MODEL PROFILE

rf-detr-base

Roboflow · rf-detr-base

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. RF-DETR is an end-to-end object detection 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 for fast convergence and strong accuracy–latency tradeoffs. Key Architectural Details: - **Backbone:** DINOv2-with-registers style ViT with RF-DETR **windowed / full** attention alternation (instead of a purely convolutional encoder). - **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; depth and input resolution vary by checkpoint (NAS frontier). - **Queries:** DETR-style object queries with bipartite matching and auxiliary decoder losses for training stability.

Object detectionlicense:apache-2.0
Context windowNot disclosed
Input → Output图像 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:17:02
OVERVIEW

Model overview

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. RF-DETR is an end-to-end object detection 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 for fast convergence and strong accuracy–latency tradeoffs. Key Architectural Details: - **Backbone:** DINOv2-with-registers style ViT with RF-DETR **windowed / full** attention alternation (instead of a purely convolutional encoder). - **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; depth and input resolution vary by checkpoint (NAS frontier). - **Queries:** DETR-style object queries with bipartite matching and auxiliary decoder losses for training stability.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。