Model weights · YOLO11

Download YOLO11 weights

An anchor-free, single-pass detector that predicts objects in one shot across five vision tasks, giving a strong balance of speed and accuracy for everyday production use. 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 · 2024 · Ultralytics (PyTorch)

YOLO11 checkpoints, pick a size and download

An anchor-free, single-pass detector that predicts objects in one shot across five vision tasks, giving a strong balance of speed and accuracy for everyday production use.

  • YOLO11n
    yolo11n.pt
    COCO mAP
    39.5
    Params
    2.6M
  • YOLO11s
    yolo11s.pt
    COCO mAP
    47.0
    Params
    9.4M
  • YOLO11m
    yolo11m.pt
    COCO mAP
    51.5
    Params
    20.1M
  • YOLO11l
    yolo11l.pt
    COCO mAP
    53.4
    Params
    25.3M
  • YOLO11x
    yolo11x.pt
    COCO mAP
    54.7
    Params
    56.9M

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 YOLO11 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("yolo11n.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 YOLO11 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

YOLO11 spans YOLO11n at 2.6M parameters to YOLO11x at 56.9M, 21.9× the model, for 15.2 more COCO mAP (39.5 to 54.7). That works out to roughly 0.28 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 YOLO11n → YOLO11s, worth 1.10 COCO mAP points per million parameters. The worst is YOLO11l → YOLO11x at 0.04, or 27× less efficient, for 31.6M extra parameters and only 1.3 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, YOLO11's best checkpoint ranks 5th at 54.7, 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, YOLO12 and YOLOv10.

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 YOLO11

YOLO11 is the mainstream Ultralytics model: anchor-free, well-documented, and supporting all five vision tasks. Its C3k2 blocks and C2PSA attention let YOLO11m match YOLOv8m accuracy with about 22% fewer parameters, which makes it the safe, best-supported default for most production work today.

Author
Ultralytics
Released
2024
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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Each page lists every checkpoint with accuracy, parameters, license and a one-click download.

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Frequently asked questions

How do I download YOLO11 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("yolo11n.pt")`. All 5 YOLO11 checkpoints are hosted officially.
Is YOLO11 free for commercial use?
YOLO11 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 YOLO11 model size should I use?
Start with YOLO11n, 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 54.7 COCO mAP.