Docs partial mdformat improvements (#7378)
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>
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Glenn Jocher
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52 changed files with 231 additions and 261 deletions
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@ -41,11 +41,8 @@ YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose model
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| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 76.8 | 93.5 | 163.0 | 0.87 | 37.5 | 99.7 |
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| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 79.0 | 94.6 | 232.0 | 1.01 | 57.4 | 154.8 |
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- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
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<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
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- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
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## Train
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@ -41,11 +41,8 @@ YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val detect data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
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## Train
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@ -12,10 +12,8 @@ Oriented object detection goes a step further than object detection and introduc
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The output of an oriented object detector is a set of rotated bounding boxes that exactly enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.
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<!-- youtube video link for obb task -->
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!!! Tip "Tip"
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YOLOv8 Obb models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
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@ -36,15 +34,13 @@ YOLOv8 pretrained Obb models are shown here, which are pretrained on the [DOTAv1
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<!-- TODO: should we report multi-scale results only as they're better or both multi-scale and single-scale. -->
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- **mAP<sup>val</sup>** values are for single-model single-scale on [DOTAv1 test](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0`
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- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
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- **mAP<sup>val</sup>** values are for single-model single-scale on [DOTAv1 test](http://cocodataset.org) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0`
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- **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
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## Train
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<!-- TODO: probably we should create a sample dataset like coco128.yaml, named dota128.yaml? -->
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Train YOLOv8n-obb on the dota128.yaml dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
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!!! Example
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@ -8,8 +8,7 @@ keywords: Ultralytics, YOLO, YOLOv8, pose estimation, keypoints detection, objec
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418616-9811ac0b-a4a7-452a-8aba-484ba32bb4a8.png" alt="Pose estimation examples">
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Pose estimation is a task that involves identifying the location of specific points in an image, usually referred to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]`
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coordinates.
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Pose estimation is a task that involves identifying the location of specific points in an image, usually referred to as keypoints. The keypoints can represent various parts of the object such as joints, landmarks, or other distinctive features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]` coordinates.
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The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific parts of an object in a scene, and their location in relation to each other.
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@ -43,12 +42,8 @@ YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models ar
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| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
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| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
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dataset.
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<br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`
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## Train
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@ -41,11 +41,8 @@ YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models
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| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
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| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val segment data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
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## Train
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