description: Discover YOLOv8, the latest advancement in real-time object detection, optimizing performance with an array of pre-trained models for diverse tasks.
YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various object detection tasks in a wide range of applications.
- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and 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 Pre-trained Models:** YOLOv8 offers a range of pre-trained models to cater to various tasks and performance requirements, making it easier to find the right model for your specific use case.
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, 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.
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.
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 [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 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 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.
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://ultralytics.com/license) licenses.
### What differentiates YOLOv8 from previous YOLO versions?
YOLOv8 builds upon the advancements of its predecessors by incorporating state-of-the-art backbone and neck architectures for improved feature extraction and object detection performance. It utilizes an anchor-free split head for better accuracy and efficiency. With a focus on maintaining the optimal accuracy-speed tradeoff, YOLOv8 is suitable for real-time object detection across diverse applications. Explore more in [YOLOv8 Key Features](#key-features).
### How can I use YOLOv8 for different tasks like segmentation and pose estimation?
YOLOv8 is versatile, offering specialized variants for various tasks such as object detection, instance segmentation, pose/keypoints detection, oriented object detection, and classification. These models come pre-trained and are optimized for high performance and accuracy. For more details, refer to the [Supported Tasks and Modes](#supported-tasks-and-modes).
### How do I run inference using a YOLOv8 model in Python?
To run inference with a YOLOv8 model in Python, you can use the `YOLO` class from the Ultralytics package. Here's a basic example:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
results = model("path/to/image.jpg")
```
For detailed examples, see the [Usage Examples](#usage-examples) section.
### What are the performance benchmarks for YOLOv8 models?
YOLOv8 models are benchmarked on datasets such as COCO and Open Images V7, showing significant improvements in mAP and speed across various hardware setups. Detailed performance metrics include parameters, FLOPs, and inference speeds on different devices. For comprehensive benchmark details, visit [Performance Metrics](#performance-metrics).
### How do I export a YOLOv8 model for deployment?
You can export YOLOv8 models to various formats like ONNX, TensorRT, and CoreML for seamless deployment across different platforms. The export process ensures maximum compatibility and performance optimization. Learn more about exporting models in the [Export](../modes/export.md) section.
