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68 lines
3.5 KiB
68 lines
3.5 KiB
--- |
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comments: true |
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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. |
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keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Validation, Prediction, Export, Tracking, Benchmarking |
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--- |
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# Ultralytics YOLOv8 Modes |
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"> |
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Ultralytics YOLOv8 supports several **modes** that can be used to perform different tasks. These modes are: |
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- **Train**: For training a YOLOv8 model on a custom dataset. |
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- **Val**: For validating a YOLOv8 model after it has been trained. |
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- **Predict**: For making predictions using a trained YOLOv8 model on new images or videos. |
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- **Export**: For exporting a YOLOv8 model to a format that can be used for deployment. |
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- **Track**: For tracking objects in real-time using a YOLOv8 model. |
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- **Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy. |
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## [Train](train.md) |
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Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the |
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specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can |
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accurately predict the classes and locations of objects in an image. |
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[Train Examples](train.md){ .md-button .md-button--primary} |
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## [Val](val.md) |
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Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a |
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validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters |
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of the model to improve its performance. |
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[Val Examples](val.md){ .md-button .md-button--primary} |
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## [Predict](predict.md) |
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Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the |
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model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model |
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predicts the classes and locations of objects in the input images or videos. |
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[Predict Examples](predict.md){ .md-button .md-button--primary} |
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## [Export](export.md) |
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Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is |
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converted to a format that can be used by other software applications or hardware devices. This mode is useful when |
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deploying the model to production environments. |
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[Export Examples](export.md){ .md-button .md-button--primary} |
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## [Track](track.md) |
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Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a |
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checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful |
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for applications such as surveillance systems or self-driving cars. |
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[Track Examples](track.md){ .md-button .md-button--primary} |
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## [Benchmark](benchmark.md) |
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Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide |
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information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose) |
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or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export |
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formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for |
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their specific use case based on their requirements for speed and accuracy. |
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[Benchmark Examples](benchmark.md){ .md-button .md-button--primary} |