qrdet
Robust QR Detector based on YOLOv8
Decision gist · record as of 2026-08-14
Yes, if you need robust QR detection with polygon-level precision. The package is stable (Production/Stable classifier), permissively licensed, has no known vulnerabilities, and offers flexible model sizing. The aging maintenance status (last release mid-2024) is a minor concern but not a blocker if the current version meets your needs. Install friction is low. Not suitable if you also need QR decoding in the same package.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Model weights are downloaded on first use; ensure the weights_folder (default: package/.model) is writable, or specify an alternative writable path (e.g., /tmp on AWS Lambda).
- Low friction install with a pure-Python wheel.
- Depends on ultralytics, numpy, requests, tqdm, and quadrilateral-fitter.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) places no restrictions on use, modification, or distribution in commercial or private projects.
last release 2024-06-15 (790 days) · last repo commit 2025-02-23 · 193 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,588 downloads/mo, #14,214 on PyPI
Alternatives
Verify before relying
pip install qrdet
from qrdet import QRDetector
detector = QRDetector(model_size='s')
detections = detector.detect(image='path/to/image.jpg')
for det in detections:
print(det['bbox_xyxy'], det['confidence'])- Whether model weights are downloaded on first use and their total size impact on installation footprint.
- Performance characteristics (inference speed, accuracy) across the four model sizes ('n', 's', 'm', 'l').
- Compatibility with different Python versions—requires_python is unspecified in the fact sheet.
What it is and what it does
QRDet is a QR code detector built on YOLOv8 that finds and segments QR codes in images, even when they are rotated, partially obscured, or in challenging lighting. It returns detection results as dictionaries containing bounding boxes, confidence scores, and precise polygon coordinates that outline the QR code's shape. The package offers four model sizes (n, s, m, l) to trade off speed against detection capability, and allows tuning of confidence thresholds and non-maximum suppression parameters to control false positives and duplicate detections.
The core dependency chain includes ultralytics (for YOLOv8), numpy (for array operations), quadrilateral-fitter (for polygon refinement), requests (for model weight downloads), and tqdm (for progress feedback). It accepts images as numpy arrays, PIL Images, torch Tensors, file paths, URLs, or screenshots. The package is designed for detection only; if you need both detection and decoding, the description suggests looking at QReader instead.
Use it for
- Locating QR codes in product photos or inventory images for automated scanning workflows.
- Extracting precise QR code boundaries in rotated or skewed images before passing to a decoder.
- Building a QR detection pipeline in computer vision applications where confidence scores and segmentation polygons are needed.
- Processing batches of images to find all QR codes and their exact positions for downstream processing.
- Detecting QR codes in challenging conditions where simpler detectors fail.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need robust QR detection with polygon-level precision.
The package is stable (Production/Stable classifier), permissively licensed, has no known vulnerabilities, and offers flexible model sizing. The aging maintenance status (last release mid-2024) is a minor concern but not a blocker if the current version meets your needs. Install friction is low. Not suitable if you also need QR decoding in the same package.
Install
qrdet on PyPI
Before you install
Low friction install with a pure-Python wheel. Depends on ultralytics, numpy, requests, tqdm, and quadrilateral-fitter. Maintenance is aging—last release was 2024-06-15 and the repo has not been updated since 2025-02-23, though it remains active and unarchived.
Model weights are downloaded on first use; ensure the weights_folder (default: package/.model) is writable, or specify an alternative writable path (e.g., /tmp on AWS Lambda).
License in practice
MIT license (permissive) places no restrictions on use, modification, or distribution in commercial or private projects.
Quickstart
pip install qrdet
from qrdet import QRDetector
detector = QRDetector(model_size='s')
detections = detector.detect(image='path/to/image.jpg')
for det in detections:
print(det['bbox_xyxy'], det['confidence'])Verify before relying
- Whether model weights are downloaded on first use and their total size impact on installation footprint.
- Performance characteristics (inference speed, accuracy) across the four model sizes ('n', 's', 'm', 'l').
- Compatibility with different Python versions—requires_python is unspecified in the fact sheet.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesultralyticsquadrilateral-fitternumpyrequeststqdm |
| Maintenance | Aging 790 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 81,588 / month, #14,214 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Multimedia :: GraphicsTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Image RecognitionTopic :: Software Development :: Libraries :: Python ModulesTopic :: UtilitiesTyping :: Typed |
Evidence: qrdet-2.5-py3-none-any.whl
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See also ddddocr · deskew · qreader · quadrilateral-fitter · qudida · pybboxes · yolov5 · boxmot · nudenet · cnstd