Title: Hi HN!<p>We&#x27;re Bert and Otto, founders of Segments.ai (<a href="https:&#x2F;&#x2F;segments.ai" rel="nofollow">https:&#x2F;&#x2F;segments.ai</a>). 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&#x2F;VR devices.<p>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.<p>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&#x27;re constantly tweaking and A&#x2F;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: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;8u1XHU7ueqU" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;8u1XHU7ueqU</a><p>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: <a href="https:&#x2F;&#x2F;youtu.be&#x2F;sCbNp9EDtjE?t=42" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;sCbNp9EDtjE?t=42</a><p>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.<p>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.<p>The easiest way to try out our platform is by creating an account (<a href="https:&#x2F;&#x2F;segments.ai&#x2F;join" rel="nofollow">https:&#x2F;&#x2F;segments.ai&#x2F;join</a>) and playing around with the example images.<p>We would love to hear your thoughts on what we&#x27;ve built!<p>Bert
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