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In this blog post, architecture of a few previous state-of-the-art models on image semantic segmentation challenges are detailed. Note that researchers test their algorithms using different datasets (PASCAL VOC, PASCAL Context, COCO, Cityscapes) which are different between the years and use different metrics of evaluation. Thus the cited performances cannot be directly compared per se.
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This blog post describes theoretical methods to reduce model size. Size reduction for deep learning models is an active field of research. Those methods are truly performant, but the specific type of machine learning models used involves extremely deep and complex architectures (Simonyan et al. (2014), He et al. (2015), Szegedy et al. (2016)). How can we simply transform a deep model into a lighter one without decreasing drastically its performances ? Moreover, does it exist specialized architectures to build light models while achieving state-of-the-art performances ? Note that researchers test their algorithms using different datasets. Thus the cited accuracies cannot be directly compared per se.
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In this blog post I will review the state-of-the art of object detection models. I will provide details about the evolution of the architectures of the most accurate object detection models from 2012 up to today. One of my analysis criteria will be on their speed at inference allowing real-time analysis. Note that researchers test their algorithms using different datasets (PASCAL VOC, COCO, ImageNet) which are different between the years. Thus the cited accuracies cannot be directly compared per se.
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The purpose of this post is to provide a review of the state-of-the-art of image classification algorithms based on the most popular labelled dataset, ImageNet. We will describe some of the innovative architectures which lead to significant improvements. Note that researchers test their algorithms using different datasets (a new ImageNet dataset is released as a new challenge with different images each year). Thus the cited accuracies cannot be directly compared per se.