digiKam is an advanced open-source digital photo management application that runs on Linux, Windows, and MacOS. The application provides a comprehensive set of tools for importing, managing, editing, and sharing photos and raw files.
Based on our record, digiKam should be more popular than Scale Nucleus. It has been mentiond 9 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Digikam seems ideal for this https://digikam.org/. - Source: Hacker News / 12 months ago
I have all of my photos (with the exception of smartphone photos... ugh) in a nicely constructed set of folders \photos\yyyy\yyyymmmdd\ then the folder made by the camera, etc. I've got a small python script to generate the folders. I use Digikam[1] to do facial recognition and tagging on them. It's finally gotten to the point where it doesn't crash all the time writing metadata, and the facial recognition is... - Source: Hacker News / about 1 year ago
I use digikam for my own personal library. I’m not sure if it’s able to be run from a server, but I know you can hook up a NAS to it to manage it. Can tag photos, rank, organize, etc. Source: about 1 year ago
Check out digiKam. It has photo editing tools as well, but the main focus is photo management. Also it is free and open source. Source: about 2 years ago
But with that many photos, I'd suggest a more fully featured digital asset management (DAM) program. Lightroom (paid), DigiKam, or DarkTable (both free) are good choices. PhoTool's IMatch (paid) also uses exiftool and is extremely powerful with regards to metadata. Source: about 2 years ago
At Scale we built a tool for model debugging in computer vision called Nucleus (scale.com/nucleus) designed exactly for this, which is free try out if you're curious to see where your model predictions are most at odds with your ground truth. Source: over 2 years ago
To address your point about gathering edge cases, which can also be defined as cases of low model fidelity for our use cases, there is active learning and tools such as Aquarium Learning and Scale Nucleus which make it easy to implement into workflows. Source: almost 3 years ago
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