Foundation models are large models that you can use without prior training. You can use foundation models to auto-label data for use in training a smaller, real-time vision model.
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Semantic segmentation models assign labels for each pixel in an image. This information is used to identify exactly where an object is in an image. With semantic segmentation, different instances of the same object type (i.e. a tree or a screw) can be uniquely identified.
Semantic segmentation models are useful when you need to know exactly where an object is an image and be able to distinguish between different instances of that object. For example, if there are two birds in an image, you should be able to distinguish between them both, instead of each bird being given the label “bird” and grouped together.
Here are a few scenarios where semantic segmentation is useful:
The SegFormer model represents the state-of-the-art in semantic segmentation. SegFormer is designed to work on images of any resolution without having an impact on inference performance.
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