We’re building a complex data labeling platform for AV and robotics teams working with multi-sensor data: images, point clouds, sequential data, and 3D scenes.
We’re hiring a Senior Product Engineer to help us ship the core web product faster and improve the quality of customer-facing labeling workflows.
You can help us solve problems like:
- Making 2D and 3D annotation workflows faster, more precise, and easier to use
- Designing point cloud tiling, autosave, undo/redo, review, filtering, and issue-tracking workflows that hold up under real labeling operations
- Improving performance where milliseconds matter: rendering, local state updates, preloading, and geometric operations
- Shaping API contracts, data models, permissions, background jobs, and operational tooling as the platform scales
This is a senior role with high ownership on a small team. To apply, please reach out to bert@segments.ai (we recently got acquired by Uber) with a resume and mention HN in your application.
Not yet, we focused on the architecture for this paper. I totally agree with you though - pixel space is generally less limiting than a latent space for diffusion, so we would expect good performance inpainting behavior and other editing tasks.
That book is great if you want to go in-depth! If you're a practitioner who wants to get to a trained model as quickly as possible, you're probably better of just following a tutorial. The official Keras tutorial on segmentation looks pretty good [1]. We also have a blog post with code samples on how to set up an image segmentation workflow with Segments.ai and Facebook's detectron2 framework [2].
1. If the segment you start dragging from is already selected, all the segments you drag through will get deselected, and vice versa.
2. Did you try changing the granularity of the segments by scrolling your mouse wheel? We've had good experiences with microscopic imagery before, happy to connect and dig a bit deeper.
1. Oh, I see. I didn't guess that's the intended behaviour. I wonder if it's not too clever.
2. Yes, then segments get too "excited" about the background noise. I would be able to make it work but with loads of manual tweaking which is, as I understand, the pain Segments wants to alleviate.
The segments you see on the screen are generated by our ML model. If your data is very noisy, our out-of-the-box model might not be the best fit. We can always improve performance by training a custom model for you on a small set of manually labeled data though.
We’re building a complex data labeling platform for AV and robotics teams working with multi-sensor data: images, point clouds, sequential data, and 3D scenes.
We’re hiring a Senior Product Engineer to help us ship the core web product faster and improve the quality of customer-facing labeling workflows.
Tech stack: Vue/React, TypeScript, Three.js/WebGL/WebGPU, TinyBase, Django.
You can help us solve problems like: - Making 2D and 3D annotation workflows faster, more precise, and easier to use - Designing point cloud tiling, autosave, undo/redo, review, filtering, and issue-tracking workflows that hold up under real labeling operations - Improving performance where milliseconds matter: rendering, local state updates, preloading, and geometric operations - Shaping API contracts, data models, permissions, background jobs, and operational tooling as the platform scales
This is a senior role with high ownership on a small team. To apply, please reach out to bert@segments.ai (we recently got acquired by Uber) with a resume and mention HN in your application.