In this article, we will define image segmentation, discover the right metrics to use in these tasks, build an end-to-end pipeline that can be us

Image segmentation

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2022-09-22 01:00:03

In this article, we will define image segmentation, discover the right metrics to use in these tasks, build an end-to-end pipeline that can be used as a template for handling image segmentation problems, and talk about some useful applications of it.

Computer vision is a popular domain, it is very known for its fast development and expansion. Over the past few years, new state-of-the-art libraries continue to be released, and it is the gateway into deep learning for many great data scientists. One of the most popular types of computer vision is image segmentation.

In this article, we will define image segmentation and Segmentation_models_PyTorch, discover the right metrics to use in these tasks, and demonstrate an end-to-end pipeline that can be used as a template for handling image segmentation problems. We will walk through the necessary steps, from data preparation to model setup using the Segmentation_models_pytorch package, which will make our task easier, to visualization of  results. Lastly, we will talk about some useful applications of image segmentation. Apart from being cool and informative.

We can consider image segmentation as a classification task at the pixel level where we classify each pixel in an image, assigning it to a corresponding class, so if we have a 256*192 image, we actually have to make 48768-pixel classifications. Depending on the task, we can have a semantic segmentation where we have to classify every pixel in a photo or an instance segmentation where we only have to classify the pixels representing objects of a certain type of interest.

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