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ultralytics 8.0.177 add https://youtube.com/ultralytics videos to Docs (#4875)

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26
docs/tasks/classify.md
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@ -4,14 +4,13 @@ description: Learn about YOLOv8 Classify models for image classification. Get de
keywords: Ultralytics, YOLOv8, Image Classification, Pretrained Models, YOLOv8n-cls, Training, Validation, Prediction, Model Export
---
Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of
predefined classes.
# Image Classification
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418606-adf35c62-2e11-405d-84c6-b84e7d013804.png">
The output of an image classifier is a single class label and a confidence score. Image
classification is useful when you need to know only what class an image belongs to and don't need to know where objects
of that class are located or what their exact shape is.
Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes.
The output of an image classifier is a single class label and a confidence score. Image classification is useful when you need to know only what class an image belongs to and don't need to know where objects of that class are located or what their exact shape is.
!!! tip "Tip"
@ -19,13 +18,9 @@ of that class are located or what their exact shape is.
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on
the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify
models are pretrained on
the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
|----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------|
@ -43,8 +38,7 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u
## Train
Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments
see the [Configuration](../usage/cfg.md) page.
Train YOLOv8n-cls on the MNIST160 dataset for 100 epochs at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example ""
@ -81,8 +75,7 @@ YOLO classification dataset format can be found in detail in the [Dataset Guide]
## Val
Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains
it's training `data` and arguments as model attributes.
Validate trained YOLOv8n-cls model accuracy on the MNIST160 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes.
!!! example ""
@ -159,8 +152,7 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes.
Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|-------------------------------|----------|-----------------------------------------------------|

15
docs/tasks/detect.md
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@ -4,12 +4,25 @@ description: Official documentation for YOLOv8 by Ultralytics. Learn how to trai
keywords: YOLOv8, Ultralytics, object detection, pretrained models, training, validation, prediction, export models, COCO, ImageNet, PyTorch, ONNX, CoreML
---
Object detection is a task that involves identifying the location and class of objects in an image or video stream.
# Object Detection
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418624-5785cb93-74c9-4541-9179-d5c6782d491a.png">
Object detection is a task that involves identifying the location and class of objects in an image or video stream.
The output of an object detector is a set of bounding boxes that 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.
<p align="center">
<br>
<iframe width="720" height="405" src="https://www.youtube.com/embed/5ku7npMrW40?si=6HQO1dDXunV8gekh"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Object Detection with Pre-trained Ultralytics YOLOv8 Model.
</p>
!!! tip "Tip"
YOLOv8 Detect models are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).

25
docs/tasks/index.md
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@ -6,46 +6,35 @@ keywords: Ultralytics, YOLOv8, Detection, Segmentation, Classification, Pose Est
# Ultralytics YOLOv8 Tasks
YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to
perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md),
and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
<br>
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png">
YOLOv8 is an AI framework that supports multiple computer vision **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
## [Detection](detect.md)
Detection is the primary task supported by YOLOv8. It involves detecting objects in an image or video frame and drawing
bounding boxes around them. The detected objects are classified into different categories based on their features.
YOLOv8 can detect multiple objects in a single image or video frame with high accuracy and speed.
Detection is the primary task supported by YOLOv8. It involves detecting objects in an image or video frame and drawing bounding boxes around them. The detected objects are classified into different categories based on their features. YOLOv8 can detect multiple objects in a single image or video frame with high accuracy and speed.
[Detection Examples](detect.md){ .md-button .md-button--primary}
## [Segmentation](segment.md)
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each
region is assigned a label based on its content. This task is useful in applications such as image segmentation and
medical imaging. YOLOv8 uses a variant of the U-Net architecture to perform segmentation.
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each region is assigned a label based on its content. This task is useful in applications such as image segmentation and medical imaging. YOLOv8 uses a variant of the U-Net architecture to perform segmentation.
[Segmentation Examples](segment.md){ .md-button .md-button--primary}
## [Classification](classify.md)
Classification is a task that involves classifying an image into different categories. YOLOv8 can be used to classify
images based on their content. It uses a variant of the EfficientNet architecture to perform classification.
Classification is a task that involves classifying an image into different categories. YOLOv8 can be used to classify images based on their content. It uses a variant of the EfficientNet architecture to perform classification.
[Classification Examples](classify.md){ .md-button .md-button--primary}
## [Pose](pose.md)
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are
referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or
video frame with high accuracy and speed.
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are referred to as keypoints and are used to track movement or pose estimation. YOLOv8 can detect keypoints in an image or video frame with high accuracy and speed.
[Pose Examples](pose.md){ .md-button .md-button--primary}
## Conclusion
YOLOv8 supports multiple tasks, including detection, segmentation, classification, and keypoints detection. Each of
these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose
the appropriate task for your computer vision application.
YOLOv8 supports multiple tasks, including detection, segmentation, classification, and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.

37
docs/tasks/pose.md
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@ -4,16 +4,25 @@ description: Learn how to use Ultralytics YOLOv8 for pose estimation tasks. Find
keywords: Ultralytics, YOLO, YOLOv8, pose estimation, keypoints detection, object detection, pre-trained models, machine learning, artificial intelligence
---
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.
# Pose Estimation
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418616-9811ac0b-a4a7-452a-8aba-484ba32bb4a8.png">
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.
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.
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.
<p align="center">
<br>
<iframe width="720" height="405" src="https://www.youtube.com/embed/Y28xXQmju64?si=pCY4ZwejZFu6Z4kZ"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Pose Estimation with Ultralytics YOLOv8.
</p>
!!! tip "Tip"
@ -21,13 +30,9 @@ parts of an object in a scene, and their location in relation to each other.
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on
the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify
models are pretrained on
the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|------------------------------------------------------------------------------------------------------|-----------------------|-----------------------|--------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
@ -84,8 +89,7 @@ YOLO pose dataset format can be found in detail in the [Dataset Guide](../datase
## Val
Validate trained YOLOv8n-pose model accuracy on the COCO128-pose dataset. No argument need to passed as the `model`
retains it's
training `data` and arguments as model attributes.
retains it's training `data` and arguments as model attributes.
!!! example ""
@ -164,8 +168,7 @@ Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your model after export completes.
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|--------------------------------|----------|-----------------------------------------------------|

34
docs/tasks/segment.md
View file

@ -4,14 +4,24 @@ description: Learn how to use instance segmentation models with Ultralytics YOLO
keywords: yolov8, instance segmentation, Ultralytics, COCO dataset, image segmentation, object detection, model training, model validation, image prediction, model export
---
Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
and segmenting them from the rest of the image.
# Instance Segmentation
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418644-7df320b8-098d-47f1-85c5-26604d761286.png">
The output of an instance segmentation model is a set of masks or
contours that outline each object in the image, along with class labels and confidence scores for each object. Instance
segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.
Instance segmentation goes a step further than object detection and involves identifying individual objects in an image and segmenting them from the rest of the image.
The output of an instance segmentation model is a set of masks or contours that outline each object in the image, along with class labels and confidence scores for each object. Instance segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.
<p align="center">
<br>
<iframe width="720" height="405" src="https://www.youtube.com/embed/o4Zd-IeMlSY?si=37nusCzDTd74Obsp"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Run Segmentation with Pre-Trained Ultralytics YOLOv8 Model in Python.
</p>
!!! tip "Tip"
@ -19,13 +29,9 @@ segmentation is useful when you need to know not only where objects are in an im
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on
the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify
models are pretrained on
the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
@ -43,8 +49,7 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u
## Train
Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available
arguments see the [Configuration](../usage/cfg.md) page.
Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example ""
@ -164,8 +169,7 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-seg export formats are in the table below. You can predict or validate directly on exported models,
i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your model after export completes.
Available YOLOv8-seg export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|-------------------------------|----------|-----------------------------------------------------|