方法证据记录
DETR (Detection Transformer)
DETR (Detection Transformer) is an end-to-end framework for object detection introduced by Carion et al. in 2020 that reformulates detection as a direct set prediction problem using transformers. Unlike traditional approaches that use hand-crafted post-processing like non-maximum suppression, DETR treats object detection as a sequence-to-sequence problem where the transformer predicts all objects at once.
源记录
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End-to-End Object Detection with Transformers
分类方法记录 · ml-model / deep-learning
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