qudida
QUick and DIrty Domain Adaptation
Decision gist · record as of 2026-08-14
Yes, if you need a lightweight, quick pixel-level domain adaptation tool for prototyping or augmentation and are comfortable with dormant maintenance. No, if you require active support, production-grade accuracy, or a clearly licensed library. Verify the license in the repository first.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires opencv-python-headless and scikit-learn; transformer must be compatible with pixel-level data.
- Installation is straightforward with low friction.
- The package is dormant (last commit 2023-12-30) but not archived, so it remains available; however, expect no active maintenance or updates.
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is available. You should verify the actual license terms in the repository before use, particularly if you plan to redistribute or use commercially.
last release 2021-08-09 (1831 days) · last repo commit 2023-12-30 · 23 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 539,552 downloads/mo, #6,107 on PyPI
Alternatives
Verify before relying
pip install qudida
from qudida import DomainAdapter
from sklearn.decomposition import PCA
adapter = DomainAdapter(transformer=PCA(n_components=1), ref_img=reference_image)
result = adapter(source_image)- Whether the package works reliably with modern scikit-learn and opencv-python-headless versions despite dormant status.
- Actual license terms and any redistribution restrictions.
- Real-world performance or accuracy compared to other domain adaptation approaches.
What it is and what it does
QuDiDA is a lightweight library for adapting image appearance at the pixel level by applying scikit-learn transformers to match a reference image's style. It wraps numpy, scikit-learn, and opencv-python-headless to enable quick, naive domain adaptation—treating it as an image augmentation technique rather than a production-grade solution.
The library works by taking a source image and a reference image, then using a scikit-learn transformer (such as PCA or other decomposition methods) to adjust the source image's pixel values to align with the target's characteristics. It is designed for speed and simplicity over accuracy, and the author notes it has not been tested in public benchmarks.
Use it for
- Augment training datasets by adapting source images to match target domain appearance before model training.
- Quick style transfer between images using decomposition-based transformers without deep learning.
- Preprocessing step to reduce visual domain shift in computer vision pipelines.
- Rapid prototyping of domain adaptation ideas without heavy dependencies.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a lightweight, quick pixel-level domain adaptation tool for prototyping or augmentation and are comfortable with dormant maintenance.
No, if you require active support, production-grade accuracy, or a clearly licensed library. Verify the license in the repository first.
Install
qudida on PyPI
Before you install
Installation is straightforward with low friction. The package is dormant (last commit 2023-12-30) but not archived, so it remains available; however, expect no active maintenance or updates.
Requires opencv-python-headless and scikit-learn; transformer must be compatible with pixel-level data.
License in practice
License status is unclear—no SPDX identifier or raw license text is available. You should verify the actual license terms in the repository before use, particularly if you plan to redistribute or use commercially.
Quickstart
pip install qudida
from qudida import DomainAdapter
from sklearn.decomposition import PCA
adapter = DomainAdapter(transformer=PCA(n_components=1), ref_img=reference_image)
result = adapter(source_image)Verify before relying
- Whether the package works reliably with modern scikit-learn and opencv-python-headless versions despite dormant status.
- Actual license terms and any redistribution restrictions.
- Real-world performance or accuracy compared to other domain adaptation approaches.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.5.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpyscikit-learntyping-extensionsopencv-python-headless |
| Maintenance | Dormant 1,831 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 539,552 / month, #6,107 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: PythonProgramming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: qudida-0.0.4-py3-none-any.whl
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