---
comments: true
description: Discover Ultralytics YOLOv8, an advancement in real-time object detection, optimizing performance with an array of pretrained models for diverse tasks.
keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer vision, advanced object detection, AI, machine learning, deep learning
---

# Explore Ultralytics YOLOv8

## Overview

YOLOv8 was released by Ultralytics on January 10, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025) tasks in a wide range of applications.

![YOLOv8 performance comparison charts](https://cdn.jsdelivr.net/gh/ultralytics/assets@main/docs/yolov8-comparison-plots.avif)

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/Na0HvJ4hkk0"
    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> Ultralytics YOLOv8 Model Overview
</p>

!!! tip "Try on Ultralytics Platform"

    Explore and run YOLOv8 models directly on [Ultralytics Platform](https://platform.ultralytics.com/ultralytics/yolov8).

## Key Features of YOLOv8

- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) and [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
- **Anchor-free Split Ultralytics Head:** YOLOv8 adopts an anchor-free split Ultralytics head, which contributes to better accuracy and a more efficient detection process compared to anchor-based approaches.
- **Optimized Accuracy-Speed Tradeoff:** With a focus on maintaining an optimal balance between accuracy and speed, YOLOv8 is suitable for real-time object detection tasks in diverse application areas.
- **Variety of Pretrained Models:** YOLOv8 offers a range of pretrained models to cater to various tasks and performance requirements, making it easier to find the right model for your specific use case.

## Supported Tasks and Modes

The YOLOv8 series offers a diverse range of models, each specialized for specific tasks in computer vision. These models are designed to cater to various requirements, from object detection to more complex tasks like [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints detection, oriented object detection, and classification.

Each variant of the YOLOv8 series is optimized for its respective task, ensuring high performance and accuracy. Additionally, these models are compatible with various operational modes including [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), facilitating their use in different stages of deployment and development.

| Model       | Filenames                                                                                                      | Task                                         | Inference | Validation | Training | Export |
| ----------- | -------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLOv8      | `yolov8n.pt` `yolov8s.pt` `yolov8m.pt` `yolov8l.pt` `yolov8x.pt`                                               | [Detection](../tasks/detect.md)              | ✅        | ✅         | ✅       | ✅     |
| YOLOv8-seg  | `yolov8n-seg.pt` `yolov8s-seg.pt` `yolov8m-seg.pt` `yolov8l-seg.pt` `yolov8x-seg.pt`                           | [Instance Segmentation](../tasks/segment.md) | ✅        | ✅         | ✅       | ✅     |
| YOLOv8-pose | `yolov8n-pose.pt` `yolov8s-pose.pt` `yolov8m-pose.pt` `yolov8l-pose.pt` `yolov8x-pose.pt` `yolov8x-pose-p6.pt` | [Pose/Keypoints](../tasks/pose.md)           | ✅        | ✅         | ✅       | ✅     |
| YOLOv8-obb  | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt`                           | [Oriented Detection](../tasks/obb.md)        | ✅        | ✅         | ✅       | ✅     |
| YOLOv8-cls  | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt`                           | [Classification](../tasks/classify.md)       | ✅        | ✅         | ✅       | ✅     |

This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).

## Performance Metrics

<script async src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script defer src="../../javascript/benchmark.js"></script>

<canvas id="modelComparisonChart" width="1024" height="400" active-models='["YOLOv8"]'></canvas>

!!! tip "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 pretrained classes.

        | Model                                                                                | size<br><sup>(pixels)</sup> | mAP<sup>val<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
        | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
        | [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
        | [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
        | [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
        | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
        | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |

    === "Detection (Open Images V7)"

        See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [Open Image V7](../datasets/detect/open-images-v7.md), which include 600 pretrained classes.

        | Model                                                                                     | size<br><sup>(pixels)</sup> | mAP<sup>val<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
        | ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
        | [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-oiv7.pt) | 640                   | 18.4                 | 142.4                          | 1.21                                | 3.5                | 10.5              |
        | [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-oiv7.pt) | 640                   | 27.7                 | 183.1                          | 1.40                                | 11.4               | 29.7              |
        | [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-oiv7.pt) | 640                   | 33.6                 | 408.5                          | 2.26                                | 26.2               | 80.6              |
        | [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-oiv7.pt) | 640                   | 34.9                 | 596.9                          | 2.43                                | 44.1               | 167.4             |
        | [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-oiv7.pt) | 640                   | 36.3                 | 860.6                          | 3.56                                | 68.7               | 260.6             |

    === "Segmentation (COCO)"

        See [Segmentation Docs](../tasks/segment.md) for usage examples with these models trained on [COCO](../datasets/segment/coco.md), which include 80 pretrained classes.

        | Model                                                                                        | size<br><sup>(pixels)</sup> | mAP<sup>box<br>50-95</sup> | mAP<sup>mask<br>50-95</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
        | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
        | [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
        | [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
        | [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
        | [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
        | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |

    === "Classification (ImageNet)"

        See [Classification Docs](../tasks/classify.md) for usage examples with these models trained on [ImageNet](../datasets/classify/imagenet.md), which include 1000 pretrained classes.

        | Model                                                                                        | size<br><sup>(pixels)</sup> | acc<br><sup>top1</sup> | acc<br><sup>top5</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B) at 224</sup> |
        | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
        | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 0.5                      |
        | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 1.7                      |
        | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 5.3                      |
        | [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-cls.pt) | 224                   | 76.8             | 93.5             | 163.0                          | 0.87                                | 37.5               | 12.3                     |
        | [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-cls.pt) | 224                   | 79.0             | 94.6             | 232.0                          | 1.01                                | 57.4               | 19.0                     |

    === "Pose (COCO)"

        See [Pose Estimation Docs](../tasks/pose.md) for usage examples with these models trained on [COCO](../datasets/pose/coco.md), which include 1 pretrained class, 'person'.

        | Model                                                                                                | size<br><sup>(pixels)</sup> | mAP<sup>pose<br>50-95</sup> | mAP<sup>pose<br>50</sup> | Speed<br><sup>CPU ONNX<br>(ms)</sup> | Speed<br><sup>A100 TensorRT<br>(ms)</sup> | params<br><sup>(M)</sup> | FLOPs<br><sup>(B)</sup> |
        | ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
        | [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-pose.pt)       | 640                   | 50.4                  | 80.1               | 131.8                          | 1.18                                | 3.3                | 9.2               |
        | [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-pose.pt)       | 640                   | 60.0                  | 86.2               | 233.2                          | 1.42                                | 11.6               | 30.2              |
        | [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-pose.pt)       | 640                   | 65.0                  | 88.8               | 456.3                          | 2.00                                | 26.4               | 81.0              |
        | [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-pose.pt)       | 640                   | 67.6                  | 90.0               | 784.5                          | 2.59                                | 44.4               | 168.6             |
        | [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-pose.pt)       | 640                   | 69.2                  | 90.2               | 1607.1                         | 3.73                                | 69.4               | 263.2             |
        | [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-pose-p6.pt) | 1280                  | 71.6                  | 91.2               | 4088.7                         | 10.04                               | 99.1               | 1066.4            |

    === "OBB (DOTAv1)"

        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 pretrained classes.

        | Model                                                                                        | size<br><sup>(pixels)</sup> | mAP<sup>test<br>50</sup>   | Speed<br><sup>CPU ONNX<br>(ms)</sup>   | Speed<br><sup>A100 TensorRT<br>(ms)</sup>   | params<br><sup>(M)</sup>   | FLOPs<br><sup>(B)</sup> |
        |----------------------------------------------------------------------------------------------|-----------------------| -------------------- | -------------------------------- | ------------------------------------- | -------------------- | ----------------- |
        | [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8n-obb.pt) | 1024                  | 78.0                 | 204.77                           | 3.57                                  | 3.1                  | 23.3              |
        | [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8s-obb.pt) | 1024                  | 79.5                 | 424.88                           | 4.07                                  | 11.4                 | 76.3              |
        | [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8m-obb.pt) | 1024                  | 80.5                 | 763.48                           | 7.61                                  | 26.4                 | 208.6             |
        | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8l-obb.pt) | 1024                  | 80.7                 | 1278.42                          | 11.83                                 | 44.5                 | 433.8             |
        | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov8x-obb.pt) | 1024                  | 81.36                | 1759.10                          | 13.23                                 | 69.5                 | 676.7             |

## YOLOv8 Usage Examples

This example provides simple YOLOv8 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 the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md), [OBB](../tasks/obb.md) docs 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 YOLOv8n model
        model = YOLO("yolov8n.pt")

        # Display model information (optional)
        model.info()

        # Train the model on the COCO8 example dataset for 100 epochs
        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

        # Run inference with the YOLOv8n model on the 'bus.jpg' image
        results = model("path/to/bus.jpg")
        ```

    === "CLI"

        CLI commands are available to directly run the models:

        ```bash
        # Load a COCO-pretrained YOLOv8n model and train it on the COCO8 example dataset for 100 epochs
        yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640

        # Load a COCO-pretrained YOLOv8n model and run inference on the 'bus.jpg' image
        yolo predict model=yolov8n.pt source=path/to/bus.jpg
        ```

## Citations and Acknowledgments

!!! tip "Ultralytics YOLOv8 Publication"

    Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com/).

If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:

!!! quote ""

    === "BibTeX"

        ```bibtex
        @software{yolov8_ultralytics,
          author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
          title = {Ultralytics YOLOv8},
          version = {8.0.0},
          year = {2023},
          url = {https://github.com/ultralytics/ultralytics},
          orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
          license = {AGPL-3.0}
        }
        ```

Please note that the DOI is pending and will be added to the citation once it is available. YOLOv8 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 is YOLOv8 and how does it differ from previous YOLO versions?

YOLOv8 is designed to improve real-time object detection performance with advanced features. Unlike earlier versions, YOLOv8 incorporates an **anchor-free split Ultralytics head**, state-of-the-art [backbone](https://www.ultralytics.com/glossary/backbone) and neck architectures, and offers optimized [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff, making it ideal for diverse applications. For more details, check the [Overview](#overview) and [Key Features](#key-features-of-yolov8) sections.

### How can I use YOLOv8 for different computer vision tasks?

YOLOv8 supports a wide range of computer vision tasks, including object detection, instance segmentation, pose/keypoints detection, oriented object detection, and classification. Each model variant is optimized for its specific task and compatible with various operational modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). Refer to the [Supported Tasks and Modes](#supported-tasks-and-modes) section for more information.

### What are the performance metrics for YOLOv8 models?

YOLOv8 models achieve state-of-the-art performance across various benchmarking datasets. For instance, the YOLOv8n model achieves a mAP (mean Average Precision) of 37.3 on the COCO dataset and a speed of 0.99 ms on A100 TensorRT. Detailed performance metrics for each model variant across different tasks and datasets can be found in the [Performance Metrics](#performance-metrics) section.

### How do I train a YOLOv8 model?

Training a YOLOv8 model can be done using either Python or CLI. Below are examples for training a model using a COCO-pretrained YOLOv8 model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch):

!!! example

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a COCO-pretrained YOLOv8n model
        model = YOLO("yolov8n.pt")

        # Train the model on the COCO8 example dataset for 100 epochs
        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
        ```

    === "CLI"

        ```bash
        yolo train model=yolov8n.pt data=coco8.yaml epochs=100 imgsz=640
        ```

For further details, visit the [Training](../modes/train.md) documentation.

### Can I benchmark YOLOv8 models for performance?

Yes, YOLOv8 models can be benchmarked for performance in terms of speed and accuracy across various export formats. You can use PyTorch, ONNX, TensorRT, and more for benchmarking. Below are example commands for benchmarking using Python and CLI:

!!! example

    === "Python"

        ```python
        from ultralytics.utils.benchmarks import benchmark

        # Benchmark on GPU
        benchmark(model="yolov8n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)
        ```

    === "CLI"

        ```bash
        yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
        ```

For additional information, check the [Performance Metrics](#performance-metrics) section.
