Roboflow/rf-detr-large
object-detection model by Roboflow
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Model card & documentation
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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: Training Details: You can use…
Read the full model card ↗ · Preview checked 2026-10-28T05:26:57.213Z
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Model overview
Roboflow/rf-detr-large is a object-detection model repository published by Roboflow on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- object-detection
- Library
- transformers
- Recent downloads (30 days)
- 1,576
- Cumulative likes
- 18
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 33,933,218
- Architecture
- RfDetrForObjectDetection
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-05-11T09:01:48.000Z
- Last modified
- 2026-05-20T16:47:07.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:34.007Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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