Project AURIONAI Use-Cases Research & Innovation
vision

RF-DETR S

RF-DETR size variant S.

Roboflow
Levelvariant
Classvision
Parameters32.1M
Contextnot in the notes
ReleasedNov 2025
Opennessunknown
Runs onsingle datacentre GPU, embedded GPU
Statusactive

Benchmark Results

vendor-reported
valvendor-reported
52.9
03570

Score in AP. Higher is better.

Nov 2025 arxiv.org
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).

R&D Topics

Identity

Developer
Roboflow
Family
RF-DETR
Released
Nov 2025 sourcesannouncement page
Weights published
–
Record checked
10 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
dense transformer
Total parameters
32.1M sources
Active parameters
–
Checkpoint parameters
–
Context length
not in the notes
Input
image
Output
image, text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
unknown
Licence
Apache License 2.0 sources
Access routes
open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
single datacentre GPU, embedded GPU
Footprint
not in the notes
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. RF-DETR: Neural Architecture Search for Real-Time Detection Transformers arxiv.org https://arxiv.org/html/2511.09554v2
  2. github.com/roboflow/rf-detr https://github.com/roboflow/rf-detr

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "RF-DETR S", https://aurion.semai.id/models/roboflow-rf-detr-s/, records of October 2026.