![]() The COCO dataset contains images from over 80 "object" and 91 generic "stuff" categories, which means the dataset can be used for benchmarking general-purpose models more effectively than small-scale datasets. The COCO dataset is designed to represent a vast array of things that we regularly encounter in everyday life, from vehicles like bikes to animals like dogs to people. COCO is a collaborative project maintained by computer vision professionals from numerous prestigious institutions, including Google, Caltech, and Georgia Tech. COCO contains over 330,000 images, of which more than 200,000 are labelled, across dozens of categories of objects. ![]() The Microsoft Common Objects in Context (COCO) dataset is the gold standard benchmark for evaluating the performance of state of the art computer vision models. Let's start by talking about what the COCO dataset is. ![]() In this post, we will dive into the COCO dataset, explaining the motivation for the dataset and exploring dataset facts and metrics. It even has applications for general practitioners in the field, too. With this approach, the efficacy of various models can be compared, in general, to show how one model is more or less performant than another.Ĭommon Objects in Context (COCO) is one such example of a benchmarking dataset, used widely throughout the computer vision research community. Benchmarking happens using standard datasets which can be used across models. The computer vision research community benchmarks new models and enhancements to existing models to test model performance.
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