Segmentation for RTDERT about ultralytics

[github-actions]

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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[glenn-jocher]

Hello!

Thank you for your interest in the RT-DETR model and for reaching out with your feature request! 😊

Currently, the RT-DETR model in our repository is primarily focused on object detection. However, your suggestion to extend its capabilities to include segmentation is an exciting idea and could certainly enhance its utility for various applications.

While we don't have a timeline for rolling out segmentation for RT-DETR just yet, we are always looking to improve and expand our models based on community feedback. Your input is invaluable, and we will definitely consider this feature for future updates.

In the meantime, if you're interested in segmentation tasks, you might want to explore our YOLOv8 models, which already support segmentation. Here's a quick example of how you can use YOLOv8 for segmentation:

Python Example

from ultralytics import YOLO

# Load a pretrained YOLOv8 segmentation model
model = YOLO('yolov8n-seg.pt')

# Run inference on an image
results = model('path/to/your/image.jpg')

# Visualize the results
results[0].show()

CLI Example

yolo segment predict model=yolov8n-seg.pt source='path/to/your/image.jpg'

We appreciate your willingness to contribute! If you have any further questions or need assistance, feel free to ask. The YOLO community and the Ultralytics team are here to help.