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ultralytics 8.0.202 sort Triton model outputs (#5945)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Mike Tune <mtuneoff@gmail.com>
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Glenn Jocher 2023-10-26 20:32:51 +02:00 • committed by GitHub
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docs/modes/index.md
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@ -12,6 +12,17 @@ keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Val
Ultralytics YOLOv8 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of machine learning models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
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<iframe width="720" height="405" src="https://www.youtube.com/embed/j8uQc0qB91s?si=dhnGKgqvs7nPgeaM"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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<strong>Watch:</strong> Ultralytics Modes Tutorial: Train, Validate, Predict, Export & Benchmark.
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### Modes at a Glance
Understanding the different **modes** that Ultralytics YOLOv8 supports is critical to getting the most out of your models: