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Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

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Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#21

Post by gkk »

bertdb wrote: post #20
gkk wrote: post #16

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…

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.

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.

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#22

Post by bertdb »

gkk wrote: post #21
bertdb wrote: post #20

Earlier quoted context omitted.

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.

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.

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.

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#23

Post by bertdb »

auraham wrote: post #15

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, ma…

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...

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#24

Post by crubier »

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!

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#25

Post by bertdb »

crubier wrote: post #24

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 idea…

Thanks! Would be great to learn more about your use case, we'll be in touch with you.

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#26

Post by the_cat_kittles »

man... every time i label images i think to myself "why cant i use the model to guess ahead the regions and then just correct them"... such a no brainer. but your implementation based on the video is so much more elegant than i ever thought of. well done!

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#27

Post by iujjkfjdkkdkf »

auraham wrote: post #15

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, ma…

If you haven't already, take a look at the detectron2 models and tutorials, especially the colab they have. It is a great way to get started.

https://github.com/facebookresearch/detectron2

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#28

Post by ottodebals »

the_cat_kittles wrote: post #26

man... every time i label images i think to myself "why cant i use the model to guess ahead the regions and then just correct them"... such a no brainer. but your implementation based on the video is so much more elegant than i ever thought of. well done!

Thanks - feel free to give it a try and let us know your suggestions!

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#29

Post by auraham »

bertdb wrote: post #23
auraham wrote: post #15

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, ma…

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.i…

Thanks, your tutorial seems great!

Re: Launch HN: Segments.ai (YC W21) – Build better datasets for image segmentation

#30

Post by auraham »

iujjkfjdkkdkf wrote: post #27
auraham wrote: post #15

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, ma…

If you haven't already, take a look at the detectron2 models and tutorials, especially the colab they have. It is a great way to get started. https://github.com/facebookresearch/detectron2

Thanks! I was not aware of that model.
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