9%	98.9%	same

Best epoch: 59/100 (early stopping at 79, patience=20).

Training Details

Base model: YOLO26n (ultralytics)
Training images: 1015 (811 train, 204 val)
Positive examples: 542 (with bounding boxes)
Negative examples: 473 (card backs, blank pages, etc.)
Epochs: 79 (early stop), best at 59
Hardware: Apple M1 Pro (MPS)
Framework: ultralytics

Usage

python

from ultralytics import YOLO
from huggingface_hub import hf_hub_download

model = YOLO(hf_hub_download(
    repo_id="davanstrien/archival-index-card-detector-v4",
    filename="model.pt"
))

results = model.predict("scan.jpg")

for result in results:
    for box in result.boxes:
        x1, y1, x2, y2 = box.xyxy[0].tolist()
        confidence = box.conf[0].item()
        print(f"Card at ({x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}) conf={confidence:.2f}")

Status

Development/validation - pending comparison against v3 on late-range images before promotion to the NLS org repo.