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ResNeXt is a deep neural network architecture for image classification built on the idea of aggregated residual transformations. Instead of simply increasing depth or width, ResNeXt introduces a new dimension called cardinality, which refers to the number of parallel transformation paths (i.e. the number of “branches”) that are aggregated together. Each branch is a small transformation (e.g. bottleneck block) and their outputs are summed—this enables richer representation without excessive...
...It uses a very basic subset of Lua, which allows it to be used also in very restrictive embedding scenarios (e.g. videogames).
The focus on data instead of processing makes it suitable for all those scripts where querying the hosting application entities is paramount: RPG videogames, Adobe Photoshop Lightroom.