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description: Explore Ultralytics YOLO metrics tools - from confusion matrix, detection metrics, pose metrics to box IOU. Learn how to compute and plot precision-recall curves. |
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keywords: Ultralytics, YOLO, YOLOv3, YOLOv4, metrics, confusion matrix, detection metrics, pose metrics, box IOU, mask IOU, plot precision-recall curves, compute average precision |
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--- |
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# Reference for `ultralytics/utils/metrics.py` |
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!!! Note |
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This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/metrics.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/metrics.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/metrics.py) 🛠️. Thank you 🙏! |
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<br><br> |
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## ::: ultralytics.utils.metrics.ConfusionMatrix |
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<br><br> |
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## ::: ultralytics.utils.metrics.Metric |
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## ::: ultralytics.utils.metrics.DetMetrics |
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<br><br> |
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## ::: ultralytics.utils.metrics.SegmentMetrics |
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<br><br> |
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## ::: ultralytics.utils.metrics.PoseMetrics |
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<br><br> |
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## ::: ultralytics.utils.metrics.ClassifyMetrics |
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<br><br> |
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## ::: ultralytics.utils.metrics.bbox_ioa |
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<br><br> |
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## ::: ultralytics.utils.metrics.box_iou |
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<br><br> |
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## ::: ultralytics.utils.metrics.bbox_iou |
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## ::: ultralytics.utils.metrics.mask_iou |
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<br><br> |
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## ::: ultralytics.utils.metrics.kpt_iou |
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<br><br> |
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## ::: ultralytics.utils.metrics.smooth_BCE |
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## ::: ultralytics.utils.metrics.smooth |
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## ::: ultralytics.utils.metrics.plot_pr_curve |
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## ::: ultralytics.utils.metrics.plot_mc_curve |
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## ::: ultralytics.utils.metrics.compute_ap |
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## ::: ultralytics.utils.metrics.ap_per_class |
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<br><br>
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