RF DETR (Segmentation) RF DETR is a real time detection transformer family introduced in RF DETR: Neural Architecture Search for Real Time Detection Transformers by Robinson et al. and integrated in 🤗 Transformers via PR 36895. Model description RF DETR is an end to end instance segmentation model that combines ideas from LW DETR and Deformable DETR: a DINOv2 with registers style ViT backbone (with an RF DETR windowing pattern for efficient attention), a multi scale projector between encoder and decoder, and a multi scale deformable DETR decoder extended with an instance segmentation head. Key Architectural Details: Backbone: DINOv2 with registers style ViT with RF DETR windowed / full attention alternation. Multi scale fusion: RF DETR multi scale projector (C2f style blocks in the LW DETR lineage) to aggregate multi level backbone features before the decoder. Decoder: Deformable DETR style decoder with multi scale deformable cross attention; segmentation checkpoints add mask prediction on top of box/class outputs. Queries: DETR style object queries with bipartite matching and auxiliary decoder losses. Training Details: Segmentation losses: mask prediction losses (e.g. focal / dic…
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