---
comments: true
description: Explore the thrilling features of YOLOv8, the latest version of our real-time object detector! Learn how advanced architectures, pre-trained models and optimal balance between accuracy & speed make YOLOv8 the perfect choice for your object detection tasks.
keywords: YOLOv8, Ultralytics, real-time object detector, pre-trained models, documentation, object detection, YOLO series, advanced architectures, accuracy, speed
---
# YOLOv8
## Overview
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.
## Key Features
- **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.
## Supported Tasks
| Model Type | Pre-trained Weights | Task |
|-------------|---------------------------------------------------------------------------------------------------------------------|-----------------------|
| YOLOv8 | `yolov8n.pt` , `yolov8s.pt` , `yolov8m.pt` , `yolov8l.pt` , `yolov8x.pt` | Detection |
| YOLOv8-seg | `yolov8n-seg.pt` , `yolov8s-seg.pt` , `yolov8m-seg.pt` , `yolov8l-seg.pt` , `yolov8x-seg.pt` | Instance Segmentation |
| YOLOv8-pose | `yolov8n-pose.pt` , `yolov8s-pose.pt` , `yolov8m-pose.pt` , `yolov8l-pose.pt` , `yolov8x-pose.pt` , `yolov8x-pose-p6.pt` | Pose/Keypoints |
| YOLOv8-cls | `yolov8n-cls.pt` , `yolov8s-cls.pt` , `yolov8m-cls.pt` , `yolov8l-cls.pt` , `yolov8x-cls.pt` | Classification |
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
!!! Performance
=== "Detection (COCO)"
| Model | size< br > < sup > (pixels) | mAP< sup > val< br > 50-95 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt ) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt ) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt ) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt ) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt ) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
=== "Detection (Open Images V7)"
See [Detection Docs ](https://docs.ultralytics.com/tasks/detect/ ) for usage examples with these models trained on [Open Image V7 ](https://docs.ultralytics.com/datasets/detect/open-images-v7/ ), which include 600 pre-trained classes.
| Model | size< br > < sup > (pixels) | mAP< sup > val< br > 50-95 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) |
| ----------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-oiv7.pt ) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
| [YOLOv8s ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-oiv7.pt ) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
| [YOLOv8m ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-oiv7.pt ) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
| [YOLOv8l ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-oiv7.pt ) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
| [YOLOv8x ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-oiv7.pt ) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
=== "Segmentation (COCO)"
| Model | size< br > < sup > (pixels) | mAP< sup > box< br > 50-95 | mAP< sup > mask< br > 50-95 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) |
| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-seg ](https://github.com/ultralytics/assets/releases/download/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/yolov8x-seg.pt ) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
=== "Classification (ImageNet)"
| Model | size< br > < sup > (pixels) | acc< br > < sup > top1 | acc< br > < sup > top5 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) at 640 |
| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n-cls ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt ) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
| [YOLOv8s-cls ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt ) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
| [YOLOv8m-cls ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt ) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
| [YOLOv8l-cls ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt ) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
| [YOLOv8x-cls ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt ) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
=== "Pose (COCO)"
| Model | size< br > < sup > (pixels) | mAP< sup > pose< br > 50-95 | mAP< sup > pose< br > 50 | Speed< br > < sup > CPU ONNX< br > (ms) | Speed< br > < sup > A100 TensorRT< br > (ms) | params< br > < sup > (M) | FLOPs< br > < sup > (B) |
| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-pose ](https://github.com/ultralytics/assets/releases/download/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/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/v0.0.0/yolov8x-pose-p6.pt ) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
## Usage
You can use YOLOv8 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv8 models for inference:
!!! example ""
This example provides simple inference code for YOLOv8. For more options including handling inference results see [Predict ](../modes/predict.md ) mode. For using YOLOv8 with additional modes see [Train ](../modes/train.md ), [Val ](../modes/val.md ) and [Export ](../modes/export.md ).
=== "Python"
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 Acknowledgements
If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:
!!! note ""
=== "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. The usage of the software is in accordance with the AGPL-3.0 license.
