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CHANGELOG.md

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# Changelog
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## 0.3.1
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- bugfix: export agumentations
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- improved CLI inference script (better performance: model gets cached)
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- better documentation in the repo and the config files
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## 0.3.0
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- added inference script: extract embeddings from single image
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- added parameter to show samples of image augmentations, and an argument to do a training dry run

README.md

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# BioEncoder
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BioEncoder is an toolkit that leverages supervised metric learning to i) learn features from images, ii) enhance biological image classification, annd iii) identify the feature most relevant to classification. Designed for diverse and complex datasets, the package and the available metric losses can handles unbalanced classes and subtle phenotypic differences more effectively than non-metric approaches. The package includes data loaders, custom augmentation techniques, hyperparameter tuning through YAML configuration files, and rich model visualizations, providing a comprehensive solution for high-throughput analysis of biological images.
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BioEncoder is a toolkit for supervised metric learning to i) learn and extract features from images, ii) enhance biological image classification, and iii) identify the features most relevant to classification. Designed for diverse and complex datasets, the package and the available metric losses can handle unbalanced classes and subtle phenotypic differences more effectively than non-metric approaches. The package includes taxon-agnostic data loaders, custom augmentation techniques, hyperparameter tuning through YAML configuration files, and rich model visualizations, providing a comprehensive solution for high-throughput analysis of biological images.
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Preprint on BioRxiv: [https://doi.org/10.1101/2024.04.03.587987]( https://doi.org/10.1101/2024.04.03.587987)
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pyproject.toml

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requires-python = "==3.9.*"
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keywords = ["metric learning", "biology"]
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dynamic = ["dependencies"]
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version = "0.3.0"
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version = "0.3.1"
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[project.urls]
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"Homepage" = "https://github.com/agporto/BioEncoder"

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