YOLO11 detection interpret and process output preds

[poppyzy]

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Question

Does anyone know how to interpret yolo11 detection results like (84, 2100), thanks

Additional

No response

[UltralyticsAssistant]

👋 Hello @poppyzy, thank you for your interest in YOLOv5 🚀! While YOLO11 is not an officially supported model in the YOLOv5 repository, we’re happy to assist with your question as it relates to YOLO concepts.

Please visit our ⭐️ Tutorials for an overview of our supported models and workflows. For deeper insights into prediction outputs, examining our guides on inference and post-processing might prove helpful.

If this question is related to a custom or non-standard YOLO implementation, please provide as much information as possible, including:

• Details on your model: Are you using any modifications or an entirely different fork of YOLO?
• Exact prediction outputs: Please clarify the format (e.g., is it bounding boxes or class IDs?).
• Code snippets or specific methods: Include any relevant code related to your inference process.

Requirements

Ensure you are running on an up-to-date environment:
Python>=3.8.0 with all requirements.txt dependencies installed, including PyTorch>=1.8. For YOLOv5, you can quickly set up as follows:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 and related workflows can be run within these verified environments (with pre-installed dependencies such as CUDA, Python, and PyTorch):

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

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This is an automated response. An Ultralytics engineer will review and assist you further soon! 😃

[pderrenger]

@poppyzy while the YOLOv5 repo doesn't have YOLO11 in it, resources for understanding the output format of newer Ultralytics YOLO models, like YOLOv11 can be found here: How to Use Ultralytics YOLO11 for Object Detection and A guide to deep dive into object detection in 2025. These guides explain object detection and how models like YOLO11 work, which might help you interpret the detection results.

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

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