The digiKam team has announced the release of digiKam 8.5, which brings significant enhancements and improvements to this open-source, cross-platform professional photo management software. This version includes several key features and updates aimed at enhancing users’ experience.
One of the main highlights is the enhanced Face Management feature, which integrates deep-learning models for quicker and more accurate detection and recognition of faces in images. The release incorporates the YuNet model as the default for face detection, providing more options for configuration and fine-tuning. For face recognition, SFace is now the primary deep-learning model utilized.
In addition, the face-matching algorithm has been updated, now requiring confirmation of only one face by the user before starting recognition of other detected faces.
Another improvement in digiKam 8.5 is the usability of color labels, which helps users identify important items more effectively. This is achieved through the implementation of a narrow linear gradient color bar below image thumbnails.
The update also enhances support for large TIFF files with solid identification in the database, improves handling of date and time formats, and introduces a new option to maintain pixel clarity at high zoom levels in preview mode. Additionally, it offers support for new Apple HEIF file format extensions, usability improvements in the progress manager, and better OpenCL support during intensive calculations using the OpenCV framework.
On the technical side, digiKam 8.5 features an updated Libraw RAW decoder, ExifTool for metadata management, and the Exiv2 shared library, keeping the software aligned with recent technological advancements.
For those interested, the software can be downloaded from the official website as a universal AppImage binary, allowing users to run it on virtually any GNU/Linux distribution without the need for installation. It is also available as a Flatpak app from Flathub.
For further details, users can visit the release announcement page.
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