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YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)

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@ -38,9 +38,9 @@ The output of a pose estimation model is a set of points that represent the keyp
!!! tip
YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt`. These models are trained on the [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks.
YOLO11 _pose_ models use the `-pose` suffix, i.e. `yolo11n-pose.pt`. These models are trained on the [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) dataset and are suitable for a variety of pose estimation tasks.
In the default YOLOv8 pose model, there are 17 keypoints, each representing a different part of the human body. Here is the mapping of each index to its respective body joint:
In the default YOLO11 pose model, there are 17 keypoints, each representing a different part of the human body. Here is the mapping of each index to its respective body joint:
0: Nose
1: Left Eye
@ -60,14 +60,14 @@ The output of a pose estimation model is a set of points that represent the keyp
15: Left Ankle
16: Right Ankle
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11)
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.
YOLO11 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.
| 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>T4 TensorRT10<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|------------------------------------------------------------------------------------------------|-----------------------|-----------------------|--------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| ---------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.6 |
| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.2 |
| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.7 |
@ -79,7 +79,7 @@ YOLOv8 pretrained Pose models are shown here. Detect, Segment and Pose models ar
## Train
Train a YOLOv8-pose model on the COCO128-pose dataset.
Train a YOLO11-pose model on the COCO128-pose dataset.
!!! example
@ -89,9 +89,9 @@ Train a YOLOv8-pose model on the COCO128-pose dataset.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.yaml") # build a new model from YAML
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolov8n-pose.yaml").load("yolov8n-pose.pt") # build from YAML and transfer weights
model = YOLO("yolo11n-pose.yaml") # build a new model from YAML
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.yaml").load("yolo11n-pose.pt") # build from YAML and transfer weights
# Train the model
results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
@ -101,13 +101,13 @@ Train a YOLOv8-pose model on the COCO128-pose dataset.
```bash
# Build a new model from YAML and start training from scratch
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml epochs=100 imgsz=640
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo pose train data=coco8-pose.yaml model=yolov8n-pose.yaml pretrained=yolov8n-pose.pt epochs=100 imgsz=640
yolo pose train data=coco8-pose.yaml model=yolo11n-pose.yaml pretrained=yolo11n-pose.pt epochs=100 imgsz=640
```
### Dataset format
@ -116,7 +116,7 @@ YOLO pose dataset format can be found in detail in the [Dataset Guide](../datase
## Val
Validate trained YOLOv8n-pose model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO128-pose dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
Validate trained YOLO11n-pose model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO128-pose dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
@ -126,7 +126,7 @@ Validate trained YOLOv8n-pose model [accuracy](https://www.ultralytics.com/gloss
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load an official model
model = YOLO("yolo11n-pose.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
@ -140,13 +140,13 @@ Validate trained YOLOv8n-pose model [accuracy](https://www.ultralytics.com/gloss
=== "CLI"
```bash
yolo pose val model=yolov8n-pose.pt # val official model
yolo pose val model=yolo11n-pose.pt # val official model
yolo pose val model=path/to/best.pt # val custom model
```
## Predict
Use a trained YOLOv8n-pose model to run predictions on images.
Use a trained YOLO11n-pose model to run predictions on images.
!!! example
@ -156,7 +156,7 @@ Use a trained YOLOv8n-pose model to run predictions on images.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load an official model
model = YOLO("yolo11n-pose.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
@ -166,7 +166,7 @@ Use a trained YOLOv8n-pose model to run predictions on images.
=== "CLI"
```bash
yolo pose predict model=yolov8n-pose.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo pose predict model=yolo11n-pose.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo pose predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
@ -174,7 +174,7 @@ See full `predict` mode details in the [Predict](../modes/predict.md) page.
## Export
Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
Export a YOLO11n Pose model to a different format like ONNX, CoreML, etc.
!!! example
@ -184,7 +184,7 @@ Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load an official model
model = YOLO("yolo11n-pose.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
@ -194,11 +194,11 @@ Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
=== "CLI"
```bash
yolo export model=yolov8n-pose.pt format=onnx # export official model
yolo export model=yolo11n-pose.pt format=onnx # export official model
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 export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. 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 YOLO11-pose export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n-pose.onnx`. Usage examples are shown for your model after export completes.
{% include "macros/export-table.md" %}
@ -206,20 +206,20 @@ See full `export` details in the [Export](../modes/export.md) page.
## FAQ
### What is Pose Estimation with Ultralytics YOLOv8 and how does it work?
### What is Pose Estimation with Ultralytics YOLO11 and how does it work?
Pose estimation with Ultralytics YOLOv8 involves identifying specific points, known as keypoints, in an image. These keypoints typically represent joints or other important features of the object. The output includes the `[x, y]` coordinates and confidence scores for each point. YOLOv8-pose models are specifically designed for this task and use the `-pose` suffix, such as `yolov8n-pose.pt`. These models are pre-trained on datasets like [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) and can be used for various pose estimation tasks. For more information, visit the [Pose Estimation Page](#pose-estimation).
Pose estimation with Ultralytics YOLO11 involves identifying specific points, known as keypoints, in an image. These keypoints typically represent joints or other important features of the object. The output includes the `[x, y]` coordinates and confidence scores for each point. YOLO11-pose models are specifically designed for this task and use the `-pose` suffix, such as `yolo11n-pose.pt`. These models are pre-trained on datasets like [COCO keypoints](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco-pose.yaml) and can be used for various pose estimation tasks. For more information, visit the [Pose Estimation Page](#pose-estimation).
### How can I train a YOLOv8-pose model on a custom dataset?
### How can I train a YOLO11-pose model on a custom dataset?
Training a YOLOv8-pose model on a custom dataset involves loading a model, either a new model defined by a YAML file or a pre-trained model. You can then start the training process using your specified dataset and parameters.
Training a YOLO11-pose model on a custom dataset involves loading a model, either a new model defined by a YAML file or a pre-trained model. You can then start the training process using your specified dataset and parameters.
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.yaml") # build a new model from YAML
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-pose.yaml") # build a new model from YAML
model = YOLO("yolo11n-pose.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
@ -227,9 +227,9 @@ results = model.train(data="your-dataset.yaml", epochs=100, imgsz=640)
For comprehensive details on training, refer to the [Train Section](#train).
### How do I validate a trained YOLOv8-pose model?
### How do I validate a trained YOLO11-pose model?
Validation of a YOLOv8-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example:
Validation of a YOLO11-pose model involves assessing its accuracy using the same dataset parameters retained during training. Here's an example:
```python
from ultralytics import YOLO
@ -244,9 +244,9 @@ metrics = model.val() # no arguments needed, dataset and settings remembered
For more information, visit the [Val Section](#val).
### Can I export a YOLOv8-pose model to other formats, and how?
### Can I export a YOLO11-pose model to other formats, and how?
Yes, you can export a YOLOv8-pose model to various formats like ONNX, CoreML, TensorRT, and more. This can be done using either Python or the Command Line Interface (CLI).
Yes, you can export a YOLO11-pose model to various formats like ONNX, CoreML, TensorRT, and more. This can be done using either Python or the Command Line Interface (CLI).
```python
from ultralytics import YOLO
@ -261,6 +261,6 @@ model.export(format="onnx")
Refer to the [Export Section](#export) for more details.
### What are the available Ultralytics YOLOv8-pose models and their performance metrics?
### What are the available Ultralytics YOLO11-pose models and their performance metrics?
Ultralytics YOLOv8 offers various pretrained pose models such as YOLOv8n-pose, YOLOv8s-pose, YOLOv8m-pose, among others. These models differ in size, accuracy (mAP), and speed. For instance, the YOLOv8n-pose model achieves a mAP<sup>pose</sup>50-95 of 50.4 and an mAP<sup>pose</sup>50 of 80.1. For a complete list and performance details, visit the [Models Section](#models).
Ultralytics YOLO11 offers various pretrained pose models such as YOLO11n-pose, YOLO11s-pose, YOLO11m-pose, among others. These models differ in size, accuracy (mAP), and speed. For instance, the YOLO11n-pose model achieves a mAP<sup>pose</sup>50-95 of 50.4 and an mAP<sup>pose</sup>50 of 80.1. For a complete list and performance details, visit the [Models Section](#models).