imagededup
Package for image deduplication
Decision gist · record as of 2026-08-14
Yes, if you need robust image deduplication and can tolerate the medium install friction from its deep learning dependencies. The package is stable (Production/Stable status), supports current Python versions, and has no known vulnerabilities. However, the aging maintenance status (364 days since last release) means updates and bug fixes may be slow; evaluate whether that risk is acceptable for your use case.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- torch and torchvision are heavy dependencies; initial installation and model loading may take significant time and disk space.
- Medium install friction due to 7 runtime dependencies including torch, torchvision, and scikit-learn.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute the package freely provided you include the license notice.
last release 2025-08-15 (364 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 101,056 downloads/mo, #12,965 on PyPI
Alternatives
Verify before relying
pip install imagededup
from imagededup.methods import PHash
phasher = PHash()
encodings = phasher.encode_images(image_dir='path/to/images')
duplicates = phasher.find_duplicates(encoding_map=encodings)- Whether the package's CNN models are pre-trained or require separate download/training steps.
- Memory requirements for processing large image collections.
- Performance characteristics of each hashing method on modern hardware.
- Current download volume and active user base.
What it is and what it does
imagededup is a Python package for identifying duplicate and near-duplicate images within collections. It provides multiple detection methods: hashing algorithms (PHash, DHash, WHash, AHash) for exact duplicates, and convolutional neural networks for finding near duplicates even when images have been transformed. You can generate encodings for all images in a directory, find duplicates using those encodings, and visualize results.
The package depends on torch, torchvision, Pillow, scikit-learn, PyWavelets, tqdm, and matplotlib. It runs on Linux, macOS, and Windows with Python 3.9 or later. An evaluation framework is included to assess deduplication quality against ground truth. The last release was 364 days ago and maintenance is aging, so updates may be infrequent.
Use it for
- Clean up large photo libraries by identifying and removing visually identical or very similar images.
- Detect near-duplicate images in datasets that have undergone minor transformations like compression or resizing.
- Evaluate the effectiveness of image deduplication methods using provided benchmarking and evaluation tools.
- Build custom deduplication pipelines by selecting from multiple hashing algorithms or supplying your own CNN model.
- Batch process image directories to generate perceptual encodings for downstream similarity analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need robust image deduplication and can tolerate the medium install friction from its deep learning dependencies.
The package is stable (Production/Stable status), supports current Python versions, and has no known vulnerabilities. However, the aging maintenance status (364 days since last release) means updates and bug fixes may be slow; evaluate whether that risk is acceptable for your use case.
Install
imagededup on PyPI
Before you install
Medium install friction due to 7 runtime dependencies including torch, torchvision, and scikit-learn. Prebuilt wheels available for Python 3.9–3.12 on Linux, macOS, and Windows. Last release was 364 days ago; maintenance status is aging.
Requires Python 3.9 or later. torch and torchvision are heavy dependencies; initial installation and model loading may take significant time and disk space.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute the package freely provided you include the license notice.
Quickstart
pip install imagededup
from imagededup.methods import PHash
phasher = PHash()
encodings = phasher.encode_images(image_dir='path/to/images')
duplicates = phasher.find_duplicates(encoding_map=encodings)Verify before relying
- Whether the package's CNN models are pre-trained or require separate download/training steps.
- Memory requirements for processing large image collections.
- Performance characteristics of each hashing method on modern hardware.
- Current download volume and active user base.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 7 packagestorchtorchvisionPillowtqdmscikit-learnPyWaveletsmatplotlib |
| Maintenance | Aging 364 days since the last release |
| First released | |
| Downloads | 101,056 / month, #12,965 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 :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: CythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: imagededup-0.3.3.post2-cp310-cp310-macosx_11_0_arm64.whl; imagededup-0.3.3.post2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; imagededup-0.3.3.post2-cp310-cp310-win32.whl; imagededup-0.3.3.post2-cp310-cp310-win_amd64.whl; imagededup-0.3.3.post2-cp311-cp311-macosx_11_0_arm64.whl; imagededup-0.3.3.post2-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; imagededup-0.3.3.post2-cp311-cp311-win32.whl; imagededup-0.3.3.post2-cp311-cp311-win_amd64.whl; imagededup-0.3.3.post2-cp312-cp312-macosx_11_0_arm64.whl; imagededup-0.3.3.post2-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; imagededup-0.3.3.post2-cp312-cp312-win32.whl; imagededup-0.3.3.post2-cp312-cp312-win_amd64.whl; imagededup-0.3.3.post2-cp39-cp39-macosx_11_0_arm64.whl; imagededup-0.3.3.post2-cp39-cp39-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; imagededup-0.3.3.post2-cp39-cp39-win32.whl; imagededup-0.3.3.post2-cp39-cp39-win_amd64.whl
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See also ImageHash · semhash · lpips · simhash · pixelhog · rensa · deepdiff6 · pytorchcv · blurhash-python · fiftyone