Ultralytics YOLO11 Released 🎉

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Just tested the Ultralytics YOLO11 models, performing well! :star_struck::rocket:

I experimented with various tasks like detection, segmentation, prediction, and pose estimation using random images, and the results look fantastic! :smiley:

In case you want to give it a try yolo predict source='path/to/image.png

Learn more :arrow_right: YOLO11 - Ultralytics YOLO Docs

Object Detection Pose Estimation Object Detection Image Segmentation
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More details here!

:hammer_and_wrench: R&D Highlights

  • 25 Open-Source Models: YOLO11 introduces 25 models across 5 sizes and 5 tasks, ensuring there’s an optimized model for any use case.
  • Accuracy Boost: YOLO11n achieves up to a 2.2% higher mAP (37.3 → 39.5) on COCO object detection tasks compared to YOLOv8n.
  • Efficiency & Speed: YOLO11 uses up to 22% fewer parameters than YOLOv8 and provides up to 2% faster inference speeds. Optimized for edge applications and resource-constrained environments.

The focus of YOLO11 is on refining architecture to improve performance while reducing computational requirements—a great fit for those who need both precision and speed.

:bar_chart: YOLO11 Benchmarks

The improvements are consistent across all model sizes, providing a noticeable upgrade for current YOLO users.

Model YOLOv8 mAP (%) YOLO11 mAP (%) YOLOv8 Params (M) YOLO11 Params (M) Improvement
YOLOn 37.3 39.5 3.2 2.6 +2.2% mAP
YOLOs 44.9 47.0 11.2 9.4 +2.1% mAP
YOLOm 50.2 51.5 25.9 20.1 +1.3% mAP
YOLOl 52.9 53.4 43.7 25.3 +0.5% mAP
YOLOx 53.9 54.7 68.2 56.9 +0.8% mAP

:bulb: Versatile Task Support

YOLO11 extends the capabilities of the YOLO series to cover multiple computer vision tasks:

:wrench: Quick Start Example

If you’re already using the Ultralytics package, upgrading to YOLO11 is easy. Install the latest package:

pip install ultralytics>=8.3.0

Then, load a pre-trained YOLO11 model and run inference on an image:

from ultralytics import YOLO

# Load the YOLO11 model
model = YOLO("yolo11n.pt")

# Run inference on an image
results = model("path/to/image.jpg")

# Display results
results[0].show()

These few lines of code are all you need to start using YOLO11 for your real-time computer vision needs.

:package: Access and Get Involved

YOLO11 is open-source and designed to integrate smoothly into various workflows, from edge devices to cloud platforms. You can explore the models and contribute at GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite.

Check it out, see how it fits into your projects, and let us know your feedback!

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