That brings panlabel to 13 supported formats with full read, write, and auto-detection. Single binary, no Python dependencies.

This is the kind of project I enjoy just steadily plodding away at — ticking off one format at a time until every common object detection annotation format is covered.

https://github.com/strickvl/panlabel

#ObjectDetection #Rust #MachineLearning #ComputerVision #OpenSource

Best part is what we can do now that we couldn't before — blog posts through git, a searchable LLMOps database with real filtering, preview URLs for every PR. Our designer said it best: "It feels much nicer and powerful to work on the website now, and also flexible to make new layouts and whatever ideas that come to our minds without the Webflow restrictions."

Wrote up the whole process including the parts that went wrong:
https://www.zenml.io/blog/how-i-rebuilt-zenml-io-in-a-week-with-claude-code

Not going to change the world, but it might save someone a few hours of debugging coordinate transforms or prevent silent data corruption between tools.

Full roadmap and install instructions in the repo. If you work with annotated datasets and have hit similar pain points, would be curious to hear what formats or features would be most useful.

What it does:

→ Convert between annotation formats (focusing on object detection first, but segmentation and classification coming soon)

→ Validate your datasets
→ Generate statistics
→ Semantic diff between dataset versions
→ Create random or stratified subsets

There are a ridiculous number of object detection formats out there, and each one has its own quirks about how it handles bounding boxes, coordinates, or class mappings. I'm working through them slowly, format by format.

The reasoning isn't strong enough for gnarly bugs, but the speed makes it useful for a different class of task. Still early days figuring out where it fits.

Are you using Codex Spark? Has it carved out a specific role in your workflow, or is it just another option you reach for occasionally?