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Glenn Jocher
2023-07-17 12:40:04 +02:00
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docs/modes/benchmark.md
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comments: true
description: Benchmark mode compares speed and accuracy of various YOLOv8 export formats like ONNX or OpenVINO. Optimize formats for speed or accuracy.
keywords: YOLOv8, Benchmark Mode, Export Formats, ONNX, OpenVINO, TensorRT, Ultralytics Docs
description: Learn how to profile speed and accuracy of YOLOv8 across various export formats; get insights on mAP50-95, accuracy_top5 metrics, and more.
keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling, mAP50-95, accuracy_top5, ONNX, OpenVINO, TensorRT, YOLO export formats
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

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docs/modes/export.md
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comments: true
description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, PaddlePaddle, TorchScript, ONNX format, OpenVINO format, TensorRT format, CoreML format, TF SavedModel format, PaddlePaddle format, Tencent ncnn format
description: Step-by-step guide on exporting your YOLOv8 models to various format like ONNX, TensorRT, CoreML and more for deployment. Explore now!.
keywords: YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, TensorFlow SavedModel, OpenVINO, PyTorch, export model
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

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docs/modes/index.md
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comments: true
description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, Benchmark) to train, validate, predict, track, export or benchmark.
keywords: yolov8, yolo, ultralytics, training, validation, prediction, export, tracking, benchmarking, real-time object detection, object tracking
description: From training to tracking, make the most of YOLOv8 with Ultralytics. Get insights and examples for each supported mode including validation, export, and benchmarking.
keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Validation, Prediction, Export, Tracking, Benchmarking
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# Ultralytics YOLOv8 Modes

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docs/modes/predict.md
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description: Get started with YOLOv8 Predict mode and input sources. Accepts various input sources such as images, videos, and directories.
keywords: YOLOv8, predict mode, generator, streaming mode, input sources, video formats, arguments customization
description: Discover how to use YOLOv8 predict mode for various tasks. Learn about different inference sources like images, videos, and data formats.
keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks, streaming mode, image processing, video processing, machine learning, AI
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

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docs/modes/track.md
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comments: true
description: Explore YOLOv8n-based object tracking with Ultralytics' BoT-SORT and ByteTrack. Learn configuration, usage, and customization tips.
keywords: object tracking, YOLO, trackers, BoT-SORT, ByteTrack
description: Learn how to use Ultralytics YOLO for object tracking in video streams. Guides to use different trackers and customise tracker configurations.
keywords: Ultralytics, YOLO, object tracking, video streams, BoT-SORT, ByteTrack, Python guide, CLI guide
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png">

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docs/modes/train.md
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description: Learn how to train custom YOLOv8 models on various datasets, configure hyperparameters, and use Ultralytics' YOLO for seamless training.
keywords: YOLOv8, train mode, train a custom YOLOv8 model, hyperparameters, train a model, Comet, ClearML, TensorBoard, logging, loggers
description: Step-by-step guide to train YOLOv8 models with Ultralytics YOLO with examples of single-GPU and multi-GPU training. Efficient way for object detection training.
keywords: Ultralytics, YOLOv8, YOLO, object detection, train mode, custom dataset, GPU training, multi-GPU, hyperparameters, CLI examples, Python examples
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

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docs/modes/val.md
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description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode.
keywords: Ultralytics, YOLO, YOLOv8, Val, Validation, Hyperparameters, Performance, Accuracy, Generalization, COCO, Export Formats, PyTorch
description: 'Guide for Validating YOLOv8 Models: Learn how to evaluate the performance of your YOLO models using validation settings and metrics with Python and CLI examples.'
keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperparameters, accuracy, metrics, Python, CLI
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">