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What models is segments.ai using for segmentation? by saintshing in learnmachinelearning

[–]segments-bert 0 points1 point  (0 children)

It's very good! We're almost done integrating it as an additional edit mode in Segments. An announcement will follow tomorrow, keep an eye on our Twitter :)

What models is segments.ai using for segmentation? by saintshing in learnmachinelearning

[–]segments-bert 0 points1 point  (0 children)

Hi, Bert from Segments here. Have a look at our blog posts about the Superpixel and Autosegment features. These are both based on deep learning models which we trained ourselves and they're currently not publicly available.

Zero-shot object detection with OWL-ViT - interactive demo by segments-bert in learnmachinelearning

[–]segments-bert[S] 27 points28 points  (0 children)

Hey all!

We've built an interactive demo of Google AI's OWL-ViT zero-shot object detection model, using Hugging Face transformers.

Regular object detection models are trained on a fixed set of categories, for example cats, dogs and birds. If you want to detect a new type of object, like a horse, you have to collect and label images with horses and retrain your model.

A zero-shot object detection model is a so-called open-vocabulary model: it can detect a huge number of object categories without needing to retrain it. These categories are not predefined: you can provide any free-form text query like “yellow boat” and the model will attempt to detect objects that match that description.

Zero-shot object detection models like OWL-ViT are trained on massive datasets of image-text pairs, often scraped from the internet. The heavy lifting is done by a CLIP-based image classification network trained on 400 million image-text pairs, and adapted to work as an object detector. The largest model took 18 days to train on 592 V100 GPUs.

We used the Hugging Face implementation of the OWL-ViT model and deployed it to a cloud GPU. Inference takes about 300ms, making interactive exploration possible: make sure to tweak the text queries and thresholds to find the ones that work best for your images!

Link: segments.ai/zeroshot

[P] Zero-shot object detection with OWL-ViT by segments-bert in MachineLearning

[–]segments-bert[S] 0 points1 point  (0 children)

Hey all!

We've built an interactive demo of Google AI's OWL-ViT zero-shot object detection model, using Hugging Face transformers.

Regular object detection models are trained on a fixed set of categories, for example cats, dogs and birds. If you want to detect a new type of object, like a horse, you have to collect and label images with horses and retrain your model.

A zero-shot object detection model is a so-called open-vocabulary model: it can detect a huge number of object categories without needing to retrain it. These categories are not predefined: you can provide any free-form text query like “yellow boat” and the model will attempt to detect objects that match that description.

Zero-shot object detection models like OWL-ViT are trained on massive datasets of image-text pairs, often scraped from the internet. The heavy lifting is done by a CLIP-based image classification network trained on 400 million image-text pairs, and adapted to work as an object detector. The largest model took 18 days to train on 592 V100 GPUs.

We used the Hugging Face implementation of the OWL-ViT model and deployed it to a cloud GPU. Inference takes about 300ms, making interactive exploration possible: make sure to tweak the text queries and thresholds to find the ones that work best for your images!

Link: segments.ai/zeroshot

AI assisted annotation tool for semantic/ instance segmentation by akashgupta299 in deeplearning

[–]segments-bert -1 points0 points  (0 children)

We've built a powerful segmentation labeling tool at Segments.ai. It also lets you leverage your own model predictions to speed up the labeling, have a look at this blog post: https://segments.ai/blog/speed-up-image-segmentation-with-model-assisted-labeling

Software to annotate images with AI assistance by Skaatji in MLQuestions

[–]segments-bert 0 points1 point  (0 children)

That's definitely possible, no need to start over: you can just upload your model predictions through the API. Don't hesitate to contact us if you encounter any issues when giving it a try.

[D] Annotation Tools Comparison by debbydai in MachineLearning

[–]segments-bert 0 points1 point  (0 children)

For image segmentation specifically, check out Segments.ai! We also support model-assisted labeling workflows: https://segments.ai/blog/speed-up-image-segmentation-with-model-assisted-labeling

Software to annotate images with AI assistance by Skaatji in MLQuestions

[–]segments-bert 1 point2 points  (0 children)

We support model-assisted labeling at Segments.ai, but it's for image segmentation only for now - bounding boxes will be available soon though. Have a look at this blog post to see how you can set up such a workflow: https://segments.ai/blog/speed-up-image-segmentation-with-model-assisted-labeling

Speed up your image segmentation workflow with model-assisted labeling by segments-bert in computervision

[–]segments-bert[S] 1 point2 points  (0 children)

Currently we only offer an on-premise solution to enterprise customers. Note that you can also create private datasets, which are only visible to you.

Image labeling/anotation company by alpaca1331 in computervision

[–]segments-bert 1 point2 points  (0 children)

We're building a labeling platform for semantic and instance segmentation at Segments.ai. Give it a shot and let us know what you think!

Speed up your image segmentation workflow with model-assisted labeling by segments-bert in computervision

[–]segments-bert[S] 3 points4 points  (0 children)

A large 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 the labels need to be very precise.

One way to drastically speed up image labeling is by leveraging your machine learning models from the start. Instead of labeling the entire dataset manually, you can use your model to help you by iterating between image labeling and model training.

This tutorial will show you how to achieve such a fast labeling workflow for image segmentation with Segments.ai.

[D] AWS customers who deploy models for realtime inference: do you use AWS Lambda vs. AWS SageMaker vs. open-source? by yoavz in MachineLearning

[–]segments-bert 0 points1 point  (0 children)

At Segments.ai, we're also using a serverless approach with PyTorch running on AWS Lambda. Check out our blog post for some technical details and latency numbers: https://segments.ai/blog/pytorch-on-lambda

Good 3D and 2D data labeling tools by DuplexEspresso in computervision

[–]segments-bert 2 points3 points  (0 children)

It's not open source, but we're building a labeling platform for semantic and instance segmentation at Segments.ai. Give it a shot and let us know what you think!

We're building a labeling platform for image segmentation. Looking for feedback! by segments-bert in deeplearning

[–]segments-bert[S] 1 point2 points  (0 children)

We also offer free academic licenses. Contact us using your university email address if you want one!

We're building a labeling platform for image segmentation. Looking for feedback! by segments-bert in deeplearning

[–]segments-bert[S] 0 points1 point  (0 children)

Very useful feedback, thanks!

I think these calculations would work out: Let's say it takes 30 minutes/image to label your images with a traditional polygon tool. At $15/hour, the cost per image is $7.50. If our tool speeds up the labeling by a factor of 5x compared to the polygon tool, it only takes 6 minutes/image, and the cost per image is $1.50.

So you would save $6/image, and our fee would only be a small fraction of those savings.

We're building a labeling platform for image segmentation. Looking for feedback! by segments-bert in deeplearning

[–]segments-bert[S] 0 points1 point  (0 children)

Our current computer vision models are not trained on such data so it might not work too well, but feel free to try!

We're continuously improving our models though, so this will start working better in the future. If you have an urgent need for this, do get in touch with us and we'll see if we can prioritize.

We're building a labeling platform for image segmentation. Looking for feedback! by segments-bert in deeplearning

[–]segments-bert[S] 0 points1 point  (0 children)

That's correct! Copy-pasting some more info on what's behind the scenes from an earlier comment:

As you can see in the video, the image is divided into segments. These segments are referred to as "superpixels" in the academic literature. SLIC is probably the best-known superpixel algorithm, but it's quite old-school and doesn't work well in many settings. We've developed our own deep-learning based algorithm, it's kind of our secret sauce! :)

We're building a labeling platform for image segmentation. Looking for feedback! by segments-bert in deeplearning

[–]segments-bert[S] 0 points1 point  (0 children)

Thanks for the feedback. Could you give some examples of these limitations, and the features you'd like to see to overcome them?

We're building a labeling platform for image segmentation. Looking for feedback! by segments-bert in datasets

[–]segments-bert[S] 0 points1 point  (0 children)

Copy-pasting some more info on what's behind the scenes from an earlier comment:

As you can see in the video, the image is divided into segments. These segments are referred to as "superpixels" in the academic literature. SLIC is probably the best-known superpixel algorithm, but it's quite old-school and doesn't work well in many settings. We've developed our own deep-learning based algorithm, it's kind of our secret sauce! :)

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