pybboxes
Light Weight Toolkit for Bounding Boxes
Decision gist · record as of 2026-08-14
Yes. PyBboxes is a focused, low-friction utility with no known vulnerabilities, permissive MIT licensing, and stable production status. It solves a concrete problem in computer vision workflows—format conversion and box geometry—that most practitioners encounter. Dormant maintenance is acceptable for a mature, feature-complete library; the last commit (2024-10-07) is recent enough to indicate active monitoring. Install if you work with multiple bounding box formats or need reliable geometric operations.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.8.
- Conversion between normalized formats (YOLO, FiftyOne, Albumentations) requires image_size to be set.
- Low friction install with three stable dependencies (numpy, pycocotools, pyyaml).
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license in distributions.
last release 2024-10-07 (676 days) · last repo commit 2024-10-07 · 154 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 118,906 downloads/mo, #12,099 on PyPI
Alternatives
Verify before relying
pip install pybboxes
from pybboxes import BoundingBox
my_coco_box = [98, 345, 322, 117]
coco_bbox = BoundingBox.from_coco(*my_coco_box, image_size=(640, 480))
voc_bbox = coco_bbox.to_voc()- Whether pycocotools is a hard requirement or optional for specific use cases
- Performance characteristics when handling large batches of bounding boxes
- Whether annotation file conversion supports all three formats (YOLO, COCO, VOC) bidirectionally
What it is and what it does
PyBboxes is a lightweight toolkit for working with bounding boxes in computer vision workflows. It handles conversion between five common bounding box formats used across popular frameworks and datasets: COCO (top-left + width/height), YOLO (center + normalized width/height), VOC (top-left + bottom-right), FiftyOne (normalized COCO), and Albumentations (normalized VOC). The package also provides geometric operations like IoU computation, area calculation, and intersection/union calculations between boxes.
The library is built on numpy, pycocotools, and pyyaml and supports both strict and lenient modes for handling out-of-bounds boxes. It includes an annotation file converter for batch processing YOLO, COCO, and VOC format files. Typical use cases are converting between dataset formats during preprocessing, computing overlap metrics during model evaluation, and batch-converting annotation files across different computer vision frameworks.
Use it for
- Convert bounding boxes between COCO and YOLO formats when switching between dataset sources or training frameworks
- Compute IoU and intersection metrics for non-maximum suppression or model evaluation pipelines
- Batch-convert annotation files across YOLO, COCO, and VOC formats during dataset preparation
- Validate and filter out-of-bounds boxes with strict mode during data cleaning
- Perform geometric operations (area, union, difference) on detected objects for post-processing
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PyBboxes is a focused, low-friction utility with no known vulnerabilities, permissive MIT licensing, and stable production status. It solves a concrete problem in computer vision workflows—format conversion and box geometry—that most practitioners encounter. Dormant maintenance is acceptable for a mature, feature-complete library; the last commit (2024-10-07) is recent enough to indicate active monitoring. Install if you work with multiple bounding box formats or need reliable geometric operations.
Install
pybboxes on PyPI
Before you install
Low friction install with three stable dependencies (numpy, pycocotools, pyyaml). Maintenance is dormant—last commit was 2024-10-07 with no activity since—but the package is marked Production/Stable and has seen no breaking changes in recent releases.
Requires Python >= 3.8. Conversion between normalized formats (YOLO, FiftyOne, Albumentations) requires image_size to be set.
License in practice
MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license in distributions.
Quickstart
pip install pybboxes
from pybboxes import BoundingBox
my_coco_box = [98, 345, 322, 117]
coco_bbox = BoundingBox.from_coco(*my_coco_box, image_size=(640, 480))
voc_bbox = coco_bbox.to_voc()Verify before relying
- Whether pycocotools is a hard requirement or optional for specific use cases
- Performance characteristics when handling large batches of bounding boxes
- Whether annotation file conversion supports all three formats (YOLO, COCO, VOC) bidirectionally
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpypycocotoolspyyaml |
| Maintenance | Dormant 676 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 118,906 / month, #12,099 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/ResearchOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: EducationTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: pybboxes-0.2.0-py3-none-any.whl
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See also rectangle-packer · albumentations · ultralytics · qrdet · PyWinBox · pycocotools · boxmot · country-converter · faster-coco-eval · pycocoevalcap