Model weights · YOLOv8

Download YOLOv8 weights

The anchor-free model that made multi-task YOLO mainstream: one architecture for detection, segmentation, pose, classification and OBB, with the largest ecosystem. Pick a size below and download the official .pt checkpoint in one click, with published COCO accuracy, size and license all in view.

AGPL-3.0Ultralytics · 2023 · Ultralytics (PyTorch)

YOLOv8 checkpoints, pick a size and download

The anchor-free model that made multi-task YOLO mainstream: one architecture for detection, segmentation, pose, classification and OBB, with the largest ecosystem.

  • YOLOv8n
    yolov8n.pt
    COCO mAP
    37.3
    Params
    3.2M
  • YOLOv8s
    yolov8s.pt
    COCO mAP
    44.9
    Params
    11.2M
  • YOLOv8m
    yolov8m.pt
    COCO mAP
    50.2
    Params
    25.9M
  • YOLOv8l
    yolov8l.pt
    COCO mAP
    52.9
    Params
    43.7M
  • YOLOv8x
    yolov8x.pt
    COCO mAP
    53.9
    Params
    68.2M

Scores are COCO mAP at 640px, published by the authors. Weights host: github.com. Clicking Download verifies the file and starts it straight from the official CDN.

How to load YOLOv8 weights

Install the Ultralytics package, then point the loader at the checkpoint. It downloads automatically on first use, or you can pass the local path to the file you downloaded above.

pip install ultralytics
from ultralytics import YOLO

# Downloads on first use, or pass a local path to your .pt file
model = YOLO("yolov8n.pt")
results = model("image.jpg")

Want to see the architecture? Export to ONNX and open it in the ONNX visualizer for a labelled diagram with shapes and parameter counts.

Open visualizer →

Choosing a YOLOv8 checkpoint

Read off the table above: what each step up the size ladder costs and returns for this family specifically, where it stops being worth it, and what the license actually permits.

What the size ladder buys

YOLOv8 spans YOLOv8n at 3.2M parameters to YOLOv8x at 68.2M, 21.3× the model, for 16.6 more COCO mAP (37.3 to 53.9). That works out to roughly 0.26 points per additional million parameters across the whole range, and the return is front-loaded: the early steps are much cheaper than the last one. Published figures from Ultralytics at 640px, not re-measured here.

Where the ladder stops paying

The best value step is YOLOv8n → YOLOv8s, worth 0.95 COCO mAP points per million parameters. The worst is YOLOv8l → YOLOv8x at 0.04, or 23× less efficient, for 24.5M extra parameters and only 1.0 more points. If you are latency- or memory-bound, that is the step to skip; if you are accuracy-bound and the compute is free, it is the only place left to get it.

Where it sits in the catalog

Against the 8 families here that publish COCO mAP, YOLOv8's best checkpoint ranks 7th at 53.9, against 57.5 for YOLO26. Read that as a tier indicator rather than a head-to-head: the families use different size ladders, input sizes and training recipes, and each number is the authors' own. The closest alternatives for object detection are YOLO26, YOLO11 and YOLO12.

Licensing, in practice

The weights are AGPL-3.0. AGPL-3.0 is free for open-source, research and internal use; a closed-source commercial product needs an Ultralytics Enterprise license.

About YOLOv8

YOLOv8 popularized anchor-free, multi-task YOLO across the industry. Its C2f backbone and decoupled head cover all five tasks, and years of tutorials, integrations and deployments make it the most widely referenced YOLO in the wild, even as YOLO11 and YOLO26 surpass it on accuracy-per-parameter. Its weights remain the most common starting point for transfer learning.

Author
Ultralytics
Released
2023
Tasks
Detect, Segment, Classify, Pose, OBB
Framework
Ultralytics (PyTorch)
Input size
640px
License
AGPL-3.0
Resources
Compare: YOLO26 vs YOLOv8 vs YOLO11DocsGitHubLicense

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Frequently asked questions

How do I download YOLOv8 weights?
Click the Download button next to any variant in the table above; we verify the file and start it straight from the official CDN. You can also let the loader fetch it automatically on first use with `model = YOLO("yolov8n.pt")`. All 5 YOLOv8 checkpoints are hosted officially.
Is YOLOv8 free for commercial use?
YOLOv8 is released under AGPL-3.0. That is free for open-source and research; closed-source commercial deployments need an Ultralytics Enterprise license. Always confirm against the linked license text.
Which YOLOv8 model size should I use?
Start with YOLOv8n, the smallest and fastest, ideal for prototyping, edge and CPU. Move up the ladder only when you need more accuracy and have the compute for it. The largest variant reaches 53.9 COCO mAP.