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
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
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| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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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。