RF-DETR Segmentation Instance Segmentation Model: What is, How to Use

Try the Model

Use the widget below to experiment with RF-DETR Segmentation. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

RF-DETR ("Roboflow detection transformer") is a real-time transformer-based architecture designed to transfer well to both a wide variety of domains and to datasets big and small. RF-DETR Seg (Preview) adds a segmentation head to the RF-DETR model architecture, allowing you to use the model for real-time image segmentation.

RF-DETR Seg (Preview) sets a new standard for instance segmentation, running 3x faster and more accurately than prior models on the Microsoft COCO benchmark. With end-to-end latency of just 5.6ms (170 FPS) on a T4 GPU, RF-DETR Seg is the first DETR-based segmentation model to achieve true real-time performance. For example, RF-DETR Seg (Preview) is 3x faster and more accurate than the largest YOLO11. RF-DETR is Apache 2.0 open source.

RF-DETR Seg expands the RF-DETR family with a segmentation head inspired by MaskDINO, optimized for efficiency with a ViT backbone. The result is precise object masks with state-of-the-art accuracy, redefining what’s possible in segmentation.

You can now train RF-DETR Segmentation models with any Instance Segmentation project in Roboflow. These models can be deployed on offline on your edge devices with Inference and Roboflow Workflows. You can also train models with the open source rfdetr Python package.

Here is example showing RF-DETR Segmentation running on images:

RF-DETR Segmentation License

RF-DETR Segmentation

is licensed under a

Apache 2.0

license.

Performance

Deploy a RF-DETR Segmentation API

You can use Roboflow Inference to deploy a

RF-DETR Segmentation

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.


import os
import supervision as sv
from inference import get_model
from PIL import Image
from io import BytesIO
import requests

url = "https://media.roboflow.com/dog.jpeg"
image = Image.open(BytesIO(requests.get(url).content))

model = get_model("rfdetr-seg-preview")

predictions = model.infer(image, confidence=0.5)[0]

detections = sv.Detections.from_inference(predictions)

labels = [prediction.class_name for prediction in predictions.predictions]

annotated_image = image.copy()
annotated_image = sv.MaskAnnotator(color=sv.ColorPalette.ROBOFLOW).annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator(color=sv.ColorPalette.ROBOFLOW).annotate(annotated_image, detections, labels)

sv.plot_image(annotated_image)

RF-DETR Segmentation

Try the Model

Overview

RF-DETR Segmentation License

Performance

Deploy a RF-DETR Segmentation API

Label Data Automatically with RF-DETR Segmentation