RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO and designed for fine-tuning.

C++ object detection inference from video or image input source

NVIDIA DeepStream SDK 8.0 / 7.1 / 7.0 / 6.4 / 6.3 / 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 application for YOLO-Segmentation models

Object tracking pipelines complete with RF-DETR, YOLOv9, YOLO-NAS, YOLOv8, and YOLOv7 detection and BYTETracker tracking

RF-DETR C++ tensorrt : Real-Time End-to-End Object Detection

ONNX model with inference, conversion and visualization scripts for RF-DETR (object detection and instance segmentation)

C++ application to perform computer vision tasks using Nvidia Triton Server for model inference

RF-DETR Object Detection with DeepSORT Tracking

RF-DETR + USLS: object detection using Rust

Run RF-DETR on NVIDIA DeepStream

One‑line fine‑tuning of RF‑DETR on selected classes from OpenImages V7

RF-DETR wrapper for ROS 2 C++ implementation.

FashionVeil: fashion apparel dataset with occlusion level annotations for clothes and accessories

RF-DETR C++ inference engine for object detection and instance segmentation with ONNX Runtime and TensorRT support

Fine-tune the SOTA RF-DETR model in AWS SageMaker or locally

RF-DETR target model for use with Autodistill.

Since have been noticed that Roboflow's RFDETRBase incorporates the drone class as an additional class, this drone detection program is presented.

YOLOv3-YOLO12 unified pipeline for edge deployment - Detection, segmentation, pose estimation with PyTorch to ONNX/TFLite/CoreML export

RF-DETR for Docment Layout Analysis

code for RF-DETR demo on HuggingFace Space