rf-detr-base

模型介绍

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.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

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。

常见问题

资料来源与可追溯记录

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。