RF-DETR — Overview
RF-DETR (Roboflow DETR) is a transformer-based architecture designed for real-time object detection and instance segmentation while maintaining strong accuracy on standard benchmarks. Developed and released by Roboflow, RF-DETR focuses on achieving a balance of throughput (low latency) and model quality, and the project includes pretrained checkpoints, training utilities, and inference optimizations.
Key features
- Real-time focus: RF-DETR emphasizes low end-to-end latency and offers models at multiple size points (Nano, Small, Medium, Base) to fit different deployment constraints.
- State-of-the-art benchmarks: According to the project, RF-DETR surpasses 60 AP on the Microsoft COCO detection benchmark for real-time models and shows strong domain-adaptability on the RF100-VL benchmark.
- Instance segmentation preview: RF-DETR-Seg (Preview) provides a segmentation head intended to be faster and more accurate than competing real-time segmentation models in their reported evaluations.
- Inference optimizations: The library exposes an optimize_for_inference method that can speed up native PyTorch inference (reports up to 2x depending on platform), plus guidance for measuring end-to-end latency using Roboflow’s benchmarking tooling.
- Training and tooling: The package supports fine-tuning on custom datasets, gradient checkpointing, early stopping, resume training, and logging integrations (TensorBoard, Weights & Biases). There are example notebooks and Colab tutorials for fine-tuning and deployment.
- Open-source & licensing: Code and COCO-pretrained weights are released under the Apache 2.0 license; the repo includes documentation, examples, and citations (including an arXiv paper describing the work).
Typical usage
RF-DETR can be used locally for research and production. The project provides a pip package (rfdetr) and instructions to run inference (including example code that integrates with Roboflow’s inference APIs and common annotation utilities). It also supports exporting/optimizing models for faster runtime inference and includes recommended checkpoints for various latency/accuracy tradeoffs.
Who is it for
- Researchers exploring real-time detection/segmentation and Transformer-based detection architectures.
- Engineers needing production-ready, low-latency object detection models with available pretrained weights and inference optimizations.
- Practitioners wanting to fine-tune compact, fast detection/segmentation models on custom datasets.
References & provenance
The README links to an arXiv paper (RF-DETR: Neural Architecture Search for Real-Time Detection Transformers) and documents release notes (checkpoints released, segmentation preview, inference optimizations). The project is maintained by Roboflow with contributors named in the repository and README.
