run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.309
- name:Benchmark ClassificationModel
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.166
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.249
- name:Benchmark YOLOWorld DetectionModel
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov8s-worldv2.pt' imgsz=160 verbose=0.318
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov8s-worldv2.pt' imgsz=160 verbose=0.337
- name:Benchmark SegmentationModel
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.279
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.195
- name:Benchmark PoseModel
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.183
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.197
- name:Benchmark OBBModel
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-obb.pt' imgsz=160 verbose=0.472
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-obb.pt' imgsz=160 verbose=0.597
- name:Benchmark YOLOv10Model
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov10n.pt' imgsz=160 verbose=0.178
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/yolov10n.pt' imgsz=160 verbose=0.205
if:(github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
[Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
[Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8<ahref="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <ahref="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, questions, or discussions, become a member of the Ultralytics <ahref="https://ultralytics.com/discord">Discord</a>, <ahref="https://reddit.com/r/ultralytics">Reddit</a> and <ahref="https://community.ultralytics.com">Forums</a>!
We hope that the resources here will help you get the most out of YOLO. Please browse the Ultralytics<ahref="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <ahref="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, questions, or discussions, become a member of the Ultralytics <ahref="https://ultralytics.com/discord">Discord</a>, <ahref="https://reddit.com/r/ultralytics">Reddit</a> and <ahref="https://community.ultralytics.com">Forums</a>!
To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
@ -47,7 +47,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
## <divalign="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com/) for full documentation on training, validation, prediction and deployment.
See below for a quickstart install and usage examples, and see our [Docs](https://docs.ultralytics.com/) for full documentation on training, validation, prediction and deployment.
<detailsopen>
<summary>Install</summary>
@ -71,23 +71,23 @@ For alternative installation methods including [Conda](https://anaconda.org/cond
### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` command:
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
### Python
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
YOLO may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model
train_results = model.train(
@ -108,26 +108,13 @@ results[0].show()
path = model.export(format="onnx") # return path to exported model
```
See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.
See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.
</details>
### Notebooks
Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://www.youtube.com/ultralytics?sub_confirmation=1) tutorial, making it easy to learn and implement advanced YOLOv8 features.
| <ahref="https://docs.ultralytics.com/modes/track/">YOLOv8 Multi-Object Tracking in Videos</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/hHyHmOtmEgs"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
| <ahref="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 Object Counting in Videos</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/Ag2e-5_NpS0"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
| <ahref="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 Heatmaps in Videos</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/4ezde5-nZZw"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
| <ahref="https://docs.ultralytics.com/datasets/explorer/">Ultralytics Datasets Explorer with SQL and OpenAI Integration 🚀 New</a> | <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a> | <ahref="https://youtu.be/3VryynorQeo"><center><imgwidth=30%src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png"alt="Ultralytics Youtube Video"></center></a> |
## <divalign="center">Models</div>
YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
@ -137,13 +124,13 @@ All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cf
See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco/), which include 80 pre-trained classes.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **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=coco.yaml batch=1 device=0|cpu`
@ -154,13 +141,13 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), which include 80 pre-trained classes.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **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=coco-seg.yaml batch=1 device=0|cpu`
@ -171,14 +158,13 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples with these models trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), which include 1 pre-trained class, person.
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **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=coco-pose.yaml batch=1 device=0|cpu`
@ -189,13 +175,13 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
- **mAP<sup>test</sup>** values are for single-model multiscale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
- **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`
@ -206,13 +192,13 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), which include 1000 pretrained classes.
- **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`
- **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`
@ -245,18 +231,18 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
| Label and export your custom datasets directly to YOLO11 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLO11 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
## <divalign="center">Ultralytics HUB</div>
Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
Experience seamless AI with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLO11 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** now!
We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
We love your input! Ultralytics YOLO would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
<ahref="https://console.paperspace.com/github/ultralytics/ultralytics"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a>
<ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a>
<ahref="https://www.kaggle.com/ultralytics/yolov8"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
<ahref="https://console.paperspace.com/github/ultralytics/ultralytics"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run Ultralytics on Gradient"></a>
<ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Ultralytics In Colab"></a>
<ahref="https://www.kaggle.com/ultralytics/yolov8"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open Ultralytics In Kaggle"></a>
| Label and export your custom datasets directly to YOLO11 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLO11 using [ClearML](https://clear.ml/) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
@ -69,6 +69,7 @@ Pose estimation is a technique used to determine the pose of the object relative
- [COCO](pose/coco.md): A large-scale dataset with human pose annotations designed for pose estimation tasks.
- [COCO8-pose](pose/coco8-pose.md): A smaller dataset for pose estimation tasks, containing a subset of 8 COCO images with human pose annotations.
- [Tiger-pose](pose/tiger-pose.md): A compact dataset consisting of 263 images focused on tigers, annotated with 12 keypoints per tiger for pose estimation tasks.
- [Hand-Keypoints](pose/hand-keypoints.md): A concise dataset featuring over 26,000 images centered on human hands, annotated with 21 keypoints per hand, designed for pose estimation tasks.
description: Explore the hand keypoints estimation dataset for advanced pose estimation. Learn about datasets, pretrained models, metrics, and applications for training with YOLO.
keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO, deep learning, computer vision
---
# Hand Keypoints Dataset
## Introduction
The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high accuracy and consistency, and the dataset is compatible [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) formats.
The dataset includes keypoints for hand detection. The keypoints are annotated as follows:
1. Wrist
2. Thumb (4 points)
3. Index finger (4 points)
4. Middle finger (4 points)
5. Ring finger (4 points)
6. Little finger (4 points)
Each hand has a total of 21 keypoints.
## Key Features
- **Large Dataset**: 26,768 images with hand keypoint annotations.
- **YOLOv8 Compatibility**: Ready for use with YOLOv8 models.
- **21 Keypoints**: Detailed hand pose representation.
## Dataset Structure
The hand keypoint dataset is split into two subsets:
1. **Train**: This subset contains 18,776 images from the hand keypoints dataset, annotated for training pose estimation models.
2. **Val**: This subset contains 7992 images that can be used for validation purposes during model training.
## Applications
Hand keypoints can be used for gesture recognition, AR/VR controls, robotic manipulation, and hand movement analysis in healthcare. They can be also applied in animation for motion capture and biometric authentication systems for security.
## Dataset YAML
A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Hand Keypoints dataset, the `hand-keypoints.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml).
!!! example "ultralytics/cfg/datasets/hand-keypoints.yaml"
To train a YOLOv8n-pose model on the Hand Keypoints dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
!!! example "Train Example"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
The Hand keypoints dataset contains a diverse set of images with human hands annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
The example showcases the variety and complexity of the images in the Hand Keypoints dataset and the benefits of using mosaicing during the training process.
## Citations and Acknowledgments
If you use the hand-keypoints dataset in your research or development work, please acknowledge the following sources:
!!! quote ""
=== "Credits"
We would like to thank the following sources for providing the images used in this dataset:
The images were collected and used under the respective licenses provided by each platform and are distributed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
We would also like to acknowledge the creator of this dataset, [Rion Dsilva](https://www.linkedin.com/in/rion-dsilva-043464229/), for his great contribution to Vision AI research.
## FAQ
### How do I train a YOLOv8 model on the Hand Keypoints dataset?
To train a YOLOv8 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLOv8n-pose model for 100 epochs with an image size of 640:
!!! Example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n-pose.pt") # load a pretrained model (recommended for training)
For more information, refer to the [Applications](#applications) section.
### How is the Hand Keypoints dataset structured?
The Hand Keypoints dataset is divided into two subsets:
1. **Train**: Contains 18,776 images for training pose estimation models.
2. **Val**: Contains 7,992 images for validation purposes during model training.
This structure ensures a comprehensive training and validation process. For more details, see the [Dataset Structure](#dataset-structure) section.
### How do I use the dataset YAML file for training?
The dataset configuration is defined in a YAML file, which includes paths, classes, and other relevant information. The `hand-keypoints.yaml` file can be found at [hand-keypoints.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml).
To use this YAML file for training, specify it in your training script or CLI command as shown in the training example above. For more details, refer to the [Dataset YAML](#dataset-yaml) section.
@ -118,6 +118,15 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
- **Usage**: Great for animal pose or any other pose that is not human-based.
- [Read more about Tiger-Pose](tiger-pose.md)
### Hand Keypoints
- **Description**: Hand keypoints pose dataset comprises nearly 26K images, with 18776 images allocated for training and 7992 for validation.
- **Label Format**: Same as Ultralytics YOLO format as described above, but with 21 keypoints for human hand and visible dimension.
- **Number of Classes**: 1 (Hand).
- **Keypoints**: 21 keypoints.
- **Usage**: Great for human hand pose estimation.
- [Read more about Hand Keypoints](hand-keypoints.md)
### Adding your own dataset
If you have your own dataset and would like to use it for training pose estimation models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.
@ -10,6 +10,17 @@ keywords: Ultralytics, YOLOv8, data visualization, line graphs, bar plots, pie c
This guide provides a comprehensive overview of three fundamental types of [data visualizations](https://www.ultralytics.com/glossary/data-visualization): line graphs, bar plots, and pie charts. Each section includes step-by-step instructions and code snippets on how to create these visualizations using Python.
@ -88,7 +88,7 @@ Let's say you are ready to annotate now. There are several open-source tools ava
- **[Label Studio](https://github.com/HumanSignal/label-studio)**: A flexible tool that supports a wide range of annotation tasks and includes features for managing projects and quality control.
- **[CVAT](https://github.com/cvat-ai/cvat)**: A powerful tool that supports various annotation formats and customizable workflows, making it suitable for complex projects.
- **[Labelme](https://github.com/labelmeai/labelme)**: A simple and easy-to-use tool that allows for quick annotation of images with polygons, making it ideal for straightforward tasks.
- **[Labelme](https://github.com/wkentaro/labelme)**: A simple and easy-to-use tool that allows for quick annotation of images with polygons, making it ideal for straightforward tasks.
@ -167,7 +167,7 @@ Several popular open-source tools can streamline the data annotation process:
- **[Label Studio](https://github.com/HumanSignal/label-studio)**: A flexible tool supporting various annotation tasks, project management, and quality control features.
- **[CVAT](https://www.cvat.ai/)**: Offers multiple annotation formats and customizable workflows, making it suitable for complex projects.
- **[Labelme](https://github.com/labelmeai/labelme)**: Ideal for quick and straightforward image annotation with polygons.
- **[Labelme](https://github.com/wkentaro/labelme)**: Ideal for quick and straightforward image annotation with polygons.
These tools can help enhance the efficiency and accuracy of your annotation workflows. For extensive feature lists and guides, refer to our [data annotation tools documentation](../datasets/index.md).
@ -100,7 +100,7 @@ However, if you choose to collect images or take your own pictures, you'll need
<imgwidth="100%"src="https://github.com/ultralytics/docs/releases/download/0/different-types-of-image-annotation.avif"alt="Different Types of Image Annotation">
</p>
[Data collection and annotation](./data-collection-and-annotation.md) can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme).
[Data collection and annotation](./data-collection-and-annotation.md) can be a time-consuming manual effort. Annotation tools can help make this process easier. Here are some useful open annotation tools: [LabeI Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/wkentaro/labelme).
## Step 3: [Data Augmentation](https://www.ultralytics.com/glossary/data-augmentation) and Splitting Your Dataset
@ -215,7 +215,7 @@ Data annotation is vital for teaching your model to recognize patterns. The type
- **Object Detection**: Bounding boxes drawn around objects.
- **Image Segmentation**: Each pixel labeled according to the object it belongs to.
Tools like [Label Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/labelmeai/labelme) can assist in this process. For more details, refer to our [data collection and annotation guide](./data-collection-and-annotation.md).
Tools like [Label Studio](https://github.com/HumanSignal/label-studio), [CVAT](https://github.com/cvat-ai/cvat), and [Labelme](https://github.com/wkentaro/labelme) can assist in this process. For more details, refer to our [data collection and annotation guide](./data-collection-and-annotation.md).
### What steps should I follow to augment and split my dataset effectively?
description: Discover Ultralytics YOLOv8 - the latest in real-time object detection and image segmentation. Learn its features and maximize its potential in your projects.
description: Discover Ultralytics YOLO - the latest in real-time object detection and image segmentation. Learn its features and maximize its potential in your projects.
<ahref="https://www.kaggle.com/ultralytics/yolov8"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
</div>
Introducing [Ultralytics](https://www.ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLOv8 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs.
Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioner or new to the field, this hub aims to maximize YOLOv8's potential in your projects
Explore the Ultralytics Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioner or new to the field, this hub aims to maximize YOLO's potential in your projects
<divalign="center">
<br>
@ -55,10 +55,10 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
## Where to Start
- **Install**`ultralytics` with pip and get up and running in minutes [:material-clock-fast: Get Started](quickstart.md){ .md-button }
- **Predict** new images and videos with YOLOv8 [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
- **Train** a new YOLOv8 model on your own custom dataset [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
- **Tasks** YOLOv8 tasks like segment, classify, pose and track [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
- **NEW 🚀 Explore** datasets with advanced semantic and SQL search [:material-magnify-expand: Explore a Dataset](datasets/explorer/index.md){ .md-button }
- **Predict** new images and videos with YOLO [:octicons-image-16: Predict on Images](modes/predict.md){ .md-button }
- **Train** a new YOLO model on your own custom dataset [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button }
- **Tasks** YOLO tasks like segment, classify, pose and track [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button }
- **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest SOTA models [:material-magnify-expand: Explore a Dataset](models/yolo11.md){ .md-button }
<palign="center">
<br>
@ -68,7 +68,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Train a YOLOv8 model on Your Custom Dataset in <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"target="_blank">Google Colab</a>.
<strong>Watch:</strong> How to Train a YOLO model on Your Custom Dataset in <ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"target="_blank">Google Colab</a>.
</p>
## YOLO: A Brief History
@ -84,6 +84,7 @@ Explore the YOLOv8 Docs, a comprehensive resource designed to help you understan
- [YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of YOLO by Ultralytics. As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports a full range of vision AI tasks, including [detection](tasks/detect.md), [segmentation](tasks/segment.md), [pose estimation](tasks/pose.md), [tracking](modes/track.md), and [classification](tasks/classify.md). This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.
- [YOLOv9](models/yolov9.md) introduces innovative methods like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN).
- [YOLOv10](models/yolov10.md) is created by researchers from [Tsinghua University](https://www.tsinghua.edu.cn/en/) using the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/). This version provides real-time [object detection](tasks/detect.md) advancements by introducing an End-to-End head that eliminates Non-Maximum Suppression (NMS) requirements.
- **[YOLO11](models/yolo11.md) NEW 🚀**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks.
## YOLO Licenses: How is Ultralytics YOLO licensed?
@ -98,7 +99,7 @@ Our licensing strategy is designed to ensure that any improvements to our open-s
### What is Ultralytics YOLO and how does it improve object detection?
Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. It builds on previous versions by introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLOv8 supports various [vision AI tasks](tasks/index.md) such as detection, segmentation, pose estimation, tracking, and classification. Its state-of-the-art architecture ensures superior speed and accuracy, making it suitable for diverse applications, including edge devices and cloud APIs.
Ultralytics YOLO is the latest advancement in the acclaimed YOLO (You Only Look Once) series for real-time object detection and image segmentation. It builds on previous versions by introducing new features and improvements for enhanced performance, flexibility, and efficiency. YOLO supports various [vision AI tasks](tasks/index.md) such as detection, segmentation, pose estimation, tracking, and classification. Its state-of-the-art architecture ensures superior speed and accuracy, making it suitable for diverse applications, including edge devices and cloud APIs.
### How can I get started with YOLO installation and setup?
@ -121,7 +122,7 @@ Training a custom YOLO model on your dataset involves a few detailed steps:
For a detailed walkthrough, check out our [Train a Model](modes/train.md) guide, which includes examples and tips for optimizing your training process.
@ -140,7 +141,7 @@ For more details, visit our [Licensing](https://www.ultralytics.com/license) pag
Ultralytics YOLO supports efficient and customizable multi-object tracking. To utilize tracking capabilities, you can use the `yolo track` command as shown below:
```bash
yolo track model=yolov8n.pt source=video.mp4
yolo track model=yolo11n.pt source=video.mp4
```
For a detailed guide on setting up and running object tracking, check our [tracking mode](modes/track.md) documentation, which explains the configuration and practical applications in real-time scenarios.
@ -148,7 +148,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
- **Model Artifacts Management**: Access and share model checkpoints, facilitating easy deployment and collaboration.
- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases' image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media#image-overlays). [See how Weights & Biases' image overlays helps visualize model inferences](https://imgur.com/a/UTSiufs).
- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases' image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media/#image-overlays). [See how Weights & Biases' image overlays helps visualize model inferences](https://imgur.com/a/UTSiufs).
By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.
@ -156,7 +156,7 @@ By using these features, you can effectively track, analyze, and optimize your Y
This guide helped you explore Ultralytics' YOLOv8 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics).
For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics/).
Also, be sure to check out the [Ultralytics integration guide page](../integrations/index.md), to learn more about different exciting integrations.
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
| `degrees` | `float` | `0.0` | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations. |
| `translate` | `float` | `0.1` | `0.0 - 1.0` | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects. |
| `scale` | `float` | `0.5` | `>=0.0` | Scales the image by a gain factor, simulating objects at different distances from the camera. |
| `shear` | `float` | `0.0` | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles. |
| `perspective` | `float` | `0.0` | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space. |
| `flipud` | `float` | `0.0` | `0.0 - 1.0` | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics. |
| `fliplr` | `float` | `0.5` | `0.0 - 1.0` | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity. |
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion. |
| `auto_augment` | `str` | `randaugment` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. |
| Argument | Type | Default | Range | Description |
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
| `degrees` | `float` | `0.0` | `-180 - +180` | Rotates the image randomly within the specified degree range, improving the model's ability to recognize objects at various orientations. |
| `translate` | `float` | `0.1` | `0.0 - 1.0` | Translates the image horizontally and vertically by a fraction of the image size, aiding in learning to detect partially visible objects. |
| `scale` | `float` | `0.5` | `>=0.0` | Scales the image by a gain factor, simulating objects at different distances from the camera. |
| `shear` | `float` | `0.0` | `-180 - +180` | Shears the image by a specified degree, mimicking the effect of objects being viewed from different angles. |
| `perspective` | `float` | `0.0` | `0.0 - 0.001` | Applies a random perspective transformation to the image, enhancing the model's ability to understand objects in 3D space. |
| `flipud` | `float` | `0.0` | `0.0 - 1.0` | Flips the image upside down with the specified probability, increasing the data variability without affecting the object's characteristics. |
| `fliplr` | `float` | `0.5` | `0.0 - 1.0` | Flips the image left to right with the specified probability, useful for learning symmetrical objects and increasing dataset diversity. |
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion. |
| `copy_paste_mode` | `str` | `flip` | - | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`). |
| `auto_augment` | `str` | `randaugment` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `crop_fraction` | `float` | `1.0` | `0.1 - 1.0` | Crops the classification image to a fraction of its size to emphasize central features and adapt to object scales, reducing background distractions. |
@ -17,16 +17,17 @@ Here are some of the key models supported:
3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
4. **[YOLOv6](yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots.
5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
6. **[YOLOv8](yolov8.md)**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
9. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
10. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
11. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
12. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
15. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
9. **[YOLO11](yolo11.md) NEW 🚀**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks.
10. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
11. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
12. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
13. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
description: Discover YOLO11, the latest advancement in state-of-the-art object detection, offering unmatched accuracy and efficiency for diverse computer vision tasks.
YOLO11 is the latest iteration in the [Ultralytics](https://www.ultralytics.com) YOLO series of real-time object detectors, redefining what's possible with cutting-edge [accuracy](https://www.ultralytics.com/glossary/accuracy), speed, and efficiency. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks.
<strong>Watch:</strong> Ultralytics YOLO11 Announcement at <ahref="https://www.ultralytics.com/events/yolovision">YOLO Vision 2024</a>
</p>
## Key Features
- **Enhanced Feature Extraction:** YOLO11 employs an improved backbone and neck architecture, which enhances [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) capabilities for more precise object detection and complex task performance.
- **Optimized for Efficiency and Speed:** YOLO11 introduces refined architectural designs and optimized training pipelines, delivering faster processing speeds and maintaining an optimal balance between accuracy and performance.
- **Greater Accuracy with Fewer Parameters:** With advancements in model design, YOLO11m achieves a higher [mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) on the COCO dataset while using 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
- **Adaptability Across Environments:** YOLO11 can be seamlessly deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs, ensuring maximum flexibility.
- **Broad Range of Supported Tasks:** Whether it's object detection, instance segmentation, image classification, pose estimation, or oriented object detection (OBB), YOLO11 is designed to cater to a diverse set of computer vision challenges.
## Supported Tasks and Modes
YOLO11 builds upon the versatile model range introduced in YOLOv8, offering enhanced support across various computer vision tasks:
| Model | Filenames | Task | Inference | Validation | Training | Export |
This table provides an overview of the YOLO11 model variants, showcasing their applicability in specific tasks and compatibility with operational modes such as Inference, Validation, Training, and Export. This flexibility makes YOLO11 suitable for a wide range of applications in computer vision, from real-time detection to complex segmentation tasks.
## Performance Metrics
!!! performance
=== "Detection (COCO)"
See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.
See [Segmentation Docs](../tasks/segment.md) for usage examples with these models trained on [COCO](../datasets/segment/coco.md), which include 80 pre-trained classes.
See [Classification Docs](../tasks/classify.md) for usage examples with these models trained on [ImageNet](../datasets/classify/imagenet.md), which include 1000 pre-trained classes.
See [Pose Estimation Docs](../tasks/pose.md) for usage examples with these models trained on [COCO](../datasets/pose/coco.md), which include 1 pre-trained class, 'person'.
See [Oriented Detection Docs](../tasks/obb.md) for usage examples with these models trained on [DOTAv1](../datasets/obb/dota-v2.md#dota-v10), which include 15 pre-trained classes.
This section provides simple YOLO11 training and inference examples. For full documentation on these and other [modes](../modes/index.md), see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md), and [Export](../modes/export.md) docs pages.
Note that the example below is for YOLO11 [Detect](../tasks/detect.md) models for [object detection](https://www.ultralytics.com/glossary/object-detection). For additional supported tasks, see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md), and [Pose](../tasks/pose.md) docs.
!!! example
=== "Python"
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in Python:
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train the model on the COCO8 example dataset for 100 epochs
Please note that the DOI is pending and will be added to the citation once it is available. YOLO11 models are provided under [AGPL-3.0](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) and [Enterprise](https://www.ultralytics.com/license) licenses.
## FAQ
### What are the key improvements in Ultralytics YOLO11 compared to previous versions?
Ultralytics YOLO11 introduces several significant advancements over its predecessors. Key improvements include:
- **Enhanced Feature Extraction:** YOLO11 employs an improved backbone and neck architecture, enhancing [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) capabilities for more precise object detection.
- **Optimized Efficiency and Speed:** Refined architectural designs and optimized training pipelines deliver faster processing speeds while maintaining a balance between accuracy and performance.
- **Greater Accuracy with Fewer Parameters:** YOLO11m achieves higher mean Average [Precision](https://www.ultralytics.com/glossary/precision) (mAP) on the COCO dataset with 22% fewer parameters than YOLOv8m, making it computationally efficient without compromising accuracy.
- **Adaptability Across Environments:** YOLO11 can be deployed across various environments, including edge devices, cloud platforms, and systems supporting NVIDIA GPUs.
- **Broad Range of Supported Tasks:** YOLO11 supports diverse computer vision tasks such as object detection, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), image classification, pose estimation, and oriented object detection (OBB).
### How do I train a YOLO11 model for object detection?
Training a YOLO11 model for object detection can be done using Python or CLI commands. Below are examples for both methods:
!!! Example
=== "Python"
```python
from ultralytics import YOLO
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train the model on the COCO8 example dataset for 100 epochs
For more detailed instructions, refer to the [Train](../modes/train.md) documentation.
### What tasks can YOLO11 models perform?
YOLO11 models are versatile and support a wide range of computer vision tasks, including:
- **Object Detection:** Identifying and locating objects within an image.
- **Instance Segmentation:** Detecting objects and delineating their boundaries.
- **[Image Classification](https://www.ultralytics.com/glossary/image-classification):** Categorizing images into predefined classes.
- **Pose Estimation:** Detecting and tracking keypoints on human bodies.
- **Oriented Object Detection (OBB):** Detecting objects with rotation for higher precision.
For more information on each task, see the [Detection](../tasks/detect.md), [Instance Segmentation](../tasks/segment.md), [Classification](../tasks/classify.md), [Pose Estimation](../tasks/pose.md), and [Oriented Detection](../tasks/obb.md) documentation.
### How does YOLO11 achieve greater accuracy with fewer parameters?
YOLO11 achieves greater accuracy with fewer parameters through advancements in model design and optimization techniques. The improved architecture allows for efficient feature extraction and processing, resulting in higher mean Average Precision (mAP) on datasets like COCO while using 22% fewer parameters than YOLOv8m. This makes YOLO11 computationally efficient without compromising on accuracy, making it suitable for deployment on resource-constrained devices.
### Can YOLO11 be deployed on edge devices?
Yes, YOLO11 is designed for adaptability across various environments, including edge devices. Its optimized architecture and efficient processing capabilities make it suitable for deployment on edge devices, cloud platforms, and systems supporting NVIDIA GPUs. This flexibility ensures that YOLO11 can be used in diverse applications, from real-time detection on mobile devices to complex segmentation tasks in cloud environments. For more details on deployment options, refer to the [Export](../modes/export.md) documentation.
YOLOv6 provides various pre-trained models with different scales:
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA T4 GPU.
- YOLOv6-S: 45.0% AP at 484 FPS.
- YOLOv6-M: 50.0% AP at 226 FPS.
- YOLOv6-L: 52.8% AP at 116 FPS.
@ -151,7 +151,7 @@ YOLOv6 offers multiple versions, each optimized for different performance requir
- YOLOv6-L: 52.8% AP at 116 FPS
- YOLOv6-L6: State-of-the-art accuracy in real-time scenarios
These models are evaluated on the COCO dataset using an NVIDIA Tesla T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
These models are evaluated on the COCO dataset using an NVIDIA T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
### How does the Anchor-Aided Training (AAT) strategy benefit YOLOv6?
description: Learn how to install Ultralytics using pip, conda, or Docker. Follow our step-by-step guide for a seamless setup of YOLOv8 with thorough instructions.
description: Learn how to install Ultralytics using pip, conda, or Docker. Follow our step-by-step guide for a seamless setup of YOLO with thorough instructions.
Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLO via the `ultralytics` pip package for the latest stable release or by cloning the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics) for the most up-to-date version. Docker can be used to execute the package in an isolated container, avoiding local installation.
<palign="center">
<br>
@ -151,7 +151,7 @@ See the `ultralytics` [pyproject.toml](https://github.com/ultralytics/ultralytic
## Use Ultralytics with CLI
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLO from the command line.
!!! example
@ -172,28 +172,28 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
@ -212,18 +212,18 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
- `yolo predict model=yolo11n.pt, imgsz=640, conf=0.25` ❌ (do not use `,`)
- `yolo predict --model yolo11n.pt --imgsz 640 --conf 0.25` ❌ (do not use `--`)
[CLI Guide](usage/cli.md){ .md-button }
## Use Ultralytics with Python
YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in their projects. This makes YOLOv8's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
YOLO's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement [object detection](https://www.ultralytics.com/glossary/object-detection), segmentation, and classification in their projects. This makes YOLO's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the [Python Guide](usage/python.md) to learn more about using YOLOv8 within your Python projects.
For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX format with just a few lines of code. Check out the [Python Guide](usage/python.md) to learn more about using YOLO within your Python projects.
!!! example
@ -231,10 +231,10 @@ For example, users can load a model, train it, evaluate its performance on a val
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO("yolov8n.yaml")
model = YOLO("yolo11n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
For more instructions, visit the [Conda quickstart guide](guides/conda-quickstart.md).
### What are the advantages of using Docker to run Ultralytics YOLOv8?
### What are the advantages of using Docker to run Ultralytics YOLO?
Using Docker to run Ultralytics YOLOv8 provides an isolated and consistent environment, ensuring smooth performance across different systems. It also eliminates the complexity of local installation. Official Docker images from Ultralytics are available on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), with different variants tailored for GPU, CPU, ARM64, NVIDIA Jetson, and Conda environments. Below are the commands to pull and run the latest image:
Using Docker to run Ultralytics YOLO provides an isolated and consistent environment, ensuring smooth performance across different systems. It also eliminates the complexity of local installation. Official Docker images from Ultralytics are available on [Docker Hub](https://hub.docker.com/r/ultralytics/ultralytics), with different variants tailored for GPU, CPU, ARM64, NVIDIA Jetson, and Conda environments. Below are the commands to pull and run the latest image:
```bash
# Pull the latest ultralytics image from Docker Hub
@ -410,9 +410,9 @@ pip install -e .
This approach allows you to contribute to the project or experiment with the latest source code. For more details, visit the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics).
### Why should I use Ultralytics YOLOv8 CLI?
### Why should I use Ultralytics YOLO CLI?
The Ultralytics YOLOv8 command line interface (CLI) simplifies running object detection tasks without requiring Python code. You can execute single-line commands for tasks like training, validation, and prediction straight from your terminal. The basic syntax for `yolo` commands is:
The Ultralytics YOLO command line interface (CLI) simplifies running object detection tasks without requiring Python code. You can execute single-line commands for tasks like training, validation, and prediction straight from your terminal. The basic syntax for `yolo` commands is:
```bash
yolo TASK MODE ARGS
@ -421,7 +421,7 @@ yolo TASK MODE ARGS
For example, to train a detection model with specified parameters:
@ -18,7 +18,7 @@ Let's begin by creating a virtual machine that's tuned for deep learning:
1. Head over to the [GCP marketplace](https://console.cloud.google.com/marketplace/details/click-to-deploy-images/deeplearning) and select the **Deep Learning VM**.
2. Opt for a **n1-standard-8** instance; it offers a balance of 8 vCPUs and 30 GB of memory, ideally suited for our needs.
3. Next, select a GPU. This depends on your workload; even a basic one like the Tesla T4 will markedly accelerate your model training.
3. Next, select a GPU. This depends on your workload; even a basic one like the T4 will markedly accelerate your model training.
4. Tick the box for 'Install NVIDIA GPU driver automatically on first startup?' for hassle-free setup.
5. Allocate a 300 GB SSD Persistent Disk to ensure you don't bottleneck on I/O operations.
6. Hit 'Deploy' and let GCP do its magic in provisioning your custom Deep Learning VM.
fatih c. akyon
2 months ago
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