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Rf Detr

Rf Detr is a Python-based real-time transformer architecture for object detection, instance segmentation and preview keypoint detection, built on a DINOv2 vision-transformer backbone and designed for fine-tuning.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rf Detr — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a project needs a real-time, fine-tunable computer-vision architecture covering detection and segmentation, with preview keypoint support in the same API.

Who it is for

  • Computer-vision engineers building detection or segmentation pipelines
  • Teams adapting a vision model to domain-specific labeled data
  • Developers who need a self-hosted or cloud-capable Python model architecture

Documented uses

  • Detect objects in images, camera feeds or video pipelines
  • Separate individual objects with instance segmentation
  • Explore preview keypoint detection for pose or landmark analysis
  • Fine-tune a model for a specialized visual domain
Before you choose

Requirements & limits

The repository identifies the open-source package and Apache-designated models as Apache 2.0 licensed. RF-DETR-XL and 2XL are identified under PML 1.0.

  • Python 3.10 or newer is required for the documented package installation.
  • Model sizes differ in reported accuracy, latency, resolution and parameter count.
  • Repository benchmark latency was measured on an NVIDIA T4 with TensorRT, FP16 and batch size one.
  • Installing from the develop branch accesses unreleased changes that may be less stable.
  • Apache 2.0 and PML 1.0 apply to different listed model groups.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the RF-DETR Python package

Prepare an environment for using the published RF-DETR package.

Before you start

  • Python 3.10 or newer
  • pip
  • A Python environment

Steps

  1. Create or select a Python 3.10-or-newer environment.

  2. Install the published package with pip using the repository’s documented package installation command.

Expected outcome

The RF-DETR package is installed in the Python environment for subsequent use.

Guide limitations

  • This procedure documents installation only; it does not establish that a model has been run or that a deployment is compatible.
  • Installing from the development branch provides unreleased changes that the repository says may be less stable than the latest published release.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
2 October 2026
Original website title
roboflow/rf-detr · Official homepage title · retained in its source language
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