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idealo avatar

idealo/imagededup

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idealo.github.io/imagededup↗

Imagededup

imagededup is a Python library used for finding exact and near-duplicate images. It provides utilities for generating image fingerprints, computing neural embeddings, and evaluating the precision of deduplication processes.

The tool utilizes perceptual hashing to identify visually similar files regardless of size or format and employs deep learning models to encode images into vectors for high-accuracy similarity searches. It includes a system for measuring the precision and recall of these processes by comparing results against known ground truth datasets.

The library covers broader capabilities for image encoding generation, duplicate identification, and visual search implementation. It also includes a visualization tool for displaying images identified as duplicates to facilitate manual verification.

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Features

  • Perceptual Visual Fingerprinting - Computes condensed fingerprints of image structures to identify visually similar files regardless of size or format.
  • Image Encoder Embedding Extractions - Uses deep learning models to encode images into vectors for high-accuracy similarity search.
  • High-Dimensional Text Embeddings - Transforms image pixels into high-dimensional vectors using deep learning models to capture semantic similarities.
  • Perceptual Image Deduplication - Provides a Python library for finding exact and near-duplicate images using perceptual hashing and neural embeddings.
  • Exact Image Deduplications - Finds exact and near-duplicate images in a collection using neural networks or perceptual hashing.
  • Image Duplicate Detection - Finds exact and near-duplicate images within large collections using perceptual hashing and neural network models.
  • Image Encoding Generation - Creates digital fingerprints for images using hashing or neural models to enable fast comparisons.
  • Vector Distance Metrics - Determines image similarity by calculating the mathematical distance between encoding vectors in a latent space.
  • Image Deduplication Evaluation - Includes a system to measure precision and recall by comparing results against known ground truth datasets.
  • Dataset Curators - Removes duplicate samples from training sets to prevent data leakage and improve machine learning model quality.
  • Image Fingerprint Indexing - Provides a searchable index of image fingerprints for fast lookup of duplicate candidates.
  • Visual Semantic Search - Creates digital fingerprints to enable fast retrieval and comparison across large sets of visual data.
  • Deduplication Accuracy Metrics - Measures the precision and recall of deduplication processes against known ground truth datasets.
  • Deduplication Quality Benchmarking - Measures the effectiveness of the deduplication process by comparing output against ground truth mapping.
  • Computer Vision - Tool for identifying and removing duplicate images.
  • Deep Learning and Computer Vision - Detects duplicate images using perceptual hashing.
  • Image Processing and Manipulation - Identifies duplicate images using perceptual hashing.
5,642 stars·477 forks·Python·Apache-2.0·69 views

Star history

Star history chart for idealo/imagededupStar history chart for idealo/imagededup

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does idealo/imagededup do?

imagededup is a Python library used for finding exact and near-duplicate images. It provides utilities for generating image fingerprints, computing neural embeddings, and evaluating the precision of deduplication processes.

What are the main features of idealo/imagededup?

The main features of idealo/imagededup are: Perceptual Visual Fingerprinting, Image Encoder Embedding Extractions, High-Dimensional Text Embeddings, Perceptual Image Deduplication, Exact Image Deduplications, Image Duplicate Detection, Image Encoding Generation, Vector Distance Metrics.

Which projects share features with idealo/imagededup?

Projects with overlapping indexed features include: librephotos/librephotos — LibrePhotos is a self-hosted photo management system designed for organizing, tagging, and searching photos and videos… lightly-ai/lightly — Lightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image… voxel51/fiftyone — FiftyOne is a visual tool for curating, analyzing, and managing image and video datasets for machine learning model… kornia/kornia — Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision… johannesbuchner/imagehash — ImageHash. obss/sahi — SAHI is a sliced inference framework and computer vision pipeline designed to detect small objects in high-resolution…

Projects sharing features with Imagededup

These projects share indexed features with Imagededup. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • librephotos/librephotosLibrePhotos avatar

    LibrePhotos/librephotos

    7,927View on GitHub↗

    LibrePhotos is a self-hosted photo management system designed for organizing, tagging, and searching photos and videos stored on personal servers. It functions as an AI-powered media organizer and a multi-user media server, providing isolated accounts and secure permissions so multiple people can manage private collections on a single system. The project differentiates itself through a semantic image search engine that allows for natural language queries based on deep metadata analysis. It incorporates computer vision to automatically detect faces and identify objects, while utilizing geospat

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  • lightly-ai/lightlylightly-ai avatar

    lightly-ai/lightly

    3,684View on GitHub↗

    Lightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image datasets and train models on unlabeled data. It functions as a PyTorch vision library and dataset management SDK, providing tools to convert raw images into high-dimensional vectors for similarity search, visualization, and feature extraction. The project implements a variety of self-supervised architectures, including MoCo, SimCLR, VICReg, Barlow Twins, and masked image modeling. It distinguishes itself by combining these learning frameworks with active learning capabilities,

    Pythoncomputer-visioncontrastive-learningcontributions-welcome
    View on GitHub↗3,684
  • voxel51/fiftyonevoxel51 avatar

    voxel51/fiftyone

    10,841View on GitHub↗

    FiftyOne is a visual tool for curating, analyzing, and managing image and video datasets for machine learning model training. It serves as a platform for identifying annotation errors, refining ground truth labels, and evaluating vision model performance by comparing predictions against ground truth to identify failure modes. The system functions as a containerized data platform that supports team collaboration on large-scale visual datasets in a cloud environment. It includes specialized capabilities for exploring high-dimensional embeddings to discover data clusters and retrieve correspondi

    Python
    View on GitHub↗10,841
  • johannesbuchner/imagehashJohannesBuchner avatar

    JohannesBuchner/imagehash

    3,845View on GitHub↗

    ImageHash

    Python
    View on GitHub↗3,845
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