Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation
We're Bert and Otto, founders of Segments.ai (https://segments.ai). Our platform helps computer vision teams build better datasets for image segmentation, an increasingly popular computer vision technique in the world of self-driving cars, autonomous robots, and AR/VR devices.
A large, curated dataset of labeled images is the first thing you need in any serious computer vision project. Building such datasets is a time-consuming endeavour, involving lots of manual labeling work. This is especially true for tasks like image segmentation, where every object and region in the image needs to be precisely annotated with a pixel-level segmentation mask. Manually segmenting a complex image can easily take up to an hour, even for experienced labelers. This leads to costs of tens to hundreds of thousands of dollars for labeling large datasets.
With Segments.ai, our goal is to make it easier, faster and cheaper to build such datasets. Our core product is a powerful labeling technology for image segmentation, with automation features powered by machine learning. We're constantly tweaking and A/B testing the UX to optimize for labeling speed, and see empirical speedups of 2x-10x for semantic, instance and panoptic segmentation labeling, compared to traditional labeling tools. Have a look at this video to see it in action: https://youtu.be/8u1XHU7ueqU
Furthermore, after you’ve labeled an initial dataset and trained a first ML model, you can upload your model predictions to our platform and use those as a starting point to label additional images. Our labeling technology makes it easy to correct the predictions, as opposed to labeling each image from scratch. We call this model-assisted labeling, and it allows you to obtain additional speedups by iterating quickly between data labeling and model training. More details in this video: https://youtu.be/sCbNp9EDtjE?t=42
Otto and I rolled into this space a year ago, after our PhDs in ML and computer vision. I did my PhD on Scene Understanding for Autonomous Platforms, and experienced the problems with collecting high-quality labeled datasets for image segmentation first-hand.
The market for generic labeling platforms and services is very crowded, and so with Segments.ai we’re going deep rather than broad: our focus is on image segmentation specifically, and we aim to be the best in it. We managed to carve out a niche, and have happy customers across a wide variety of industries: from pharmaceutical companies and automotive OEMs to robotics startups. Our bet is that image segmentation is a fast-growing niche.
The easiest way to try out our platform is by creating an account (https://segments.ai/join) and playing around with the example images.
We would love to hear your thoughts on what we've built!
Bert
29 comments
[ 3.3 ms ] story [ 67.2 ms ] threadAcquarium is focused more on exploring and curating your data. It integrates with external labeling providers, like us.
As a fellow belgian, I have been following you and segments closely. Congrats on being the first belgian YC company :).
From the beginning onwards I was wondering why you chose to put such an emphasis on segmentation labelling. Do you see this usecase as the Computer Vision application with the biggest (future) market or maybe the least saturated offering at the moment?
In a sense, image segmentation labels are strictly more informative than bounding box labels: you can trivially extract the containing bounding box from a segmentation mask. One big reason that segmentation labels are not used more often, is simply because they are too expensive. Labeling a bounding box requires only two clicks, while labeling a segmentation mask requires much more time with manual tools. We're trying to solve that problem.
In the future we want to dig even deeper into this problem, and expand our scope to video and 3D segmentation labeling. We believe there will be a huge need for such tools now that everyone is getting smartphones with Lidar and AR/VR capabilities in their pockets.
Congrats on YC! I'm excited to see what you build next.
will labelbox offer something around image matting?
we need a more precise GT for our datasets.
image segmentation would help, but we would love to automate/outsource the whole image matting process.
thanks.
[1] https://keras.io/examples/vision/oxford_pets_image_segmentat...
[2] https://segments.ai/blog/speed-up-image-segmentation-with-mo...
https://github.com/facebookresearch/detectron2
Excited to see you launching this! I agree on the basic premise: existing tools for segmentation labeling leave copious room for an improvement.
I just gave Segments a spin with an image data I work on at the moment. First impressions:
1. When trying to connect segments (by dragging), I seem to lose the original segment
2. Your model seems to be confused by noisy data that I happened to upload - it's a microscopy image. To a human eye it's quite clear what the areas of interest are.
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.