[HN Gopher] Launch HN: Segments.ai (YC W21) - Build better datas...
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Launch HN: Segments.ai (YC W21) - Build better datasets for image
segmentation
Hi HN! 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
Author : bertdb
Score : 49 points
Date : 2021-03-09 13:24 UTC (9 hours ago)
| gkk wrote:
| Hi,
|
| 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.
| bertdb wrote:
| Thanks for your feedback!
|
| 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.
| gkk wrote:
| Thanks for a quick reply!
|
| 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.
| bertdb wrote:
| 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.
| panabee wrote:
| hi there! this looks awesome. we need something like this but for
| image matting. is image matting on your roadmap?
| bertdb wrote:
| Happy to listen to what you need, feel free to shoot me an
| email.
| technologia wrote:
| Love what I've seen so far, I've tried superannotate but ended up
| opting to build my own AI assisted tools into label box, excited
| to potentially try this out.
| bertdb wrote:
| Looking forward to hearing your feedback when you give it a
| try!
| fourseventy wrote:
| There are like 4 different companies called segment or segments
| lnsp wrote:
| Agree, but at least their name is descriptive. A bit more
| originality would have done a lot of good here though.
| auraham wrote:
| Any recommendations for learning image segmentation for medical
| images? I would like to learn how to use a pre-trained Keras
| model like FCN or U-Net. However, most of the resources I've
| found so far are a bit harder to grasp. Right now, I am reading
| 'Deep Learning with PyTorch'. The second part of the book covers
| image segmentation in great detail, but sometimes is too dense. I
| am familiar with CNNs, convolutions, max pooling, and so on, but
| not with upscaling and skip connections.
| bertdb wrote:
| 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]
| https://keras.io/examples/vision/oxford_pets_image_segmentat...
|
| [2] https://segments.ai/blog/speed-up-image-segmentation-with-
| mo...
| marmada wrote:
| How does this compare to scale.ai?
| bertdb wrote:
| Scale only provides labeling services and does not provide
| labeling software that you can use with your own in-house or
| dedicated workforce team. We offer both, and our labeling
| service is even a bit cheaper thanks to our focus on speeding
| up labeling with our technology.
| Riegerb wrote:
| Hey Bert & Otto. It's Brian from Labelbox.
|
| Congrats on YC! I'm excited to see what you build next.
| bertdb wrote:
| Thanks Brian!
| panabee wrote:
| hi brian.
|
| 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.
| crubier wrote:
| Really cool! We actually planned to do something similar at
| Sterblue 2 years ago but never prioritized it because it's too
| far from our primary scope. But we definitely see the use for
| this exact tool, it's really nice. This approach of << smart
| labelling >> is really perfect to quickly obtain high quality
| segmentation datasets. Would you offer your product frontend
| labelling component as a library ? That would be ideal for us, as
| labelling interacts with other stuff in our frontend. Having it
| in our product rather than on a separate platform would be ideal.
| Congrats on the launch!
| janhenr wrote:
| Hi Bert and Otto,
|
| 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?
| bertdb wrote:
| Thanks! The existing tools on the market for image segmentation
| are not very sophisticated, so it's a niche where we can
| immediately make a difference.
|
| 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.
| p_papageorgiou wrote:
| Hey! Great idea... It's a big pain to label properly but is there
| something extra from something like labelbox.com?
| p_papageorgiou wrote:
| Or aquariumlearning.com
| bertdb wrote:
| Our biggest differentiator is our strong focus on image
| segmentation: we've put a lot of effort in creating a
| labeling interface that is optimized to speed up segmentation
| labeling, a task that is notoriously slow and expensive.
| Another thing we do differently is that we allow unlimited
| labeling for free in public datasets.
|
| Acquarium is focused more on exploring and curating your
| data. It integrates with external labeling providers, like
| us.
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