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[N] Detectron2: A PyTorch-based modular object detection library

Detectron2: A PyTorch-based modular object detection library

Improvements in Detectron2

PyTorch: The original Detectron was implemented in Caffe2. PyTorch provides a more intuitive imperative programming model that allows researchers and practitioners to iterate more rapidly on model design and experiments. Because we’ve rewritten Detectron2 from scratch in PyTorch, users can now benefit from PyTorch’s approach to deep learning as well as the large and active community that continually improves PyTorch

Modular, extensible design: In Detectron2, we’ve introduced a modular design that allows users to plug custom module implementations into almost any part of an object detection system. This means that many new research projects can be written in hundreds of lines of code with a clean separation between the core Detectron2 library and the novel research implementation. We continue to refine the modular, extensible design by implementing new models and discovering new ways in which we can make Detectron2 more flexible.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.