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

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. These tests verify the correct operation of YOLOv5 tools like training, validation, inference, export, and benchmarks.

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[bot]]

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