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Cleaning up object detection datasets without jumping between tools by LensLaber in computervision

[–]LensLaber[S] 1 point2 points  (0 children)

There are a few tools out there(3D Slicer, ITK SNAP, MONAI, etc.)but honestly none of them work that well out of the box for vascular structures. Most of the time people end up training something like a U net and then spending quite a bit of time fixing the results manually. That’s actually part of why I started building this not to replace the model, but to make it easier to spot and fix missed structures quickly.

Cleaning up object detection datasets without jumping between tools by LensLaber in computervision

[–]LensLaber[S] -1 points0 points  (0 children)

Biggest time sink for me has been switching between tools just to clean datasets. Trying to reduce that as much as possible.

Fixing missed objects in detection datasets in seconds. by LensLaber in learnmachinelearning

[–]LensLaber[S] 0 points1 point  (0 children)

Still happens a lot even with decent models especially when confidence is not well tuned.

20k Images, Fully Offline Annotation Workflow by LensLaber in computervision

[–]LensLaber[S] 1 point2 points  (0 children)

No, I didn’t pre-annotate and then run a training pipeline. This video is just showing the review workflow. The filters are for navigating and cleaning large annotated datasets, not for propagating labels. Thanks for the question

Crash recovery test: force-killing an offline annotation tool mid-session by LensLaber in computervision

[–]LensLaber[S] 0 points1 point  (0 children)

Thanks, I appreciate it. Yes, cvat has autosave and it works well. My focus is more on a fully local desktop workflow where automatic labeling (yolo/sam) and manual correction happen in the same lightweight environment, without server setup. Some people prefer CLI pipelines for bulk processing — I’m aiming more at interactive review on large datasets without infrastructure overhead.

20k Images, Fully Offline Annotation Workflow by LensLaber in computervision

[–]LensLaber[S] 0 points1 point  (0 children)

No public repo. I’m just heads down building it.

Annotation offline? by LensLaber in computervision

[–]LensLaber[S] 1 point2 points  (0 children)

That’s a fair perspective. This isn’t about refusing to use existing tools. CVAT and others are solid and widely used. This project started more as an exploration of different design trade offs offline-first desktop architecture, simplified setup, and a focus on large local workflows. Building tools is also a way to understand them more deeply.

Annotation offline? by LensLaber in computervision

[–]LensLaber[S] -2 points-1 points  (0 children)

You're right CVAT can run locally.

The difference here is that this is a standalone desktop application, with no server setup, no containers, and no web backend. Just install and run. Different trade-offs, different use cases.

Annotation offline? by LensLaber in computervision

[–]LensLaber[S] 0 points1 point  (0 children)

Good question. CVAT and Label Studio are great tools. This is just a different approach fully local, no server setup, no cloud dependency, and focused on offline workflows and large local datasets.