Highlights: In this post we are going to show how to build a computer vision model and prepare it for deploying on mobile and embedded devices. With this knowledge you can really deploy a script into an everyday use or a mobile app. Tutorial Overview: Introduction Building a model in TensorFlow Converting a model to TensorFlow Lite Post-training quantization 1. Introduction Last time, we showed how we can improve a model performance using transfer learning. But…
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Image classification refers to a process in computer vision that can classify an image according to its visual content. For example, an image classification algorithm may be designed to tell if an image contains an animal or not. While detecting an object is irrelevant for humans, robust image classification is still a challenge in computer vision applications. In this section, you can find state-of-the-art, greatest papers for image classification along with the authors’ names, link to the…
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Image generation is the process of generating new images from an existing dataset. For example, DeepFake which are artificial media in which a person in an existing image or video is replaced with someone else’s likeness. They are different types of generations which are Unconditional generation and Conditional generation. Unconditional generation means the generated image doesn’t depend on the dataset i.e p(y). While Conditional generation means generating images based on the dataset i.e p(y|x)p(y|x). In…
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In computer vision, Image segmentation is the process of subdividing a digital image into multiple segments commonly known as image objects. The main objective is to change the representation of the object found in a given image into something that is much simpler to analyze. This technique is commonly used when locating objects, and boundaries such as lines, curves, etc. in an image. In this section, you can find state-of-the-art, greatest papers for segment segmentation…
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