# RF-DETR — Real-Time Object Detection Architecture by Roboflow

> A state-of-the-art real-time object detection model designed for fine-tuning, achieving top results on COCO with a transformer-based architecture optimized for speed.

## Install

Save as a script file and run:

# RF-DETR — Real-Time Object Detection Architecture by Roboflow

## Quick Use
```bash
pip install rf-detr
from rfdetr import RFDETRBase
model = RFDETRBase()
detections = model.predict("image.jpg")
```

## Introduction
RF-DETR is a real-time object detection model architecture developed by Roboflow that achieves state-of-the-art accuracy on the COCO benchmark while maintaining real-time inference speed. It is specifically designed for easy fine-tuning on custom datasets.

## What RF-DETR Does
- Provides real-time object detection with transformer-based accuracy
- Achieves top performance on COCO without sacrificing inference speed
- Supports fine-tuning on custom datasets with minimal code
- Includes instance segmentation capabilities
- Offers multiple model sizes for different speed-accuracy trade-offs

## Architecture Overview
RF-DETR uses a hybrid architecture combining an efficient CNN backbone for feature extraction with a deformable transformer decoder for object queries. This design avoids the need for NMS post-processing, producing direct set predictions. The architecture is optimized for GPU inference with attention-efficient operators.

## Self-Hosting & Configuration
- Install via pip with CUDA support for GPU acceleration
- Pretrained weights download automatically on first use
- Fine-tune with a single function call pointing to your labeled dataset
- Export to ONNX or TensorRT for production deployment
- Supports batch inference for throughput-critical pipelines

## Key Features
- Real-time inference at 30+ FPS on modern GPUs
- NMS-free architecture for simpler deployment pipelines
- Built-in support for Roboflow dataset format and augmentation
- Multiple model scales from Base to Large for flexibility
- Published at ICLR with peer-reviewed methodology

## Comparison with Similar Tools
- **YOLOv8/v9** — CNN-only architecture vs transformer decoder with better accuracy
- **DINO-DETR** — slower convergence vs RF-DETR's fast training schedule
- **RT-DETR** — similar concept but RF-DETR achieves higher mAP on COCO
- **Detectron2** — research framework vs production-focused single-model package
- **Grounding DINO** — open-vocabulary vs closed-set with higher speed

## FAQ
**Q: What hardware do I need for inference?**
A: An NVIDIA GPU with 4GB+ VRAM for real-time; CPU inference works but is slower.

**Q: Can I fine-tune on my own dataset?**
A: Yes, RF-DETR is specifically designed for fine-tuning with as few as 100 labeled images.

**Q: What annotation formats are supported?**
A: COCO JSON, Pascal VOC, and Roboflow format are all supported natively.

**Q: Is there a license restriction for commercial use?**
A: RF-DETR is released under the Apache 2.0 license, permitting commercial use.

## Sources
- https://github.com/roboflow/rf-detr
- https://blog.roboflow.com/rf-detr/

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Source: https://tokrepo.com/en/workflows/asset-b2458724
Author: Script Depot