CNN works best for near duplicates and datasets containing transformations.Generally speaking, following conclusions can be made: The next releases have significant changes to all methods, so the current benchmarks may not hold.ĭetailed benchmarks on speed and classification metrics for different methods have been provided in the documentation. Update: Provided benchmarks are only valid upto imagededup v0.2.2. utils import plot_duplicates plot_duplicates( image_dir = 'path/to/image/directory',įor more examples, refer this part of theįor more detailed usage of the package functionality, refer: ⏳ Benchmarks # plot duplicates obtained for a given file using the duplicates dictionary from imagededup. find_duplicates( encoding_map = encodings) # Find duplicates using the generated encodings duplicates = phasher. encode_images( image_dir = 'path/to/image/directory') # Generate encodings for all images in an image directory encodings = phasher. Install imagededup from PyPI (recommended):įrom imagededup.There are two ways to install imagededup: It is distributed under the Apache 2.0 license. Imagededup is compatible with Python 3.8+ and runs on Linux, MacOS X and Windows. Plotting duplicates found for a given image file.ĭetailed documentation for the package can be found at:.Framework to evaluate effectiveness of deduplication given a ground truth mapping.Generation of encodings for images using one of the above stated algorithms.Finding duplicates in a directory using one of the following algorithms:.An evaluationįramework is also provided to judge the quality of deduplication for a given dataset.įollowing details the functionality provided by the package: This package provides functionality to make use of hashing algorithms that are particularly good at finding exactĭuplicates as well as convolutional neural networks which are also adept at finding near duplicates.
Imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.
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